@article{astropy1,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A},
Archiveprefix = {arXiv},
Author = {{Astropy Collaboration} and {Robitaille}, T.~P. and {Tollerud}, E.~J. and {Greenfield}, P. and {Droettboom}, M. and {Bray}, E. and {Aldcroft}, T. and {Davis}, M. and {Ginsburg}, A. and {Price-Whelan}, A.~M. and {Kerzendorf}, W.~E. and {Conley}, A. and {Crighton}, N. and {Barbary}, K. and {Muna}, D. and {Ferguson}, H. and {Grollier}, F. and {Parikh}, M.~M. and {Nair}, P.~H. and {Unther}, H.~M. and {Deil}, C. and {Woillez}, J. and {Conseil}, S. and {Kramer}, R. and {Turner}, J.~E.~H. and {Singer}, L. and {Fox}, R. and {Weaver}, B.~A. and {Zabalza}, V. and {Edwards}, Z.~I. and {Azalee Bostroem}, K. and {Burke}, D.~J. and {Casey}, A.~R. and {Crawford}, S.~M. and {Dencheva}, N. and {Ely}, J. and {Jenness}, T. and {Labrie}, K. and {Lim}, P.~L. and {Pierfederici}, F. and {Pontzen}, A. and {Ptak}, A. and {Refsdal}, B. and {Servillat}, M. and {Streicher}, O.},
Doi = {10.1051/0004-6361/201322068},
Eid = {A33},
Eprint = {1307.6212},
Journal = {\aap},
Keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools},
Month = oct,
Pages = {A33},
Primaryclass = {astro-ph.IM},
Title = {{Astropy: A community Python package for astronomy}},
Volume = 558,
Year = 2013,
Bdsk-Url-1 = {https://dx.doi.org/10.1051/0004-6361/201322068}}
@article{astropy2,
Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
Adsurl = {https://ui.adsabs.harvard.edu/#abs/2018AJ....156..123T},
Author = {{Price-Whelan}, A.~M. and {Sip{\H{o}}cz}, B.~M. and {G{\"u}nther}, H.~M. and {Lim}, P.~L. and {Crawford}, S.~M. and {Conseil}, S. and {Shupe}, D.~L. and {Craig}, M.~W. and {Dencheva}, N. and {Ginsburg}, A. and {VanderPlas}, J.~T. and {Bradley}, L.~D. and {P{\'e}rez-Su{\'a}rez}, D. and {de Val-Borro}, M. and {Paper Contributors}, (Primary and {Aldcroft}, T.~L. and {Cruz}, K.~L. and {Robitaille}, T.~P. and {Tollerud}, E.~J. and {Coordination Committee}, (Astropy and {Ardelean}, C. and {Babej}, T. and {Bach}, Y.~P. and {Bachetti}, M. and {Bakanov}, A.~V. and {Bamford}, S.~P. and {Barentsen}, G. and {Barmby}, P. and {Baumbach}, A. and {Berry}, K.~L. and {Biscani}, F. and {Boquien}, M. and {Bostroem}, K.~A. and {Bouma}, L.~G. and {Brammer}, G.~B. and {Bray}, E.~M. and {Breytenbach}, H. and {Buddelmeijer}, H. and {Burke}, D.~J. and {Calderone}, G. and {Cano Rodr{\'\i}guez}, J.~L. and {Cara}, M. and {Cardoso}, J.~V.~M. and {Cheedella}, S. and {Copin}, Y. and {Corrales}, L. and {Crichton}, D. and {D{\textquoteright}Avella}, D. and {Deil}, C. and {Depagne}, {\'E}. and {Dietrich}, J.~P. and {Donath}, A. and {Droettboom}, M. and {Earl}, N. and {Erben}, T. and {Fabbro}, S. and {Ferreira}, L.~A. and {Finethy}, T. and {Fox}, R.~T. and {Garrison}, L.~H. and {Gibbons}, S.~L.~J. and {Goldstein}, D.~A. and {Gommers}, R. and {Greco}, J.~P. and {Greenfield}, P. and {Groener}, A.~M. and {Grollier}, F. and {Hagen}, A. and {Hirst}, P. and {Homeier}, D. and {Horton}, A.~J. and {Hosseinzadeh}, G. and {Hu}, L. and {Hunkeler}, J.~S. and {Ivezi{\'c}}, {\v{Z}}. and {Jain}, A. and {Jenness}, T. and {Kanarek}, G. and {Kendrew}, S. and {Kern}, N.~S. and {Kerzendorf}, W.~E. and {Khvalko}, A. and {King}, J. and {Kirkby}, D. and {Kulkarni}, A.~M. and {Kumar}, A. and {Lee}, A. and {Lenz}, D. and {Littlefair}, S.~P. and {Ma}, Z. and {Macleod}, D.~M. and {Mastropietro}, M. and {McCully}, C. and {Montagnac}, S. and {Morris}, B.~M. and {Mueller}, M. and {Mumford}, S.~J. and {Muna}, D. and {Murphy}, N.~A. and {Nelson}, S. and {Nguyen}, G.~H. and {Ninan}, J.~P. and {N{\"o}the}, M. and {Ogaz}, S. and {Oh}, S. and {Parejko}, J.~K. and {Parley}, N. and {Pascual}, S. and {Patil}, R. and {Patil}, A.~A. and {Plunkett}, A.~L. and {Prochaska}, J.~X. and {Rastogi}, T. and {Reddy Janga}, V. and {Sabater}, J. and {Sakurikar}, P. and {Seifert}, M. and {Sherbert}, L.~E. and {Sherwood-Taylor}, H. and {Shih}, A.~Y. and {Sick}, J. and {Silbiger}, M.~T. and {Singanamalla}, S. and {Singer}, L.~P. and {Sladen}, P.~H. and {Sooley}, K.~A. and {Sornarajah}, S. and {Streicher}, O. and {Teuben}, P. and {Thomas}, S.~W. and {Tremblay}, G.~R. and {Turner}, J.~E.~H. and {Terr{\'o}n}, V. and {van Kerkwijk}, M.~H. and {de la Vega}, A. and {Watkins}, L.~L. and {Weaver}, B.~A. and {Whitmore}, J.~B. and {Woillez}, J. and {Zabalza}, V. and {Contributors}, (Astropy},
Doi = {10.3847/1538-3881/aabc4f},
Eid = {123},
Journal = {\aj},
Keywords = {methods: data analysis, methods: miscellaneous, methods: statistical, reference systems, Astrophysics - Instrumentation and Methods for Astrophysics},
Month = Sep,
Pages = {123},
Primaryclass = {astro-ph.IM},
Title = {{The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package}},
Volume = {156},
Year = 2018,
Bdsk-Url-1 = {https://doi.org/10.3847/1538-3881/aabc4f}}
@article{PyLops,
abstract = {Linear operators and optimisation are at the core of many algorithms used in signal and image processing, remote sensing, and inverse problems. For small to medium-scale problems, existing software packages (e.g., MATLAB, Python numpy and scipy) allow for explicitly building dense (or sparse) matrices and performing algebraic operations (e.g., computation of matrix-vector products and manipulation of matrices) with syntax that closely represents their corresponding analytical forms. However, many real application, large-scale operators do not lend themselves to explicit matrix representations, usually forcing practitioners to forego of the convenient linear-algebra syntax available for their explicit-matrix counterparts. PyLops is an open-source Python library providing a flexible and scalable framework for the creation and combination of so-called linear operators, class-based entities that represent matrices and inherit their associated syntax convenience, but do not rely on the creation of explicit matrices. We show that PyLops operators can dramatically reduce the memory load and CPU computations compared to explicit-matrix calculations, while still allowing users to seamlessly use their existing knowledge of compact matrix-based syntax that scales to any problem size because no explicit matrices are required.},
archivePrefix = {arXiv},
arxivId = {1907.12349},
author = {Ravasi, Matteo and Vasconcelos, Ivan},
doi = {10.1016/j.softx.2019.100361},
eprint = {1907.12349},
file = {:home/jammy/Documents/Papers/Software/PyLops.pdf:pdf},
title = {{PyLops -- A Linear-Operator Python Library for large scale optimization}},
url = {http://arxiv.org/abs/1907.12349},
year = {2019}
}
@article{colossus,
abstract = {This paper introduces Colossus, a public, open-source python package for calculations related to cosmology, the large-scale structure (LSS) of matter in the universe, and the properties of dark matter halos. The code is designed to be fast and easy to use, with a coherent, well-documented user interface. The cosmology module implements Friedman-Lemaitre-Robertson-Walker cosmologies including curvature, relativistic species, and different dark energy equations of state, and provides fast computations of the linear matter power spectrum, variance, and correlation function. The LSS module is concerned with the properties of peaks in Gaussian random fields and halos in a statistical sense, including their peak height, peak curvature, halo bias, and mass function. The halo module deals with spherical overdensity radii and masses, density profiles, concentration, and the splashback radius. To facilitate the rapid exploration of these quantities, Colossus implements more than 40 different fitting functions from the literature. I discuss the core routines in detail, with particular emphasis on their accuracy. Colossus is available at bitbucket.org/bdiemer/colossus.},
archivePrefix = {arXiv},
arxivId = {1712.04512},
author = {Diemer, Benedikt},
doi = {10.3847/1538-4365/aaee8c},
eprint = {1712.04512},
file = {:home/jammy/Documents/Papers/Software/Collosus2018.pdf:pdf},
issn = {0067-0049},
journal = {The Astrophysical Journal Supplement Series},
keywords = {cosmology,cosmology: theory,methods: numerical,methods,numerical,theory},
number = {2},
pages = {35},
publisher = {IOP Publishing},
title = {{COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos}},
url = {http://dx.doi.org/10.3847/1538-4365/aaee8c},
volume = {239},
year = {2018}
}
@article{corner,
  doi = {10.21105/joss.00024},
  url = {https://doi.org/10.21105/joss.00024},
  year  = {2016},
  month = {jun},
  publisher = {The Open Journal},
  volume = {1},
  number = {2},
  pages = {24},
  author = {Daniel Foreman-Mackey},
  title = {corner.py: Scatterplot matrices in Python},
  journal = {The Journal of Open Source Software}
}
@article{dynesty,
abstract = {We present dynesty, a public, open-source, python package to estimate Bayesian posteriors and evidences (marginal likelihoods) using the dynamic nested sampling methods developed by Higson et al. By adaptively allocating samples based on posterior structure, dynamic nested sampling has the benefits of Markov chain Monte Carlo (MCMC) algorithms that focus exclusively on posterior estimation while retaining nested sampling's ability to estimate evidences and sample from complex, multimodal distributions. We provide an overview of nested sampling, its extension to dynamic nested sampling, the algorithmic challenges involved, and the various approaches taken to solve them in this and previous work. We then examine dynesty's performance on a variety of toy problems along with several astronomical applications. We find in particular problems dynesty can provide substantial improvements in sampling efficiency compared to popular MCMC approaches in the astronomical literature. More detailed statistical results related to nested sampling are also included in the appendix.},
archivePrefix = {arXiv},
arxivId = {1904.02180},
author = {Speagle, Joshua S},
doi = {10.1093/mnras/staa278},
eprint = {1904.02180},
file = {:home/jammy/Documents/Papers/PPLs/Dynesty.pdf:pdf},
issn = {0035-8711},
journal = {MNRAS},
keywords = {data analysis,methods,statistical},
number = {3},
pages = {3132--3158},
title = {{dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences}},
volume = {493},
year = {2020}
}
@article{emcee,
abstract = {We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman {\&} Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to {\$}\backslashsim N{\^{}}2{\$} for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the MIT License.},
archivePrefix = {arXiv},
arxivId = {1202.3665},
author = {Foreman-Mackey, Daniel and Hogg, David W. and Lang, Dustin and Goodman, Jonathan},
doi = {10.1086/670067},
eprint = {1202.3665},
file = {:home/jammy/Documents/Papers/PPLs/Emcee.pdf:pdf},
issn = {00046280},
journal = {Publications of the Astronomical Society of the Pacific},
number = {925},
pages = {306--312},
title = {{emcee : The MCMC Hammer}},
volume = {125},
year = {2013}
}
@article{matplotlib,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}
@article{numba,
abstract = {Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is of-ten a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addi-tion, we share our experience in building a JIT compiler using LLVM[1].},
author = {Lam, Siu Kwan and Pitrou, Antoine and Seibert, Stanley},
doi = {10.1145/2833157.2833162},
file = {:home/jammy/Documents/Papers/Software/numba{\_}sc15.pdf:pdf},
isbn = {9781450340052},
journal = {Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC - LLVM '15},
keywords = {2,a jit for numeric,com-,compiler,jit,just-in-time,llvm,numba is a function-at-a-time,python},
pages = {1--6},
title = {{Numba: a LLVM-based Python JIT compiler}},
url = {http://dl.acm.org/citation.cfm?doid=2833157.2833162},
year = {2015}
}
@article{numpy,
  author={S. {van der Walt} and S. C. {Colbert} and G. {Varoquaux}},
  journal={Computing in Science   Engineering},
  title={The NumPy Array2D: A Structure for Efficient Numerical Computation},
  year={2011},
  volume={13},
  number={2},
  pages={22-30},
  doi={10.1109/MCSE.2011.37}}

@article{pyautofit,
  doi = {10.21105/joss.02550},
  url = {https://doi.org/10.21105/joss.02550},
  year = {2021},
  publisher = {The Open Journal},
  volume = {6},
  number = {58},
  pages = {2550},
  author = {Nightingale, J. W. and Hayes, R. G. and Griffiths, M.},
  title = {`PyAutoFit`: A Classy Probabilistic Programming Language for Model Composition and Fitting},
  journal = {J. Open Source Softw.}
}
@article{multinest,
abstract = {We present further development and the first public release of our multimodal nested sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence, with an associated error estimate, and produces posterior samples from distributions that may contain multiple modes and pronounced (curving) degeneracies in high dimensions. The developments presented here lead to further substantial improvements in sampling efficiency and robustness, as compared to the original algorithm presented in Feroz and Hobson, which itself significantly outperformed existing Markov chain Monte Carlo techniques in a wide range of astrophysical inference problems. The accuracy and economy of the MultiNest algorithm are demonstrated by application to two toy problems and to a cosmological inference problem focusing on the extension of the vanilla $\Lambda$ cold dark matter model to include spatial curvature and a varying equation of state for dark energy. The MultiNest software, which is fully parallelized using MPI and includes an interface to CosmoMC, is available at http://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part of the SuperBayeS package, for the analysis of supersymmetric theories of particle physics, at http://www.superbayes.org. {\textcopyright} 2009 RAS.},
archivePrefix = {arXiv},
arxivId = {0809.3437},
author = {Feroz, F. and Hobson, M. P. and Bridges, M.},
doi = {10.1111/j.1365-2966.2009.14548.x},
eprint = {0809.3437},
isbn = {0035-8711},
issn = {00358711},
journal = {MNRAS},
keywords = {Methods: Data analysis,Methods: Statistical},
number = {4},
pages = {1601--1614},
pmid = {29176},
title = {{MultiNest: An efficient and robust Bayesian inference tool for cosmology and particle physics}},
volume = {398},
year = {2009}
}
@article{pymultinest,
abstract = {Context. Aims. Active galactic nuclei are known to have complex X-ray spectra that depend on both the properties of the accreting super-massive black hole (e.g. mass, accretion rate) and the distribution of obscuring material in its vicinity (i.e. the "torus"). Often however, simple and even unphysical models are adopted to represent the X-ray spectra of AGN, which do not capture the complexity and diversity of the observations. In the case of blank field surveys in particular, this should have an impact on e.g. the determination of the AGN luminosity function, the inferred accretion history of the Universe and also on our understanding of the relation between AGN and their host galaxies. Methods. We develop a Bayesian framework for model comparison and parameter estimation of X-ray spectra. We take into account uncertainties associated with both the Poisson nature of X-ray data and the determination of source redshift using photometric methods. We also demonstrate how Bayesian model comparison can be used to select among ten different physically motivated X-ray spectral models the one that provides a better representation of the observations. This methodology is applied to X-ray AGN in the 4 Ms Chandra Deep Field South. Results. For the {\~{}}350 AGN in that field, our analysis identifies four components needed to represent the diversity of the observed X-ray spectra: (1) an intrinsic power law; (2) a cold obscurer which reprocesses the radiation due to photo-electric absorption, Compton scattering and Fe-K fluorescence; (3) an unabsorbed power law associated with Thomson scattering off ionised clouds; and (4) Compton reflection, most noticeable from a stronger-than-expected Fe-K line. Simpler models, such as a photo-electrically absorbed power law with a Thomson scattering component, are ruled out with decisive evidence (B {\textgreater} 100). We also find that ignoring the Thomson scattering component results in underestimation of the inferred column density, NH, of the obscurer. Regarding the geometry of the obscurer, there is strong evidence against both a completely closed (e.g. sphere), or entirely open (e.g. blob of material along the line of sight), toroidal geometry in favour of an intermediate case. Conclusions. Despite the use of low-count spectra, our methodology is able to draw strong inferences on the geometry of the torus. Simpler models are ruled out in favour of a geometrically extended structure with significant Compton scattering. We confirm the presence of a soft component, possibly associated with Thomson scattering off ionised clouds in the opening angle of the torus. The additional Compton reflection required by data over that predicted by toroidal geometry models, may be a sign of a density gradient in the torus or reflection off the accretion disk. Finally, we release a catalogue of AGN in the CDFS with estimated parameters such as the accretion luminosity in the 2-10 keV band and the column density, NH, of the obscurer. {\textcopyright} ESO, 2014.},
archivePrefix = {arXiv},
arxivId = {1402.0004},
author = {Buchner, J. and Georgakakis, A. and Nandra, K. and Hsu, L. and Rangel, C. and Brightman, M. and Merloni, A. and Salvato, M. and Donley, J. and Kocevski, D.},
doi = {10.1051/0004-6361/201322971},
eprint = {1402.0004},
file = {:home/jammy/Documents/Papers/Stats/ButchnerPyMultiNest.pdf:pdf},
issn = {14320746},
journal = {A&A},
keywords = {Accretion, accretion disks,galaxies: high-redshift,galaxies: nuclei,Methods: data analysis,Methods: statistical,X-rays: galaxies},
pages = {A125},
title = {{X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue}},
volume = {564},
year = {2014}
}
@article{pynufft,
abstract = {A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated environment, the time efficiency of the NUFFT algorithm has been a major obstacle to real-time non-Cartesian image reconstruction with Python. The current PyNUFFT software enables multi-dimensional NUFFT accelerated on a heterogeneous platform, which yields an efficient solution to many non-Cartesian imaging problems. The PyNUFFT also provides several solvers, including the conjugate gradient method, 1 total variation regularized ordinary least square (L1TV-OLS), and 1 total variation regularized least absolute deviation (L1TV-LAD). Metaprogramming libraries have been employed to accelerate PyNUFFT. The PyNUFFT package has been tested on multi-core central processing units (CPUs) and graphic processing units (GPUs), with acceleration factors of 6.3–9.5× on a 32-thread CPU platform and 5.4–13× on a GPU.},
author = {Lin, Jyh Miin},
doi = {10.3390/jimaging4030051},
file = {:home/jammy/Documents/Papers/Software/jimaging-04-00051-v2.pdf:pdf},
issn = {2313433X},
journal = {Journal of Imaging},
keywords = {Graphic processing unit (GPU),Heterogeneous system architecture (HSA),Magnetic resonance imaging (MRI),Multi-core system,Total variation (TV)},
number = {3},
pages = {1--22},
title = {{Python non-uniform fast fourier transform (PyNUFFT): An accelerated non-cartesian MRI package on a heterogeneous platform (CPU/GPU)}},
volume = {4},
year = {2018}
}
@software{pyquad,
  author       = {Ashley J. Kelly},
  title        = {pyquad},
  month        = jul,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.6.4},
  doi          = {10.5281/zenodo.3936959},
  url          = {https://doi.org/10.5281/zenodo.3936959}
}
@article{pyswarms,
    author  = {Lester James V. Miranda},
    title   = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython",
    journal = {J. Open Source Softw.},
    year    = {2018},
    volume  = {3},
    issue   = {21},
    doi     = {10.21105/joss.00433},
    url     = {https://doi.org/10.21105/joss.00433}
}
 @book{python,
 author = {Van Rossum, Guido and Drake, Fred L.},
 title = {Python 3 Reference Manual},
 year = {2009},
 isbn = {1441412697},
 publisher = {CreateSpace},
 address = {Scotts Valley, CA}
}
@article{scikit-image,
  title={scikit-image: image processing in Python},
  author={Van der Walt, Stefan and Sch{\"o}nberger, Johannes L and Nunez-Iglesias, Juan and Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony},
  journal={PeerJ},
  volume={2},
  pages={e453},
  year={2014},
  publisher={PeerJ Inc.}
}
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
@article{scipy,
       author = {{Virtanen}, Pauli and {Gommers}, Ralf and {Oliphant},
         Travis E. and {Haberland}, Matt and {Reddy}, Tyler and
         {Cournapeau}, David and {Burovski}, Evgeni and {Peterson}, Pearu
         and {Weckesser}, Warren and {Bright}, Jonathan and {van der Walt},
         St{\'e}fan J.  and {Brett}, Matthew and {Wilson}, Joshua and
         {Jarrod Millman}, K.  and {Mayorov}, Nikolay and {Nelson}, Andrew
         R.~J. and {Jones}, Eric and {Kern}, Robert and {Larson}, Eric and
         {Carey}, CJ and {Polat}, {\.I}lhan and {Feng}, Yu and {Moore},
         Eric W. and {Vand erPlas}, Jake and {Laxalde}, Denis and
         {Perktold}, Josef and {Cimrman}, Robert and {Henriksen}, Ian and
         {Quintero}, E.~A. and {Harris}, Charles R and {Archibald}, Anne M.
         and {Ribeiro}, Ant{\^o}nio H. and {Pedregosa}, Fabian and
         {van Mulbregt}, Paul and {Contributors}, SciPy 1. 0},
        title = "{SciPy 1.0: Fundamental Algorithms for Scientific
                  Computing in Python}",
      journal = {Nature Methods},
      year = "2020",
      volume={17},
      pages={261--272},
      adsurl = {https://rdcu.be/b08Wh},
      doi = {10.1038/s41592-019-0686-2},
}
@article{Alexander2019,
abstract = {Dark matter substructure has the potential to discriminate between broad classes of dark matter models. With this in mind, we construct novel solutions to the equations of motion governing condensate dark matter candidates, namely axion Bose-Einstein condensates and superfluids. These solutions are highly compressed along one axis and thus have a disk-like geometry. We discuss linear stability of these solutions, consider the astrophysical implications as a large-scale dark disk or as small scale substructure, and find a characteristic signal in strong gravitational lensing. This adds to the growing body of work that indicates that the morphology of dark matter substructure is a powerful probe of the nature of dark matter.},
archivePrefix = {arXiv},
arxivId = {1901.03694},
author = {Alexander, Stephon and Bramburger, Jason J. and McDonough, Evan},
doi = {10.1016/j.physletb.2019.134871},
eprint = {1901.03694},
file = {:home/jammy/Documents/Papers/PyAutoLens/Alexander2018DiskSuperfliod.pdf:pdf},
issn = {03702693},
journal = {Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics},
pages = {1--7},
title = {{Dark disk substructure and superfluid dark matter}},
url = {http://arxiv.org/abs/1901.03694},
volume = {797},
year = {2019}
}
@article{Bolton2012,
abstract = {We present an analysis of the evolution of the central mass-density profile of massive elliptical galaxies from the SLACS and BELLS strong gravitational lens samples over the redshift interval z ≈ 0.1-0.6, based on the combination of strong-lensing aperture mass and stellar velocity-dispersion constraints. We find a significant trend toward steeper mass profiles (parameterized by the power-law density model with $\rho$ ∝ r-$\gamma$) at later cosmic times, with magnitude d 〈$\gamma$〉/dz = -0.60 ± 0.15. We show that the combined lens-galaxy sample is consistent with a non-evolving distribution of stellar velocity dispersions. Considering possible additional dependence of 〈$\gamma$〉 on lens-galaxy stellar mass, effective radius, and S{\'{e}}rsic index, we find marginal evidence for shallower mass profiles at higher masses and larger sizes, but with a significance that is subdominant to the redshift dependence. Using the results of published Monte Carlo simulations of spectroscopic lens surveys, we verify that our mass-profile evolution result cannot be explained by lensing selection biases as a function of redshift. Interpreted as a true evolutionary signal, our result suggests that major dry mergers involving off-axis trajectories play a significant role in the evolution of the average mass-density structure of massive early-type galaxies over the past 6Gyr. We also consider an alternative non-evolutionary hypothesis based on variations in the strong-lensing measurement aperture with redshift, which would imply the detection of an "inflection zone" marking the transition between the baryon-dominated and dark-matter halo-dominated regions of the lens galaxies. Further observations of the combined SLACS+BELLS sample can constrain this picture more precisely, and enable a more detailed investigation of the multivariate dependences of galaxy mass structure across cosmic time. {\textcopyright} 2012. The American Astronomical Society. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {1201.2988},
author = {Bolton, Adam S. and Brownstein, Joel R. and Kochanek, Christopher S. and Shu, Yiping and Schlegel, David J. and Eisenstein, Daniel J. and Wake, David A. and Connolly, Natalia and Maraston, Claudia and Arneson, Ryan A. and Weaver, Benjamin A.},
doi = {10.1088/0004-637X/757/1/82},
eprint = {1201.2988},
issn = {15384357},
journal = {Astrophysical Journal},
keywords = {galaxies: elliptical and lenticular, cD,galaxies: evolution,galaxies: structure,Gravitational lensing: strong},
number = {1},
pages = {82},
title = {{The BOSS emission-line lens survey. II. Investigating mass-density profile evolution in the SLACS+BELLS strong gravitational lens sample}},
url = {http://arxiv.org/abs/1201.2988},
volume = {757},
year = {2012}
}
@article{Collett2015,
abstract = {Ongoing and future imaging surveys represent significant improvements in depth, area, and seeing compared to current data sets. These improvements offer the opportunity to discover up to three orders of magnitude more galaxy-galaxy strong lenses than are currently known. In this work we forecast the number of lenses that will be discoverable in forthcoming surveys and simulate their properties. We generate a population of statistically realistic strong lenses and simulate observations of this population for the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), and Euclid surveys. We verify our model against the galaxy-scale lens search of the Canada-France-Hawaii Telescope Legacy Survey, predicting 250 discoverable lenses compared to 220 found by Gavazzi et al. The predicted Einstein radius distribution is also remarkably similar to that found by Sonnenfeld et al. For future surveys we find that, assuming Poisson limited lens galaxy subtraction, searches of the DES, LSST, and Euclid data sets should discover 2400, 120000, and 170000 galaxy-galaxy strong lenses, respectively. Finders using blue-minus-red (g - i) difference imaging for lens subtraction can discover 1300 and 62000 lenses in DES and LSST. The uncertainties on the model are dominated by the high-redshift source population, which typically gives fractional errors on the discoverable lens number at the level of tens of percent. We find that doubling the signal-to-noise ratio required for a lens to be detectable approximately halves the number of detectable lenses in each survey, indicating the importance of understanding the selection function and the sensitivity of future lens finders in interpreting strong lens statistics. We make our population forecasting and simulated observation codes publicly available so that the selection function of strong lens finders can easily be calibrated.},
archivePrefix = {arXiv},
arxivId = {1507.02657},
author = {Collett, Thomas E.},
doi = {10.1088/0004-637X/811/1/20},
eprint = {1507.02657},
isbn = {0004-637x},
issn = {15384357},
journal = {Astrophysical Journal},
keywords = {gravitational lensing: strong},
number = {1},
pages = {20},
title = {{The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys}},
url = {http://arxiv.org/abs/1507.02657},
volume = {811},
year = {2015}
}
@article{Czoske2012,
abstract = {This paper presents the full Very Large Telescope (VLT)/VIMOS-IFU data set and related data products from an ESO Large Programme with the observational goal of obtaining two-dimensional kinematic data of early-type lens galaxies, out to one effective radius. The sample consists of 17 early-type galaxies (ETGs) selected from the SLACS gravitational-lens survey. The galaxies cover the redshift range from 0.08 to 0.35 and have stellar velocity dispersions between 200 and 350 kms-1. This programme is complemented by a similar observational programme on Keck, using long-slit spectroscopy. In combination with multi-band imaging data, the kinematic data provide stringent constraints on the inner mass profiles of ETGs beyond the local Universe. Our Large Programme thus extends studies of nearby ETGs (e.g. SAURON/ATLAS3D) by an order of magnitude in distance and towards higher masses. We provide an overview of our observational strategy, the data products (luminosity-weighted spectra andHubble Space Telescopeimages) and derived products (i.e. two-dimensional fields of velocity dispersions and streaming motions) that have been used in a number of published and forthcoming lensing, kinematic and stellar-population studies. These studies also pave the way for future studies of ETGs atz≈ 1 with the upcoming extremely large telescopes. {\textcopyright} 2011 The Authors MNRAS {\textcopyright} 2011 RAS.},
archivePrefix = {arXiv},
arxivId = {1108.0577},
author = {Czoske, Oliver and Barnab{\`{e}}, Matteo and Koopmans, L{\'{e}}on V.E. and Treu, Tommaso and Bolton, Adam S.},
doi = {10.1111/j.1365-2966.2011.19726.x},
eprint = {1108.0577},
isbn = {00358711},
issn = {00358711},
journal = {MNRAS},
keywords = {galaxies: elliptical and lenticular, cD,galaxies: kinematics and dynamics,galaxies: structure,Gravitational lensing: strong,Techniques: spectroscopic},
number = {1},
pages = {656--668},
title = {{Two-dimensional kinematics of SLACS lenses - IV. The complete VLT-VIMOS data set}},
volume = {419},
year = {2012}
}
@article{Dye2014,
abstract = {We have determined the mass density radial profiles of the first five strong gravitational lens systems discovered by the Herschel Astrophysical Terahertz Large Area Survey. We present an enhancement of the semilinear lens inversion method of Warren {\&} Dye which allows simultaneous reconstruction of several different wavebands and apply this to dual-band imaging of the lenses acquired with the Hubble Space Telescope. The five systems analysed here have lens redshifts which span a range 0.22 ≤ z ≤ 0.94. Our findings are consistent with other studies by concluding that: (1) the logarithmic slope of the total mass density profile steepens with decreasing redshift; (2) the slope is positively correlated with the average total projected mass density of the lens contained within half the effective radius and negatively correlated with the effective radius; (3) the fraction of dark matter contained within half the effective radius increases with increasing effective radius and increases with redshift. {\textcopyright} 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.},
archivePrefix = {arXiv},
arxivId = {1311.5893},
author = {Dye, S. and Negrello, M. and Hopwood, R. and Nightingale, J. W. and Bussmann, R. S. and Amber, S. and Bourne, N. and Cooray, A. and Dariush, A. and Dunne, L. and Eales, S. A. and Gonzalez-Nuevo, J. and Ibar, E. and Ivison, R. J. and Maddox, S. and Valiante, E. and Smith, M.},
doi = {10.1093/mnras/stu305},
eprint = {1311.5893},
issn = {13652966},
journal = {MNRAS},
keywords = {galaxies: evolution,galaxies: structure},
number = {3},
pages = {2013--2025},
title = {{Herschel*-ATLAS: Modelling the first strong gravitational lenses}},
volume = {440},
year = {2014}
}
@article{Enia2018,
abstract = {We perform lens modelling and source reconstruction of Sub-millimetre Array2D (SMA) data for a sample of 12 strongly lensed galaxies selected at 500$\mu$m in the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). A previous analysis of the same data set used a single S{\'{e}}rsic profile to model the light distribution of each background galaxy. Here we model the source brightness distribution with an adaptive pixel scale scheme, extended to work in the Fourier visibility space of interferometry. We also present new SMA observations for seven other candidate lensed galaxies from theH-ATLAS sample. Our derived lens model parameters are in general consistent with previous findings. However, our estimated magnification factors, ranging from 3 to 10, are lower. The discrepancies are observed in particular where the reconstructed source hints at the presence of multiple knots of emission.We define an effective radius of the reconstructed sources based on the area in the source plane where emission is detected above 5s. We also fit the reconstructed source surface brightness with an elliptical Gaussian model. We derive a median value reff {\~{}} 1.77 kpc and a median Gaussian full width at half-maximum {\~{}}1.47 kpc. After correction for magnification, our sources have intrinsic star formation rates (SFR) {\~{}} 900-3500M⊙ yr-1, resulting in a median SFR surface density $\Sigma$SFR {\~{}} 132M⊙ yr-1 kpc-2 (or {\~{}}218M⊙ yr-1 kpc-2 for the Gaussian fit). This is consistent with that observed for other star-forming galaxies at similar redshifts, and is significantly below the Eddington limit for a radiation pressure regulated starburst.},
archivePrefix = {arXiv},
arxivId = {1801.01831},
author = {Enia, A. and Negrello, M. and Gurwell, M. and Dye, S. and Rodighiero, G. and Massardi, M. and {De Zotti}, G. and Franceschini, A. and Cooray, A. and van der Werf, P. and Birkinshaw, M. and Michalowski, M. J. and Oteo, I.},
doi = {10.1093/mnras/sty021},
eprint = {1801.01831},
file = {:home/jammy/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Enia et al. - 2018 - The Herschel-ATLAS Magnifications and physical sizes of 500-$\mu$m-selected strongly lensed galaxies.pdf:pdf},
issn = {13652966},
journal = {MNRAS},
keywords = {galaxies: structure,Gravitational lensing: strong,Instrumentation: interferometers},
number = {3},
pages = {3467--3484},
title = {{The Herschel-ATLAS: Magnifications and physical sizes of 500-$\mu$m-selected strongly lensed galaxies}},
url = {http://arxiv.org/abs/1801.01831},
volume = {475},
year = {2018}
}
@article{Koopmans2009,
abstract = {Based on 58 SLACS strong-lens early-type galaxies (ETGs) with direct total-mass and stellar-velocity dispersion measurements, we find that inside one effective radius massive elliptical galaxies with M eff ≳ 3 × 1010 M⊙ are well approximated by a power-law ellipsoid, with an average logarithmic density slope of 〈$\gamma$′ LD〉 ≡ -dlog($\rho$tot)/dlog(r) = 2.085 +0.025-0.018 (random error on mean) for isotropic orbits with $\beta$r = 0, 0.1 (syst.) and intrinsic scatter (all errors indicate the 68{\%} CL). We find no correlation of $\gamma$′LD with galaxy mass (M eff), rescaled radius (i.e., R einst/R eff) or redshift, despite intrinsic differences in density-slope between galaxies. Based on scaling relations, the average logarithmic density slope can be derived in an alternative manner, fully independent from dynamics, yielding 〈$\gamma$′SR〉 = 1.959 0.077. Agreement between the two values is reached for 〈$\beta$r〉 = 0.45 0.25, consistent with mild radial anisotropy. This agreement supports the robustness of our results, despite the increase in mass-to-light ratio with total galaxy mass: M eff L 1.3630.056V,eff. We conclude that massive ETGs are structurally close to homologous with close to isothermal total density profiles (≲10{\%} intrinsic scatter) and have at most some mild radial anisotropy. Our results provide new observational limits on galaxy formation and evolution scenarios, covering 4 Gyr look-back time. {\textcopyright} 2009. The American Astronomical Society.},
archivePrefix = {arXiv},
arxivId = {0906.1349},
author = {Koopmans, L. V.E. and Bolton, A. and Treu, T. and Czoske, O. and Auger, M. W. and Barnab{\`{e}}, M. and Vegetti, S. and Gavazzi, R. and Moustakas, L. A. and Burles, S.},
doi = {10.1088/0004-637X/703/1/L51},
eprint = {0906.1349},
isbn = {1522-1601 (Electronic)$\backslash$r0161-7567 (Linking)},
issn = {15384357},
journal = {Astrophysical Journal},
keywords = {galaxies: structure,Gravitational lensing},
number = {1 PART 2},
pages = {L51--L54},
pmid = {25414243},
title = {{The structure and dynamics of massive early-type galaxies: On homology, isothermality, and isotropy inside one effective radius}},
url = {http://stacks.iop.org/1538-4357/703/i=1/a=L51?key=crossref.0217bdab14c5f868ab70f18136b0acdd},
volume = {703},
year = {2009}
}
@article{McCully2014,
abstract = {In strong gravitational lens systems, the light bending is usually dominated by one main galaxy, but may be affected by other mass along the line of sight (LOS). Shear and convergence can be used to approximate the contributions from less significant perturbers (e.g. those that are projected far from the lens or have a small mass), but higher order effects need to be included for objects that are closer or more massive. We develop a framework for multiplane lensing that can handle an arbitrary combination of tidal planes treated with shear and convergence and planes treated exactly (i.e. including higher order terms). This framework addresses all of the traditional lensing observables including image positions, fluxes, and time delays to facilitate lens modelling that includes the non-linear effects due to mass along the LOS. It balances accuracy (accounting for higher order terms when necessary) with efficiency (compressing all other LOS effects into a set of matrices that can be calculated up front and cached for lens modelling). We identify a generalized multiplane mass sheet degeneracy, in which the effective shear and convergence are sums over the lensing planes with specific, redshift-dependent weighting factors. {\textcopyright} 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.},
archivePrefix = {arXiv},
arxivId = {1401.0197},
author = {McCully, Curtis and Keeton, Charles R. and Wong, Kenneth C. and Zabludoff, Ann I.},
doi = {10.1093/mnras/stu1316},
eprint = {1401.0197},
issn = {13652966},
journal = {MNRAS},
keywords = {Gravitational lensing: strong,Gravitational lensing: weak},
month = {oct},
number = {4},
pages = {3631--3642},
title = {{A new hybrid framework to efficiently model lines of sight to gravitational lenses}},
volume = {443},
year = {2014}
}
@article{Negrello2014,
abstract = {We report on deep near-infrared observations obtained with the Wide Field Camera-3 (WFC3) onboard the Hubble Space Telescope (HST) of the first five confirmed gravitational lensing events discovered by the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). We succeed in disentangling the background galaxy from the lens to gain separate photometry of the two components. The HST data allow us to significantly improve on previous constraints of the mass in stars of the lensed galaxy and to perform accurate lens modelling of these systems, as described in the accompanying paper by Dye et al. We fit the spectral energy distributions of the background sources from near-IR to millimetre wavelengths and use the magnification factors estimated by Dye et al. to derive the intrinsic properties of the lensed galaxies. We find these galaxies to have star-formations rates (SFR) {\~{}} 400-2000 M⊙ yr-1, with {\~{}}(6-25) × 1010 M⊙ of their baryonic mass already turned into stars. At these rates of star formation, all remaining molecular gas will be exhausted in less than {\~{}}100 Myr, reaching a final mass in stars of a few 1011 M⊙. These galaxies are thus proto-ellipticals caught during their major episode of star formation, and observed at the peak epoch (z {\~{}} 1.5-3) of the cosmic star formation history of the Universe.},
archivePrefix = {arXiv},
arxivId = {1311.5898},
author = {Negrello, M. and Hopwood, R. and Dye, S. and da Cunha, E. and Serjeant, S. and Fritz, J. and Rowlands, K. and Fleuren, S. and Bussmann, R. S. and Cooray, A. and Dannerbauer, H. and Gonzalez-Nuevo, J. and Lapi, A. and Omont, A. and Amber, S. and Auld, R. and Baes, M. and Buttiglione, S. and Cava, A. and Danese, L. and Dariush, A. and {De Zotti}, G. and Dunne, L. and Eales, S. and Ibar, E. and Ivison, R. J. and Kim, S. and Leeuw, L. and Maddox, S. and Michalowski, M. J. and Massardi, M. and Pascale, E. and Pohlen, M. and Rigby, E. and Smith, D. J.B. and Sutherland, W. and Temi, P. and Wardlow, J.},
doi = {10.1093/mnras/stu413},
eprint = {1311.5898},
issn = {13652966},
journal = {MNRAS},
keywords = {galaxies: elliptical and lenticular, cD,galaxies: evolution,galaxies: formation,Gravitational lensing: strong,Infrared: galaxies,Submillimetre: galaxies},
number = {3},
pages = {1999--2012},
title = {{Herschel *-ATLAS: Deep HST/WFC3 imaging of strongly lensed submillimetre galaxies}},
url = {http://arxiv.org/abs/1311.5898},
volume = {440},
year = {2014}
}
@article{Nightingale2015,
abstract = {We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model magnification. The distinguishing feature of adaptive SLI is that every pixelization is derived from a random initialization, ensuring that data discretization is performed in a completely different and unique way for every model parameter set. We compare standard SLI on a fixed source pixel grid with the new method and demonstrate the shortcomings of the former when modelling singular power-law ellipsoid (SPLE) lens profiles. In particular, we demonstrate the superior reliability and efficiency of adaptive SLI which, by design, fixes the number of degrees of freedom (NDOF) of the optimization and thereby removes biases present with other methods that allow the NDOF to vary. In addition, we highlight the importance of data discretization in pixel-based inversion methods, showing that adaptive SLI averages over significant systematics that are present when a fixed source pixel grid is used. In the case of the SPLE lens profile, we show how the method successfully samples its highly degenerate posterior probability distribution function with a single nonlinear search. The robustness of adaptive SLI provides a firm foundation for the development of a strong lens modelling pipeline, which will become necessary in the short-term future to cope with the increasing rate of discovery of new strong lens systems.},
archivePrefix = {arXiv},
arxivId = {1412.7436},
author = {Nightingale, J. W. and Dye, S.},
doi = {10.1093/mnras/stv1455},
eprint = {1412.7436},
issn = {13652966},
journal = {MNRAS},
keywords = {galaxies: evolution,galaxies: structure,Methods: observational},
month = {sep},
number = {3},
pages = {2940--2959},
title = {{Adaptive semi-linear inversion of strong gravitational lens imaging}},
volume = {452},
year = {2015}
}
@article{Nightingale2018,
abstract = {This work presents AutoLens, the first entirely automated modeling suite for the analysis of galaxy-scale strong gravitational lenses. AutoLens simultaneously models the lens galaxy's light and mass whilst reconstructing the extended source galaxy on an adaptive pixel-grid. The method's approach to source-plane discretization is amorphous, adapting its clustering and regularization to the intrinsic properties of the lensed source. The lens's light is fitted using a superposition of Sersic functions, allowing AutoLens to cleanly deblend its light from the source. Single-component mass models representing the lens's total mass density profile are demonstrated, which in conjunction with light modeling can detect central images using a centrally cored profile. Decomposed mass modeling is also shown, which can fully decouple a lens's light and dark matter and determine whether the two components are geometrically aligned. The complexity of the light and mass models is automatically chosen via Bayesian model comparison. These steps form AutoLens's automated analysis pipeline, such that all results in this work are generated without any user intervention. This is rigorously tested on a large suite of simulated images, assessing its performance on a broad range of lens profiles, source morphologies, and lensing geometries. The method's performance is excellent, with accurate light, mass, and source profiles inferred for data sets representative of both existing Hubble imaging and future Euclid wide-field observations.},
archivePrefix = {arXiv},
arxivId = {1708.07377},
author = {Nightingale, J. W. and Dye, S. and Massey, Richard J.},
doi = {10.1093/mnras/sty1264},
eprint = {1708.07377},
file = {:home/jammy/Documents/Papers{\_}Me/AutoLensChangesMarked.pdf:pdf},
issn = {13652966},
journal = {MNRAS},
keywords = {Galaxy: structure,Gravitational lensing,Methods: data analysis},
number = {4},
pages = {4738--4784},
title = {{AutoLens: Automated modeling of a strong lens's light, mass, and source}},
url = {https://academic.oup.com/mnras/article/478/4/4738/5001434},
volume = {478},
year = {2018}
}
@article{Nightingale2019,
abstract = {We investigate how strong gravitational lensing can test contemporary models of massive elliptical (ME) galaxy formation, by combining a traditional decomposition of their visible stellar distribution with a lensing analysis of their mass distribution. As a proof of concept, we study a sample of three ME lenses, observing that all are composed of two distinct baryonic structures, a 'red' central bulge surrounded by an extended envelope of stellar material. Whilst these two components look photometrically similar, their distinct lensing effects permit a clean decomposition of their mass structure. This allows us to infer two key pieces of information about each lens galaxy: (i) the stellar mass distribution (without invoking stellar populations models) and (ii) the inner dark matter halo mass. We argue that these two measurements are crucial to testing models of ME formation, as the stellar mass profile provides a diagnostic of baryonic accretion and feedback whilst the dark matter mass places each galaxy in the context of LCDM large-scale structure formation. We also detect large rotational offsets between the two stellar components and a lopsidedness in their outer mass distributions, which hold further information on the evolution of each ME. Finally, we discuss how this approach can be extended to galaxies of all Hubble types and what implication our results have for studies of strong gravitational lensing.},
archivePrefix = {arXiv},
arxivId = {1901.07801},
author = {Nightingale, J. W. and Massey, Richard J. and Harvey, David R. and Cooper, Andrew P. and Etherington, Amy and Tam, Sut Ieng and Hayes, Richard G.},
doi = {10.1093/mnras/stz2220},
eprint = {1901.07801},
file = {:home/jammy/Documents/Papers{\_}Me/Gal{\_}Structure{\_}Final/GalaxyStructure.pdf:pdf},
issn = {13652966},
journal = {MNRAS},
keywords = {galaxies: Evolution,galaxies: Formation,Gravitational lensing: Strong},
number = {2},
pages = {2049--2068},
title = {{Galaxy structure with strong gravitational lensing: Decomposing the internal mass distribution of massive elliptical galaxies}},
url = {http://arxiv.org/abs/1901.07801},
volume = {489},
year = {2019}
}
@article{Dunlop2013,
abstract = {We use the new ultra-deep, near-infrared imaging of the Hubble Ultra-Deep Field (HUDF) provided by our UDF12 Hubble Space Telescope (HST)Wide Field Camera 3/IR campaign to explore the rest-frame ultraviolet (UV) properties of galaxies at redshifts z ≥ 6.5. We present the first unbiased measurement of the average UV power-law index, ($\beta$), (f$\lambda$ $\alpha$ $\lambda$$\beta$) for faint galaxies at z≃7, the first meaningful measurements of ($\beta$) at z≃8, and tentative estimates for a new sample of galaxies at z≃9. Utilizing galaxy selection in the new F140W (J140) imaging to minimize colour bias, and applying both colour and power-law estimators of $\beta$, we find ($\beta$)= -2.1 ± 0.2 at z ≃7 for galaxies with MUV ≃-18. This means that the faintest galaxies uncovered at this epoch have, on average, UV colours no more extreme than those displayed by the bluest star-forming galaxies at low redshift. At z ≃ 8 we find a similar value, ($\beta$)= -1.9± 0.3. At z ≃ 9, we find ($\beta$)= -1.8± 0.6, essentially unchanged from z ≃ 6 to 7 (albeit highly uncertain). Finally, we show that there is as yet no evidence for a significant intrinsic scatter in $\beta$ within our new, robust z ≃ 7 galaxy sample. Our results are most easily explained by a population of steadily star-forming galaxies with either ≃ solar metallicity and zero dust, or moderately sub-solar (≃10-20 per cent) metallicity with modest dust obscuration (AV ≃ 0.1-0.2). This latter interpretation is consistent with the predictions of a state-of-the-art galaxy-formation simulation, which also suggests that a significant population of very-low metallicity, dust-free galaxies with $\beta$ ≃ -2.5 may not emerge until MUV > -16, a regime likely to remain inaccessible until the James Webb Space Telescope. {\textcopyright} 2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.},
archivePrefix = {arXiv},
arxivId = {1212.0860},
author = {Dunlop, J. S. and Rogers, A. B. and McLure, R. J. and Ellis, R. S. and Robertson, B. E. and Koekemoer, A. and Dayal, P. and Curtis-Lake, E. and Wild, V. and Charlot, S. and Bowler, R. A.A. and Schenker, M. A. and Ouchi, M. and Ono, Y. and Cirasuolo, M. and Furlanetto, S. R. and Stark, D. P. and Targett, T. A. and Schneider, E.},
doi = {10.1093/mnras/stt702},
eprint = {1212.0860},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dunlop et al. - 2013 - The UV continua and inferred stellar populations of galaxies at z≃7-9 revealed by the Hubble Ultra-Deep Field 201.pdf:pdf},
issn = {00358711},
journal = {MNRAS},
keywords = {Evolution-galaxies,First stars,Formation-galaxies,Galaxies,High-redshift-galaxies,Reionization,Stellar content-dark ages},
number = {4},
pages = {3520--3533},
title = {{The UV continua and inferred stellar populations of galaxies at z≃7-9 revealed by the Hubble Ultra-Deep Field 2012 campaign}},
volume = {432},
year = {2013}
}
@article{Bouwens2015,
abstract = {The remarkable Hubble Space Telescope (HST) data sets from the CANDELS, HUDF09, HUDF12, ERS, and BoRG/HIPPIES programs have allowed us to map the evolution of the rest-frame UV luminosity function (LF) from z ∼ 10 to z ∼ 4. We develop new color criteria that more optimally utilize the full wavelength coverage from the optical, near-IR, and mid-IR observations over our search fields, while simultaneously minimizing the incompleteness and eliminating redshift gaps. We have identified 5859, 3001, 857, 481, 217, and 6 galaxy candidates at z ∼ 4, z ∼ 5, z ∼ 6, z ∼ 7, z ∼ 8, and z ∼ 10, respectively, from the ∼1000 arcmin2 area covered by these data sets. This sample of >10,000 galaxy candidates at z ≥ 4 is by far the largest assembled to date with HST. The selection of z ∼ 4-8 candidates over the five CANDELS fields allows us to assess the cosmic variance; the largest variations are at z ≥ 7. Our new LF determinations at z ∼ 4 and z ∼ 5 span a 6 mag baseline and reach to -16 AB mag. These determinations agree well with previous estimates, but the larger samples and volumes probed here result in a more reliable sampling of >L∗ galaxies and allow us to reassess the form of the UV LFs. Our new LF results strengthen our earlier findings to 3.4$\sigma$ significance for a steeper faint-end slope of the UV LF at z ∼ 4, with $\alpha$ evolving from $\alpha$ = -2.06 ∓ 0.13 at to z ∼ 7 (and $\alpha$ = -2.02 ∓ 0.23 at z ∼ 8), consistent with that expected from the evolution of the halo mass function. We find less evolution in the characteristic magnitude M∗ from z ∼ 7 to z ∼ 4; the observed evolution in the LF is now largely represented by changes ∅∗ in . No evidence for a non-Schechter-like form to the z ∼ 4-8 LFs is found. A simple conditional LF model based on halo growth and evolution in the M/L ratio(∝(1+ ∼)-1.5 of halos provides a good representation of the observed evolution.},
archivePrefix = {arXiv},
arxivId = {arXiv:1403.4295v4},
author = {Bouwens, R. J. and Illingworth, G. D. and Oesch, P. A. and Trenti, M. and Labb{\'{e}}, I. and Bradley, L. and Carollo, M. and {Van Dokkum}, P. G. and Gonzalez, V. and Holwerda, B. and Franx, M. and Spitler, L. and Smit, R. and Magee, D.},
doi = {10.1088/0004-637X/803/1/34},
eprint = {arXiv:1403.4295v4},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bouwens et al. - 2015 - UV luminosity functions at redshifts z ∼ 4 to z ∼ 10 10,000 galaxies from HST legacy fields.pdf:pdf},
issn = {15384357},
journal = {ApJ},
keywords = {Galaxies: evolution,Glaxies: high-redshift},
number = {1},
pages = {1--49},
title = {{UV luminosity functions at redshifts z ∼ 4 to z ∼ 10: 10,000 galaxies from HST legacy fields}},
volume = {803},
year = {2015}
}
@article{Massey2007,
abstract = {We present a three dimensional cosmic shear analysis of the Hubble Space Telescope COSMOS survey, the largest ever optical imaging program performed in space. We have measured the shapes of galaxies for the tell-tale distortions caused by weak gravitational lensing, and traced the growth of that signal as a function of redshift. Using both 2D and 3D analyses, we measure cosmological parameters Omega_m, the density of matter in the universe, and sigma_8, the normalization of the matter power spectrum. The introduction of redshift information tightens the constraints by a factor of three, and also reduces the relative sampling (or "cosmic") variance compared to recent surveys that may be larger but are only two dimensional. From the 3D analysis, we find sigma_8*(Omega_m/0.3)^0.44=0.866+^0.085_-0.068 at 68% confidence limits, including both statistical and potential systematic sources of error in the total budget. Indeed, the absolute calibration of shear measurement methods is now the dominant source of uncertainty. Assuming instead a baseline cosmology to fix the geometry of the universe, we have measured the growth of structure on both linear and non-linear physical scales. Our results thus demonstrate a proof of concept for tomographic analysis techniques that have been proposed for future weak lensing surveys by a dedicated wide-field telescope in space.},
archivePrefix = {arXiv},
arxivId = {astro-ph/0701480},
author = {Massey, Richard and Rhodes, Jason and Leauthaud, Alexie and Capak, Peter and Ellis, Richard and Koekemoer, Anton and Refregier, Alexandre and Scoville, Nick and Taylor, James E. and Albert, Justin and Berge, Joel and Heymans, Catherine and Johnston, David and Kneib, Jean‐Paul and Mellier, Yannick and Mobasher, Bahram and Semboloni, Elisabetta and Shopbell, Patrick and Tasca, Lidia and {Van Waerbeke}, Ludovic},
doi = {10.1086/516599},
eprint = {0701480},
issn = {0067-0049},
journal = {ApJS},
keywords = {Cosmology: Gravitational Lensing,Cosmology: Large-Scale Structure of Universe,Cosmology: Observations},
month = {sep},
number = {1},
pages = {239--253},
primaryClass = {astro-ph},
title = {{COSMOS: Three‐dimensional Weak Lensing and the Growth of Structure}},
url = {http://arxiv.org/abs/astro-ph/0701480%0Ahttp://dx.doi.org/10.1086/516599},
volume = {172},
year = {2007}
}
@article{Schrabback2010,
abstract = {We present a comprehensive analysis of weak gravitational lensing by large-scale structure in the Hubble Space Telescope Cosmic Evolution Survey (COSMOS), in which we combine space-based galaxy shape measurements with ground-based photometric redshifts to study the redshift dependence of the lensing signal and constrain cosmological parameters. After applying our weak lensing-optimized data reduction, principal-component interpolation for the spatially, and temporally varying ACS point-spread function, and improved modelling of charge-transfer inefficiency, we measured a lensing signal that is consistent with pure gravitational modes and no significant shape systematics. We carefully estimated the statistical uncertainty from simulated COSMOS-like fields obtained from ray-tracing through the Millennium Simulation, including the full non-Gaussian sampling variance. We tested our lensing pipeline on simulated space-based data, recalibrated non-linear power spectrum corrections using the ray-tracing analysis, employed photometric redshift information to reduce potential contamination by intrinsic galaxy alignments, and marginalized over systematic uncertainties. We find that the weak lensing signal scales with redshift as expected from general relativity for a concordance $\Lambda$CDM cosmology, including the full cross-correlations between different redshift bins. Assuming a flat $\Lambda$CDM cosmology, we measure $\sigma$8($\Omega$ m/0.3)0.51 = 0.75±0.08 from lensing, in perfect agreement with WMAP-5, yielding joint constraints $\Omega$m = 0.266+0.025-0.023, $\sigma$8 = 0.802 +0.028-0.029 (all 68.3% conf.). Dropping the assumption of flatness and using priors from the HST Key Project and Big-Bang nucleosynthesis only, we find a negative deceleration parameter q0 at 94.3% confidence from the tomographic lensing analysis, providing independent evidence of the accelerated expansion of the Universe. For a flat wCDM cosmology and prior w $\epsilon$ [-2,0], we obtain w <-0.41 (90% conf.). Our dark energy constraints are still relatively weak solely due to the limited area of COSMOS. However, they provide an important demonstration of the usefulness of tomographic weak lensing measurements from space. {\textcopyright} ESO 2010.},
archivePrefix = {arXiv},
arxivId = {0911.0053},
author = {Schrabback, T. and Hartlap, J. and Joachimi, B. and Kilbinger, M. and Simon, P. and Benabed, K. and Brada{\v{c}}, M. and Eifler, T. and Erben, T. and Fassnacht, C. D. and High, F. William and Hilbert, S. and Hildebrandt, H. and Hoekstra, H. and Kuijken, K. and Marshall, P. J. and Mellier, Y. and Morganson, E. and Schneider, P. and Semboloni, E. and {Van Waerbeke}, L. and Velander, M.},
doi = {10.1051/0004-6361/200913577},
eprint = {0911.0053},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schrabback et al. - 2010 - Evidence of the accelerated expansion of the Universe from weak lensing tomography with COSMOS.pdf:pdf},
issn = {14320746},
journal = {A\&A},
keywords = {cosmological parameters,dark matter,gravitational lensing: weak,large-scale structure of Universe},
number = {19},
title = {{Evidence of the accelerated expansion of the Universe from weak lensing tomography with COSMOS}},
volume = {516},
year = {2010}
}
@article{Brown2018,
abstract = {Context. We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. Aims. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Methods. The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. Results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the GBP (330-680 nm) and GRP (630-1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14 000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia-CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent. Conclusions. Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.},
archivePrefix = {arXiv},
arxivId = {1804.09365},
author = {Brown, A. G.A. and Vallenari, A. and Prusti, T. and {De Bruijne}, J. H.J. and Babusiaux, C. and Bailer-Jones, C. A.L. and Biermann, M. and Evans, D. W. and Eyer, L. and Jansen, F. and Jordi, C. and Klioner, S. A. and Lammers, U. and Lindegren, L. and Luri, X. and Mignard, F. and Panem, C. and Pourbaix, D. and Randich, S. and Sartoretti, P. and Siddiqui, H. I. and Soubiran, C. and {Van Leeuwen}, F. and Walton, N. A. and Arenou, F. and Bastian, U. and Cropper, M. and Drimmel, R. and Katz, D. and Lattanzi, M. G. and Bakker, J. and Cacciari, C. and Casta{\~{n}}eda, J. and Chaoul, L. and Cheek, N. and {De Angeli}, F. and Fabricius, C. and Guerra, R. and Holl, B. and Masana, E. and Messineo, R. and Mowlavi, N. and Nienartowicz, K. and Panuzzo, P. and Portell, J. and Riello, M. and Seabroke, G. M. and Tanga, P. and Th{\'{e}}venin, F. and Gracia-Abril, G. and Comoretto, G. and Garcia-Reinaldos, M. and Teyssier, D. and Altmann, M. and Andrae, R. and Audard, M. and Bellas-Velidis, I. and Benson, K. and Berthier, J. and Blomme, R. and Burgess, P. and Busso, G. and Carry, B. and Cellino, A. and Clementini, G. and Clotet, M. and Creevey, O. and Davidson, M. and {De Ridder}, J. and Delchambre, L. and Dell'Oro, A. and Ducourant, C. and Fern{\'{a}}ndez-Hern{\'{a}}ndez, J. and Fouesneau, M. and Fr{\'{e}}mat, Y. and Galluccio, L. and Garc{\'{i}}a-Torres, M. and Gonz{\'{a}}lez-N{\'{u}}{\~{n}}ez, J. and Gonz{\'{a}}lez-Vidal, J. J. and Gosset, E. and Guy, L. P. and Halbwachs, J. L. and Hambly, N. C. and Harrison, D. L. and Hern{\'{a}}ndez, J. and Hestroffer, D. and Hodgkin, S. T. and Hutton, A. and Jasniewicz, G. and Jean-Antoine-Piccolo, A. and Jordan, S. and Korn, A. J. and Krone-Martins, A. and Lanzafame, A. C. and Lebzelter, T. and L{\"{o}}ffler, W. and Manteiga, M. and Marrese, P. M. and Mart{\'{i}}n-Fleitas, J. M. and Moitinho, A. and Mora, A. and Muinonen, K. and Osinde, J. and Pancino, E. and Pauwels, T. and Petit, J. M. and Recio-Blanco, A. and Richards, P. J. and Rimoldini, L. and Robin, A. C. and Sarro, L. M. and Siopis, C. and Smith, M. and Sozzetti, A. and S{\"{u}}veges, M. and Torra, J. and {Van Reeven}, W. and Abbas, U. and {Abreu Aramburu}, A. and Accart, S. and Aerts, C. and Altavilla, G. and {\'{A}}lvarez, M. A. and Alvarez, R. and Alves, J. and Anderson, R. I. and Andrei, A. H. and {Anglada Varela}, E. and Antiche, E. and Antoja, T. and Arcay, B. and Astraatmadja, T. L. and Bach, N. and Baker, S. G. and Balaguer-N{\'{u}}{\~{n}}ez, L. and Balm, P. and Barache, C. and Barata, C. and Barbato, D. and Barblan, F. and Barklem, P. S. and Barrado, D. and Barros, M. and Barstow, M. A. and {Bartholom{\'{e}} Mu{\~{n}}oz}, S. and Bassilana, J. L. and Becciani, U. and Bellazzini, M. and Berihuete, A. and Bertone, S. and Bianchi, L. and Bienaym{\'{e}}, O. and Blanco-Cuaresma, S. and Boch, T. and Boeche, C. and Bombrun, A. and Borrachero, R. and Bossini, D. and Bouquillon, S. and Bourda, G. and Bragaglia, A. and Bramante, L. and Breddels, M. A. and Bressan, A. and Brouillet, N. and Br{\"{u}}semeister, T. and Brugaletta, E. and Bucciarelli, B. and Burlacu, A. and Busonero, D. and Butkevich, A. G. and Buzzi, R. and Caffau, E. and Cancelliere, R. and Cannizzaro, G. and Cantat-Gaudin, T. and Carballo, R. and Carlucci, T. and Carrasco, J. M. and Casamiquela, L. and Castellani, M. and Castro-Ginard, A. and Charlot, P. and Chemin, L. and Chiavassa, A. and Cocozza, G. and Costigan, G. and Cowell, S. and Crifo, F. and Crosta, M. and Crowley, C. and Cuypersy, J. and Dafonte, C. and Damerdji, Y. and Dapergolas, A. and David, P. and David, M. and {De Laverny}, P. and {De Luise}, F. and {De March}, R. and {De Martino}, D. and {De Souza}, R. and {De Torres}, A. and Debosscher, J. and {Del Pozo}, E. and Delbo, M. and Delgado, A. and Delgado, H. E. and {Di Matteo}, P. and Diakite, S. and Diener, C. and Distefano, E. and Dolding, C. and Drazinos, P. and Dur{\'{a}}n, J. and Edvardsson, B. and Enke, H. and Eriksson, K. and Esquej, P. and {Eynard Bontemps}, G. and Fabre, C. and Fabrizio, M. and Faigler, S. and Falc{\~{a}}o, A. J. and {Farr{\`{a}}s Casas}, M. and Federici, L. and Fedorets, G. and Fernique, P. and Figueras, F. and Filippi, F. and Findeisen, K. and Fonti, A. and Fraile, E. and Fraser, M. and Fr{\'{e}}zouls, B. and Gai, M. and Galleti, S. and Garabato, D. and Garc{\'{i}}a-Sedano, F. and Garofalo, A. and Garralda, N. and Gavel, A. and Gavras, P. and Gerssen, J. and Geyer, R. and Giacobbe, P. and Gilmore, G. and Girona, S. and Giuffrida, G. and Glass, F. and Gomes, M. and Granvik, M. and Gueguen, A. and Guerrier, A. and Guiraud, J. and Guti{\'{e}}rrez-S{\'{a}}nchez, R. and Haigron, R. and Hatzidimitriou, D. and Hauser, M. and Haywood, M. and Heiter, U. and Helmi, A. and Heu, J. and Hilger, T. and Hobbs, D. and Hofmann, W. and Holland, G. and Huckle, H. E. and Hypki, A. and Icardi, V. and Jan{\ss}en, K. and {De Fombelle}, G. Jevardat and Jonker, P. G. and Juh{\'{a}}sz, L. and Julbe, F. and Karampelas, A. and Kewley, A. and Klar, J. and Kochoska, A. and Kohley, R. and Kolenberg, K. and Kontizas, M. and Kontizas, E. and Koposov, S. E. and Kordopatis, G. and Kostrzewa-Rutkowska, Z. and Koubsky, P. and Lambert, S. and Lanza, A. F. and Lasne, Y. and Lavigne, J. B. and {Le Fustec}, Y. and {Le Poncin-Lafitte}, C. and Lebreton, Y. and Leccia, S. and Leclerc, N. and Lecoeur-Taibi, I. and Lenhardt, H. and Leroux, F. and Liao, S. and Licata, E. and Lindstr{\o}m, H. E.P. and Lister, T. A. and Livanou, E. and Lobel, A. and L{\'{o}}pez, M. and Managau, S. and Mann, R. G. and Mantelet, G. and Marchal, O. and Marchant, J. M. and Marconi, M. and Marinoni, S. and Marschalk{\'{o}}, G. and Marshall, D. J. and Martino, M. and Marton, G. and Mary, N. and Massari, D. and Matijevi{\'{c}}, G. and Mazeh, T. and McMillan, P. J. and Messina, S. and Michalik, D. and Millar, N. R. and Molina, D. and Molinaro, R. and Moln{\'{a}}r, L. and Montegriffo, P. and Mor, R. and Morbidelli, R. and Morel, T. and Morris, D. and Mulone, A. F. and Muraveva, T. and Musella, I. and Nelemans, G. and Nicastro, L. and Noval, L. and O'Mullane, W. and Ord{\'{e}}novic, C. and Ord{\'{o}}{\~{n}}ez-Blanco, D. and Osborne, P. and Pagani, C. and Pagano, I. and Pailler, F. and Palacin, H. and Palaversa, L. and Panahi, A. and Pawlak, M. and Piersimoni, A. M. and Pineau, F. X. and Plachy, E. and Plum, G. and Poggio, E. and Poujoulet, E. and Pr{\v{s}}a, A. and Pulone, L. and Racero, E. and Ragaini, S. and Rambaux, N. and Ramos-Lerate, M. and Regibo, S. and Reyl{\'{e}}, C. and Riclet, F. and Ripepi, V. and Riva, A. and Rivard, A. and Rixon, G. and Roegiers, T. and Roelens, M. and Romero-G{\'{o}}mez, M. and Rowell, N. and Royer, F. and Ruiz-Dern, L. and Sadowski, G. and {Sagrist{\`{a}} Sell{\'{e}}s}, T. and Sahlmann, J. and Salgado, J. and Salguero, E. and Sanna, N. and Santana-Ros, T. and Sarasso, M. and Savietto, H. and Schultheis, M. and Sciacca, E. and Segol, M. and Segovia, J. C. and S{\'{e}}gransan, D. and Shih, I. C. and Siltala, L. and Silva, A. F. and Smart, R. L. and Smith, K. W. and Solano, E. and Solitro, F. and Sordo, R. and {Soria Nieto}, S. and Souchay, J. and Spagna, A. and Spoto, F. and Stampa, U. and Steele, I. A. and Steidelm{\"{u}}ller, H. and Stephenson, C. A. and Stoev, H. and Suess, F. F. and Surdej, J. and Szabados, L. and Szegedi-Elek, E. and Tapiador, D. and Taris, F. and Tauran, G. and Taylor, M. B. and Teixeira, R. and Terrett, D. and Teyssandier, P. and Thuillot, W. and Titarenko, A. and {Torra Clotet}, F. and Turon, C. and Ulla, A. and Utrilla, E. and Uzzi, S. and Vaillant, M. and Valentini, G. and Valette, V. and {Van Elteren}, A. and {Van Hemelryck}, E. and {Van Leeuwen}, M. and Vaschetto, M. and Vecchiato, A. and Veljanoski, J. and Viala, Y. and Vicente, D. and Vogt, S. and {Von Essen}, C. and Voss, H. and Votruba, V. and Voutsinas, S. and Walmsley, G. and Weiler, M. and Wertz, O. and Wevers, T. and Wyrzykowski and Yoldas, A. and {\v{Z}}erjal, M. and Ziaeepour, H. and Zorec, J. and Zschocke, S. and Zucker, S. and Zurbach, C. and Zwitter, T.},
doi = {10.1051/0004-6361/201833051},
eprint = {1804.09365},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Brown et al. - 2018 - Summary of the contents and survey properties.pdf:pdf},
issn = {14320746},
journal = {A\&A},
keywords = {Asteroids: general,Astrometry,Catalogs,Minor planets,Stars: fundamental parameters,Stars: variables: general,Techniques: radial velocities},
title = {{Summary of the contents and survey properties}},
url = {http://arxiv.org/abs/1804.09365%0Ahttp://dx.doi.org/10.1051/0004-6361/201833051},
volume = {616},
year = {2018}
}
@article{Brown2020,
	author = {{Gaia Collaboration} and {Brown, A. G. A.} and {Vallenari, A.} and {Prusti, T.} and {de Bruijne, J. H. J.} and {Babusiaux, C.} and {Biermann, M.} and {Creevey, O. L.} and {Evans, D. W.} and {Eyer, L.} and {Hutton, A.} and {Jansen, F.} and {Jordi, C.} and {Klioner, S. A.} and {Lammers, U.} and {Lindegren, L.} and {Luri, X.} and {Mignard, F.} and {Panem, C.} and {Pourbaix, D.} and {Randich, S.} and {Sartoretti, P.} and {Soubiran, C.} and {Walton, N. A.} and {Arenou, F.} and {Bailer-Jones, C. A. L.} and {Bastian, U.} and {Cropper, M.} and {Drimmel, R.} and {Katz, D.} and {Lattanzi, M. G.} and {van Leeuwen, F.} and {Bakker, J.} and {Cacciari, C.} and {Casta\~neda, J.} and {De Angeli, F.} and {Ducourant, C.} and {Fabricius, C.} and {Fouesneau, M.} and {Fr\'emat, Y.} and {Guerra, R.} and {Guerrier, A.} and {Guiraud, J.} and {Jean-Antoine Piccolo, A.} and {Masana, E.} and {Messineo, R.} and {Mowlavi, N.} and {Nicolas, C.} and {Nienartowicz, K.} and {Pailler, F.} and {Panuzzo, P.} and {Riclet, F.} and {Roux, W.} and {Seabroke, G. M.} and {Sordo, R.} and {Tanga, P.} and {Th\'evenin, F.} and {Gracia-Abril, G.} and {Portell, J.} and {Teyssier, D.} and {Altmann, M.} and {Andrae, R.} and {Bellas-Velidis, I.} and {Benson, K.} and {Berthier, J.} and {Blomme, R.} and {Brugaletta, E.} and {Burgess, P. W.} and {Busso, G.} and {Carry, B.} and {Cellino, A.} and {Cheek, N.} and {Clementini, G.} and {Damerdji, Y.} and {Davidson, M.} and {Delchambre, L.} and {Dell\'{}Oro, A.} and {Fern\'andez-Hern\'andez, J.} and {Galluccio, L.} and {Garc\'{\i}a-Lario, P.} and {Garcia-Reinaldos, M.} and {Gonz\'alez-N\'u\~nez, J.} and {Gosset, E.} and {Haigron, R.} and {Halbwachs, J.-L.} and {Hambly, N. C.} and {Harrison, D. L.} and {Hatzidimitriou, D.} and {Heiter, U.} and {Hern\'andez, J.} and {Hestroffer, D.} and {Hodgkin, S. T.} and {Holl, B.} and {Jan\ss{}en, K.} and {Jevardat de Fombelle, G.} and {Jordan, S.} and {Krone-Martins, A.} and {Lanzafame, A. C.} and {L\"offler, W.} and {Lorca, A.} and {Manteiga, M.} and {Marchal, O.} and {Marrese, P. M.} and {Moitinho, A.} and {Mora, A.} and {Muinonen, K.} and {Osborne, P.} and {Pancino, E.} and {Pauwels, T.} and {Petit, J.-M.} and {Recio-Blanco, A.} and {Richards, P. J.} and {Riello, M.} and {Rimoldini, L.} and {Robin, A. C.} and {Roegiers, T.} and {Rybizki, J.} and {Sarro, L. M.} and {Siopis, C.} and {Smith, M.} and {Sozzetti, A.} and {Ulla, A.} and {Utrilla, E.} and {van Leeuwen, M.} and {van Reeven, W.} and {Abbas, U.} and {Abreu Aramburu, A.} and {Accart, S.} and {Aerts, C.} and {Aguado, J. J.} and {Ajaj, M.} and {Altavilla, G.} and {\'Alvarez, M. A.} and {\'Alvarez Cid-Fuentes, J.} and {Alves, J.} and {Anderson, R. I.} and {Anglada Varela, E.} and {Antoja, T.} and {Audard, M.} and {Baines, D.} and {Baker, S. G.} and {Balaguer-N\'u\~nez, L.} and {Balbinot, E.} and {Balog, Z.} and {Barache, C.} and {Barbato, D.} and {Barros, M.} and {Barstow, M. A.} and {Bartolom\'e, S.} and {Bassilana, J.-L.} and {Bauchet, N.} and {Baudesson-Stella, A.} and {Becciani, U.} and {Bellazzini, M.} and {Bernet, M.} and {Bertone, S.} and {Bianchi, L.} and {Blanco-Cuaresma, S.} and {Boch, T.} and {Bombrun, A.} and {Bossini, D.} and {Bouquillon, S.} and {Bragaglia, A.} and {Bramante, L.} and {Breedt, E.} and {Bressan, A.} and {Brouillet, N.} and {Bucciarelli, B.} and {Burlacu, A.} and {Busonero, D.} and {Butkevich, A. G.} and {Buzzi, R.} and {Caffau, E.} and {Cancelliere, R.} and {C\'anovas, H.} and {Cantat-Gaudin, T.} and {Carballo, R.} and {Carlucci, T.} and {Carnerero, M. I.} and {Carrasco, J. M.} and {Casamiquela, L.} and {Castellani, M.} and {Castro-Ginard, A.} and {Castro Sampol, P.} and {Chaoul, L.} and {Charlot, P.} and {Chemin, L.} and {Chiavassa, A.} and {Cioni, M.-R. L.} and {Comoretto, G.} and {Cooper, W. J.} and {Cornez, T.} and {Cowell, S.} and {Crifo, F.} and {Crosta, M.} and {Crowley, C.} and {Dafonte, C.} and {Dapergolas, A.} and {David, M.} and {David, P.} and {de Laverny, P.} and {De Luise, F.} and {De March, R.} and {De Ridder, J.} and {de Souza, R.} and {de Teodoro, P.} and {de Torres, A.} and {del Peloso, E. F.} and {del Pozo, E.} and {Delbo, M.} and {Delgado, A.} and {Delgado, H. E.} and {Delisle, J.-B.} and {Di Matteo, P.} and {Diakite, S.} and {Diener, C.} and {Distefano, E.} and {Dolding, C.} and {Eappachen, D.} and {Edvardsson, B.} and {Enke, H.} and {Esquej, P.} and {Fabre, C.} and {Fabrizio, M.} and {Faigler, S.} and {Fedorets, G.} and {Fernique, P.} and {Fienga, A.} and {Figueras, F.} and {Fouron, C.} and {Fragkoudi, F.} and {Fraile, E.} and {Franke, F.} and {Gai, M.} and {Garabato, D.} and {Garcia-Gutierrez, A.} and {Garc\'{\i}a-Torres, M.} and {Garofalo, A.} and {Gavras, P.} and {Gerlach, E.} and {Geyer, R.} and {Giacobbe, P.} and {Gilmore, G.} and {Girona, S.} and {Giuffrida, G.} and {Gomel, R.} and {Gomez, A.} and {Gonzalez-Santamaria, I.} and {Gonz\'alez-Vidal, J. J.} and {Granvik, M.} and {Guti\'errez-S\'anchez, R.} and {Guy, L. P.} and {Hauser, M.} and {Haywood, M.} and {Helmi, A.} and {Hidalgo, S. L.} and {Hilger, T.} and {Hladczuk, N.} and {Hobbs, D.} and {Holland, G.} and {Huckle, H. E.} and {Jasniewicz, G.} and {Jonker, P. 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	title = {Gaia Early Data Release 3 - Summary of the contents and survey properties (Corrigendum)},
	DOI= "10.1051/0004-6361/202039657e",
	url= "https://doi.org/10.1051/0004-6361/202039657e",
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	pages = "C3",
}
@article{Massey2010b,
abstract = {We update a physically motivated model of radiation damage in the Hubble Space Telescope Advanced Camera for Surveys/Wide Field Channel, using data up to mid-2010. We find that charge transfer inefficiency increased dramatically before shuttle Servicing Mission 4, with $\sim$1.3 charge traps now present per pixel. During detector readout, charge traps spuriously drag electrons behind all astronomical sources, degrading image quality in a way that affects object photometry, astrometry and morphology. Our detector readout model is robust to changes in operating temperature and background level, and can be used to iteratively remove the trailing by pushing electrons back to where they belong. The result is data taken in mid-2010 that recovers the quality of imaging obtained within the first six months of orbital operations. {\textcopyright} 2010 The Authors. Journal compilation {\textcopyright} 2010 RAS.},
archivePrefix = {arXiv},
arxivId = {1009.4335},
author = {Massey, Richard},
doi = {10.1111/j.1745-3933.2010.00959.x},
eprint = {1009.4335},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Massey - 2010 - Charge transfer inefficiency in the Hubble Space Telescope since Servicing Mission 4(2).pdf:pdf},
issn = {17453933},
journal = {MNRAS Letters},
keywords = {Instrumentation: detectors,Methods: data analysis,Space vehicles: instruments,Techniques: image processing},
month = {nov},
number = {1},
pages = {L109--L113},
title = {{Charge transfer inefficiency in the Hubble Space Telescope since Servicing Mission 4}},
volume = {409},
year = {2010}
}
@article{Massey2010d,
abstract = {Charge Transfer Inefficiency (CTI) due to radiation damage above the Earth's atmosphere creates spurious trailing in Hubble Space Telescope (HST) images. Radiation damage also creates unrelated warm pixels - but these happen to be perfect for measuring CTI. We model CTI in the Advanced Camera for Surveys (ACS)/Wide Field Channel and construct a physically motivated correction scheme. This operates on raw data, rather than secondary science products, by returning individual electrons to pixels from which they were unintentionally dragged during readout. We apply our correction to images from the HST Cosmic Evolution Survey (COSMOS), successfully reducing the CTI trails by a factor of ∼30 everywhere in the CCD and at all flux levels. We quantify changes in galaxy photometry, astrometry and shape. The remarkable 97 per cent level of correction is more than sufficient to enable a (forthcoming) reanalysis of downstream science products and the collection of larger surveys. {\textcopyright} 2009 RAS.},
archivePrefix = {arXiv},
arxivId = {0909.0507},
author = {Massey, Richard and Stoughton, Chris and Leauthaud, Alexie and Rhodes, Jason and Koekemoer, Anton and Ellis, Richard and Shaghoulian, Edgar},
doi = {10.1111/j.1365-2966.2009.15638.x},
eprint = {0909.0507},
file = {:C\:/Users/Jammy/Documents/Papers/CTI/Massey2010PixelBased.pdf:pdf},
issn = {00358711},
journal = {MNRAS},
keywords = {Instrumentation: detectors,Methods: data analysis,Space vehicles: instruments},
number = {1},
pages = {371--384},
title = {{Pixel-based correction for charge transfer inefficiency in the hubble space telescope advanced camera for surveys}},
volume = {401},
year = {2010}
}
@article{Massey2014,
abstract = {Charge-coupled device (CCD) detectors, widely used to obtain digital imaging, can be damaged by high energy radiation. Degraded images appear blurred, because of an effect known as Charge Transfer Inefficiency (CTI), which trails bright objects as the image is read out. It is often possible to correct most of the trailing during post-processing, by moving flux back to where it belongs. We compare several popular algorithms for this: quantifying the effect of their physical assumptions and tradeoffs between speed and accuracy. We combine their best elements to construct a more accurate model of damaged CCDs in the Hubble Space Telescope's Advanced Camera for Surveys/Wide Field Channel, and update it using data up to early 2013. Our algorithm now corrects 98 per cent of CTI trailing in science exposures, a substantial improvement over previous work. Further progress will be fundamentally limited by the presence of read noise. Read noise is added after charge transfer so does not get trailed-but it is incorrectly untrailed during post-processing. {\textcopyright} 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.},
archivePrefix = {arXiv},
arxivId = {1401.1151},
author = {Massey, Richard and Schrabback, Tim and Cordes, Oliver and Marggraf, Ole and Israel, Holger and Miller, Lance and Hall, David and Cropper, Mark and Prod'homme, Thibaut and Niemi, Sami Matias},
doi = {10.1093/mnras/stu012},
eprint = {1401.1151},
file = {:C\:/Users/Jammy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Massey et al. - 2014 - An improved model of charge transfer inefficiency and correction algorithm for the Hubble Space Telescope.pdf:pdf},
issn = {00358711},
journal = {MNRAS},
keywords = {Instrumentation: Detectors,Methods: Data analysis,Space vehicles: Instruments},
month = {mar},
number = {1},
pages = {887--907},
title = {{An improved model of charge transfer inefficiency and correction algorithm for the Hubble Space Telescope}},
url = {http://arxiv.org/abs/1401.1151},
volume = {439},
year = {2014}
}
@article{Halverson2016,
abstract = {We describe a detailed radial velocity error budget for the NASA-NSF Extreme Precision Doppler Spectrometer instrument concept NEID (NN-explore Exoplanet Investigations with Doppler spectroscopy). Such an instrument performance budget is a necessity for both identifying the variety of noise sources currently limiting Doppler measurements, and estimating the achievable performance of next generation exoplanet hunting Doppler spectrometers. For these instruments, no single source of instrumental error is expected to set the overall measurement floor. Rather, the overall instrumental measurement precision is set by the contribution of many individual error sources. We use a combination of numerical simulations, educated estimates based on published materials, extrapolations of physical models, results from laboratory measurements of spectroscopic subsystems, and informed upper limits for a variety of error sources to identify likely sources of systematic error and construct our global instrument performance error budget. While natively focused on the performance of the NEID instrument, this modular performance budget is immediately adaptable to a number of current and future instruments. Such an approach is an important step in charting a path towards improving Doppler measurement precisions to the levels necessary for discovering Earth-like planets.},
archivePrefix = {arXiv},
arxivId = {1607.05634},
author = {Halverson, Samuel and Terrien, Ryan and Mahadevan, Suvrath and Roy, Arpita and Bender, Chad and Stef{\'{a}}nsson, Gudmundur K and Monson, Andrew and Levi, Eric and Hearty, Fred and Blake, Cullen and McElwain, Michael and Schwab, Christian and Ramsey, Lawrence and Wright, Jason and Wang, Sharon and Gong, Qian and Roberston, Paul},
doi = {10.1117/12.2232761},
eprint = {1607.05634},
isbn = {9781510601956},
issn = {1996756X},
journal = {Ground-based and Airborne Instrumentation for Astronomy VI},
keywords = {exoplanets,high resolution spectroscopy,radial velocity instrumentation,systems engineering},
pages = {99086P},
title = {{A comprehensive radial velocity error budget for next generation Doppler spectrometers}},
url = {http://arxiv.org/abs/1607.05634%7B%5C%25%7D0Ahttp://dx.doi.org/10.1117/12.2232761},
volume = {9908},
year = {2016}
}
@ARTICLE{Bellini2013,
       author = {{Bellini}, A. and {van der Marel}, R.~P. and {Anderson}, J.},
        title = "{HST proper motions of stars within globular clusters.}",
      journal = {\memsai},
     keywords = {Astrometry: proper motions, Stars: Population II, Galaxy: globular clusters, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Galaxy Astrophysics},
         year = 2013,
        month = jan,
        doi = {10.48550/arXiv.1301.2338},
       volume = {84},
        pages = {140},
archivePrefix = {arXiv},
       eprint = {1301.2338},
 primaryClass = {astro-ph.SR},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2013MmSAI..84..140B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Vehtari2020,
abstract = {A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.},
archivePrefix = {arXiv},
arxivId = {1412.4869},
author = {Vehtari, Aki and Gelman, Andrew and Sivula, Tuomas and Jyl{\"{a}}nki, Pasi and Tran, Dustin and Sahai, Swupnil and Blomstedt, Paul and Cunningham, John P. and Schiminovich, David and Robert, Christian P.},
eprint = {1412.4869},
file = {:C\:/Users/Jammy/Documents/Papers/Stats/Vehtair2020EP.pdf:pdf},
issn = {15337928},
journal = {Journal of Machine Learning Research},
keywords = {Bayesian computation,Data partitioning,Expectation propagation,Hierarchical models,Statistical computing},
pages = {1--53},
title = {{Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data}},
volume = {21},
year = {2020}
}
@inproceedings{c3tm,
          volume = {10709},
           month = {July},
          author = {Jesper Skottfelt and David J. Hall and Benjamin Dryer and Ross Burgon and Andrew D. Holland},
          series = {Proceedings},
          doi = {10.1117/12.2309944},
       booktitle = {High Energy, Optical, and Infrared Detectors for Astronomy VIII},
           title = {C3TM: CEI CCD charge transfer model for radiation damage analysis and testing},
       publisher = {Society of Photo-Optical Instrumentation Engineers},
            year = {2018},
             url = {http://oro.open.ac.uk/56280/},
        abstract = {Radiation induced defects in the silicon lattice of Charge Couple Devices (CCDs) are able to trap electrons during read out and thus create a smearing effect that is detrimental to the scientific data. To further our understanding of the positions and properties of individual radiation-induced traps and how they affect space- borne CCD performance, we have created the Centre for Electronic Imaging (CEI) CCD Charge Transfer Model (C3TM). This model simulates the physical processes taking place when transferring signal through a radiation damaged CCD. C3TM is a Monte Carlo model based on Shockley-Read-Hall theory, and it mimics the physical properties in the CCD as closely as possible. It runs on a sub-electrode level taking device specific charge density simulations made with professional TCAD software as direct input. Each trap can be specified with 3D positional information, emission time constant and other physical properties. The model is therefore also able to simulate multi-level clocking and other complex clocking schemes, such as trap pumping.}
}
@INPROCEEDINGS{cdm03,
       author = {{Short}, Alexander and {Prod'homme}, Thibaut and {Weiler}, Michael and {Brown}, Scott and {Brown}, Anthony},
        title = "{A fast model of radiation-induced electron trapping in CCDs for implementation in the Gaia data processing}",
    booktitle = {High Energy, Optical, and Infrared Detectors for Astronomy IV},
         year = 2010,
       editor = {{Holland}, Andrew D. and {Dorn}, David A.},
       series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
       volume = {7742},
        month = jul,
          eid = {774212},
        pages = {774212},
          doi = {10.1117/12.856386},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2010SPIE.7742E..12S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{Anderson2010,
       author = {{Anderson}, Jay and {Bedin}, Luigi R.},
        title = "{An Empirical Pixel-Based Correction for Imperfect CTE. I. HST{\textquoteright}s Advanced Camera for Surveys}",
      journal = {\pasp},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2010,
        month = sep,
       volume = {122},
       number = {895},
        pages = {1035},
          doi = {10.1086/656399},
archivePrefix = {arXiv},
       eprint = {1007.3987},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2010PASP..122.1035A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@MISC{Anderson2018,
       author = {{Anderson}, Jay and {Ryon}, Jenna E.},
        title = "{Improving the Pixel-Based CTE-correction Model for ACS/WFC}",
 howpublished = {Instrument Science Report ACS 2018-04, 37 pages},
         year = 2018,
        month = aug,
        pages = {4},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018acs..rept....4A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@MISC{Anderson2021,
       author = {{Anderson}, Jay and {Baggett}, Sylvia and {Kuhn}, Ben},
        title = "{Updating the WFC3/UVIS CTE model and Mitigation Strategies}",
     keywords = {Hubble Space Telescope, HST, STScI, Space Telescope Science Institute, WFC3, Wide Field Camera 3, UVIS, Charge-transfer efficiency, Pixel-based correction, Calibration, Photometry, Pipeline},
 howpublished = {Instrument Science Report 2021-9, 44 pages},
         year = 2021,
        month = jun,
        pages = {9},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021wfc..rept....9A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}