CD; Cosmological parameters; Cosmology: observations; Galaxies: elliptical and lenticular; Galaxies: structure; Methods: data analysis; Methods: statistical; Cosmology observations; Galaxies:structure; Lens model; Methods. Data analysis; Methods:statistical; Model method; Systematic bias; Astronomy and Astrophysics; Space and Planetary Science; astro-ph.CO; astro-ph.GA; astro-ph.IM
Abstract :
[en] Challenges inherent to high-resolution and high signal-to-noise data as well as model degeneracies can cause systematic biases in analyses of strong lens systems. In the past decade, the number of lens modeling methods has significantly increased, from purely analytical methods, to pixelated and non-parametric ones, or ones based on deep learning. We embraced this diversity by selecting different software packages and use them to blindly model independently simulated Hubble Space Telescope (HST) imaging data. To overcome the difficulties arising from using different codes and conventions, we used the COde-independent Organized LEns STandard (COOLEST) to store, compare, and release all models in a self-consistent and human-readable manner. From an ensemble of six modeling methods, we studied the recovery of the lens potential parameters and properties of the reconstructed source. In particular, we simulated and inferred parameters of an elliptical power-law mass distribution embedded in a shear field for the lens, while each modeling method reconstructs the source differently. We find that, overall, both lens and source properties are recovered reasonably well, but systematic biases arise in all methods. Interestingly, we do not observe that a single method is significantly more accurate than others, and the amount of bias largely depends on the specific lens or source property of interest. By combining posterior distributions from individual methods using equal weights, the maximal systematic biases on lens model parameters inferred from individual models are reduced by a factor of 5.4 on average. We investigated a selection of modeling effects that partly explain the observed biases, such as the cuspy nature of the background source and the accuracy of the point spread function. This work introduces, for the first time, a generic framework to compare and ease the combination of models obtained from different codes and methods, which will be key to retain accuracy in future strong lensing analyses.
Research Center/Unit :
STAR - Space sciences, Technologies and Astrophysics Research - ULiège
Disciplines :
Space science, astronomy & astrophysics
Author, co-author :
Galan, A. ; Technical University of Munich, TUM School of Natural Sciences, Department of Physics, Garching, Germany ; Max-Planck-Institut für Astrophysik, Garching, Germany ; Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, Versoix, Switzerland
Vernardos, G. ; Department of Physics and Astronomy, Lehman College of the CUNY, Bronx, United States ; American Museum of Natural History, Department of Astrophysics, New York, United States
Minor, Q.; American Museum of Natural History, Department of Astrophysics, New York, United States ; Borough of Manhattan Community College, City University of New York, Department of Science, New York, United States
Sluse, Dominique ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO)
Van de Vyvere, Lyne ; Université de Liège - ULiège > Unités de recherche interfacultaires > Space sciences, Technologies and Astrophysics Research (STAR)
Gomer, M.; STAR Institute, University of Liège, Quartier Agora, Liège, Belgium
Language :
English
Title :
Exploiting the diversity of modeling methods to probe systematic biases in strong lensing analyses
SNF - Schweizerischer Nationalfonds zur Förderung der wissenschaftlichen Forschung Schmidt Futures European Union
Funding text :
The authors thank the anonymous referee for reviewing the original manuscript and providing useful comments. This work originated in the Lensing Odyssey 2021 workshop10, and so we would like to acknowledge the organizers and attendees for the fruitful discussions. AG acknowledges the Swiss National Science Foundation (SNSF) for supporting this work. This work was also supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (COSMICLENS: grant agreement No 787886). GV and QM were both supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. QM gratefully acknowledges a grant of computer time from ACCESS allocation TG-AST130007. This research has made use of SCIPY (Virtanen et al. 2020), NUMPY (Oliphant 2006; Van Der Walt et al. 2011), MATPLOTLIB (Hunter 2007), ASTROPY (Astropy Collaboration 2013; Price-Whelan et al. 2018), JUPYTER (Kluyver et al. 2016) and GETDIST (Lewis 2019).The authors thank the anonymous referee for reviewing the original manuscript and providing useful comments. This work originated in the Lensing Odyssey 2021 workshop10, and so we would like to acknowledge the organizers and attendees for the fruitful discussions. AG acknowledges the Swiss National Science Foundation (SNSF) for supporting this work. This work was also supported by the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation programme (COSMICLENS: grant agreement No 787886). GV and QM were both supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. QM gratefully acknowledges a grant of computer time from ACCESS allocation TG-AST130007. This research has made use of S CI P Y (Virtanen et al. 2020), N UM P Y (Oliphant 2006; Van Der Walt et al. 2011), M ATPLOTLIB (Hunter 2007), A STROPY (Astropy Collaboration 2013; Price-Whelan et al. 2018), J UPYTER (Kluyver et al. 2016) and G ET D IST (Lewis 2019).
Commentary :
19 pages, 12 figures (excluding appendix). Published in A&A
