Recovered supernova Ia rate from simulated LSST images

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Author(s)Petrecca, V.
Author(s)Botticella, M. T.
Author(s)Cappellaro, E.
Author(s)Greggio, L.
Author(s)Sánchez, B. O.
Author(s)Möller, A.
Author(s)Sako, M.
Author(s)Graham, M. L.
Author(s)Paolillo, M.
Author(s)Bianco, F.
Author(s)the LSST Dark Energy Science Collaboration
Date Accessioned2024-07-24T16:57:46Z
Date Available2024-07-24T16:57:46Z
Publication Date2024-05-24
DescriptionThis article was originally published in Astronomy & Astrophysics. The version of record is available at: https://doi.org/10.1051/0004-6361/202349012. © The Authors 2024. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
AbstractAims. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods. We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results. We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.
SponsorThis paper has undergone internal review in the LSST Dark Energy Science Collaboration. We thank the internal reviewers Francisco Forster, Dominique Fouchez, and Christopher Frohmaier for their comments. We also deeply thank Richard Kessler for his help and advice throughout the work, especially on photometric classification and photometric redshifts. The DESC acknowledges ongoing support from the Institut National de Physique Nucléaire et de Physique des Particules in France; the Science & Technology Facilities Council in the United Kingdom; and the Department of Energy, the National Science Foundation, and the LSST Corporation in the United States. DESC uses resources of the IN2P3 Computing Center (CC-IN2P3–Lyon/Villeurbanne – France) funded by the Centre National de la Recherche Scientifique; the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231; STFC DiRAC HPC Facilities, funded by UK BEIS National E-infrastructure capital grants; and the UK particle physics grid, supported by the GridPP Collaboration. This work was performed in part under DOE Contract DE-AC02-76SF00515. V.P. and M.T.B. were responsible for the overall analysis and interpretation of the data, and wrote the paper. E.C. contributed to the analysis and interpretation of the results. L.G. provided theoretical models for SN Ia progenitor systems. B.O.S. provided data from his previous work and hints on the analysis. A.M. contributed to photometric classification with SuperNNova, while M.S. contributed to photometric classification with PSNID. M.L.G. provided comments on photometric redshifts and helped exploiting DP0 and RSP resources. M.P. and F.B. provided final comments on the analysis and the results. All authors contributed with notes and comments to improve the clarity of the paper. A.M. is supported by the Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) project number DE23010005. M.S. acknowledges support from DOE grant DE-FOA-0002424 and NSF grant AST-2108094. M.P. and V.P. acknowledge the financial contribution from PRIN-MIUR 2022 funded by the European Union – Next Generation EU, and from the Timedomes grant within the “INAF 2023 Finanziamento della Ricerca Fondamentale”. The research has made use of the following Python software packages: Astropy (Astropy Collaboration 2013, 2018), Matplotlib (Hunter 2007), Pandas (van der Walt & Millman 2010), NumPy (van der Walt et al. 2011), SciPy (Virtanen et al. 2020). Other software specific for SN analysis are cited in the paper.
CitationPetrecca, V., M. T. Botticella, E. Cappellaro, L. Greggio, B. O. Sánchez, A. Möller, M. Sako, et al. “Recovered Supernova Ia Rate from Simulated LSST Images.” Astronomy & Astrophysics 686 (June 2024): A11. https://doi.org/10.1051/0004-6361/202349012.
ISSN1432-0746
URLhttps://udspace.udel.edu/handle/19716/34583
Languageen_US
PublisherAstronomy & Astrophysics
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
Keywordssurveys
Keywordssupernovae: general
Keywordsgalaxies: stellar content
TitleRecovered supernova Ia rate from simulated LSST images
TypeArticle
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