| Title: |
Are light curve classification metrics good proxies for SN Ia cosmological constraining power? |
| Authors: |
Malz, Alex I.; Dai, Mi; Ponder, Kara A.; Ishida, Emille; González-Gaitán, Santiago; Durgesh, Rupesh; Krone-Martins, Alberto; Souza, Rafael S. de; Kennamer, Noble; Sreejith, Sreevarsha; Galbany, Lluís; LSST Dark Energy Science Collaboration; COIN Collaboration |
| Contributors: |
Max Planck Society; Alexander von Humboldt Foundation; Schmidt Sciences; Johns Hopkins University; Ministerio de Ciencia e Innovación (España); European Commission; Consejo Superior de Investigaciones Científicas (España); Centre National de la Recherche Scientifique (France); Department of Energy (US); Institut National de Physique Nucléaire et de Physique des Particules (France); Science and Technology Facilities Council (UK); National Science Foundation (US); Malz, Alex I.; Consejo Superior de Investigaciones Científicas https://ror.org/02gfc7t72 |
| Publisher Information: |
EDP Sciences |
| Publication Year: |
2025 |
| Collection: |
Digital.CSIC (Consejo Superior de Investigaciones Científicas / Spanish National Research Council) |
| Subject Terms: |
Cosmological parameters; Methods: data analysis; Methods: miscellaneous; Methods: observational; Methods: statistical; Supernovae: general |
| Description: |
[Context] When selecting a light curve classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process. ; [Aims] This study tests the assumption that light curve classification metrics are an appropriate proxy for cosmology metrics. ; [Methods] We emulated photometric SN Ia cosmology light curve samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. ; [Results] We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. ; [Conclusions] Based on these findings, we discourage any exclusive reliance on light curve classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest. ; This paper has undergone internal review in the LSST Dark Energy Science Collaboration. The authors would like to thank Renée Hložek, Alex Kim, and Maria Vincenzi for serving as the LSST-DESC publication review committee, as well as David O. Jones, for his comments and suggestions that improved the quality of this manuscript. AIM acknowledges support ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
#PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/AEI//RYC2019-027683; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115253GA-I00/ES/ACORRALANDO LA "TENSION DE HUBBLE" ESTUDIANDO ERRORES SISTEMATICOS Y RECONSTRUYENDO DEL UNIVERSO LOCAL CON SUPERNOVAS/; https://doi.org/10.1051/0004-6361/202346891; Sí; Astronomy & Astrophysics 694: A130 (2025); https://hdl.handle.net/10261/385016; http://dx.doi.org/10.13039/100007880; http://dx.doi.org/10.13039/501100000271; http://dx.doi.org/10.13039/501100000780; http://dx.doi.org/10.13039/100000015; http://dx.doi.org/10.13039/501100004794; http://dx.doi.org/10.13039/100005156; http://dx.doi.org/10.13039/501100003339; http://dx.doi.org/10.13039/100000001; http://dx.doi.org/10.13039/501100004837; http://dx.doi.org/10.13039/501100004189; http://arxiv.org/abs/2305.14421v1 |
| DOI: |
10.1051/0004-6361/202346891 |
| DOI: |
10.13039/100007880 |
| DOI: |
10.13039/501100000271 |
| DOI: |
10.13039/501100000780 |
| DOI: |
10.13039/100000015 |
| DOI: |
10.13039/501100004794 |
| DOI: |
10.13039/100005156 |
| DOI: |
10.13039/501100003339 |
| DOI: |
10.13039/100000001 |
| DOI: |
10.13039/501100004837 |
| DOI: |
10.13039/501100004189 |
| Availability: |
https://hdl.handle.net/10261/385016; https://doi.org/10.1051/0004-6361/202346891; https://doi.org/10.13039/100007880; https://doi.org/10.13039/501100000271; https://doi.org/10.13039/501100000780; https://doi.org/10.13039/100000015; https://doi.org/10.13039/501100004794; https://doi.org/10.13039/100005156; https://doi.org/10.13039/501100003339; https://doi.org/10.13039/100000001; https://doi.org/10.13039/501100004837; https://doi.org/10.13039/501100004189; http://arxiv.org/abs/2305.14421v1 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.A23AE1F7 |
| Database: |
BASE |