Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/acbb09 |
| _version_ | 1797695756579110912 |
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| author | Catarina S. Alves Hiranya V. Peiris Michelle Lochner Jason D. McEwen Richard Kessler The LSST Dark Energy Science Collaboration |
| author_facet | Catarina S. Alves Hiranya V. Peiris Michelle Lochner Jason D. McEwen Richard Kessler The LSST Dark Energy Science Collaboration |
| author_sort | Catarina S. Alves |
| collection | DOAJ |
| description | The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST. |
| first_indexed | 2024-03-12T03:16:47Z |
| format | Article |
| id | doaj.art-3253eca4b3064a8cb74f333cdf8df351 |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-03-12T03:16:47Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-3253eca4b3064a8cb74f333cdf8df3512023-09-03T14:08:33ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492023-01-0126524310.3847/1538-4365/acbb09Impact of Rubin Observatory Cadence Choices on Supernovae Photometric ClassificationCatarina S. Alves0https://orcid.org/0000-0002-6164-9044Hiranya V. Peiris1https://orcid.org/0000-0002-2519-584XMichelle Lochner2https://orcid.org/0000-0003-2221-8281Jason D. McEwen3Richard Kessler4https://orcid.org/0000-0003-3221-0419The LSST Dark Energy Science CollaborationDepartment of Physics & Astronomy, University College London , Gower Street, London WC1E 6BT, UK ; catarina.alves.18@ucl.ac.ukDepartment of Physics & Astronomy, University College London , Gower Street, London WC1E 6BT, UK ; catarina.alves.18@ucl.ac.uk; Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University , AlbaNova University Center, Stockholm SE-10691, SwedenDepartment of Physics and Astronomy, University of the Western Cape , Bellville, Cape Town, 7535, South Africa; South African Radio Astronomy Observatory , 2 Fir Street, Black River Park, Observatory, 7925, South AfricaMullard Space Science Laboratory, University College London , Holmbury St Mary, Dorking, Surrey RH5 6NT, UKKavli Institute for Cosmological Physics, University of Chicago , Chicago, IL 60637, USA; Department of Astronomy and Astrophysics, University of Chicago , Chicago, IL 60637, USAThe Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST.https://doi.org/10.3847/1538-4365/acbb09CosmologySupernovaeAstronomy softwareOpen source softwareAstronomy data analysisLight curve classification |
| spellingShingle | Catarina S. Alves Hiranya V. Peiris Michelle Lochner Jason D. McEwen Richard Kessler The LSST Dark Energy Science Collaboration Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification The Astrophysical Journal Supplement Series Cosmology Supernovae Astronomy software Open source software Astronomy data analysis Light curve classification |
| title | Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification |
| title_full | Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification |
| title_fullStr | Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification |
| title_full_unstemmed | Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification |
| title_short | Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification |
| title_sort | impact of rubin observatory cadence choices on supernovae photometric classification |
| topic | Cosmology Supernovae Astronomy software Open source software Astronomy data analysis Light curve classification |
| url | https://doi.org/10.3847/1538-4365/acbb09 |
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