| Title: |
Recovered supernova Ia rate from simulated LSST images |
| Authors: |
Petrecca, V.; Botticella, M. T.; Cappellaro, E.; Greggio, L.; Sánchez, B. O.; Möller, A.; Sako, M.; Graham, M. L.; Paolillo, M.; Bianco, F.; the LSST Dark Energy Science Collaboration |
| Publisher Information: |
Astronomy & Astrophysics |
| Publication Year: |
2024 |
| Collection: |
The University of Delaware Library Institutional Repository |
| Subject Terms: |
surveys; supernovae: general; galaxies: stellar content |
| Description: |
This 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. ; Aims. 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 ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://udspace.udel.edu/handle/19716/34583 |
| Availability: |
https://udspace.udel.edu/handle/19716/34583 |
| Rights: |
Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.4452C364 |
| Database: |
BASE |