Alves, CS;
Peiris, HV;
Lochner, M;
McEwen, JD;
Jr, TA;
Biswas, R;
(2022)
Considerations for optimizing photometric classification of supernovae from the Rubin Observatory.
The Astrophysical Journal Supplement Series
, 258
(2)
, Article 23. 10.3847/1538-4365/ac3479.
Preview |
Text
Peiris_Alves_2022_ApJS_258_23.pdf Download (1MB) | Preview |
Abstract
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all SNe discovered. Thus, photometric classification is crucial, but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory's Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multiband light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we augment the simulated training set to be representative of the photometric redshift distribution per SNe class, the cadence of observations, and the flux uncertainty distribution of the test set. Then we build a classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits, yielding a similar performance to the winning PLAsTiCC entry. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is important, with light curves of 150 days yielding the highest performance. Cadence also has an important impact on SNe classification; events with median inter-night gap <3.5 days yield higher classification performance. Interestingly, we find that large gaps (>10 days) in light-curve observations do not impact performance if sufficient observations are available on either side, due to the effectiveness of the Gaussian process interpolation. This analysis is the first exploration of the impact of observing strategy on photometric SN classification with LSST.
| Type: | Article |
|---|---|
| Title: | Considerations for optimizing photometric classification of supernovae from the Rubin Observatory |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/1538-4365/ac3479 |
| Publisher version: | https://doi.org/10.3847/1538-4365/ac3479 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Cosmology; Supernovae; Astronomy software; Open source software; Astronomy data analysis; Classification; Light curve classification |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139965 |
Archive Staff Only
 |
View Item |
