Abstract
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.
Highlights
The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST; LSST Science Collaboration et al 2009; Collaboration et al 2017; Ivezić et al 2019) is expected to discover, during its 10 yr duration, at least one order-ofmagnitude more supernovae (SNe) than the currently available SNe samples (Guillochon et al 2017)
SNe that are used in astrophysical and cosmological studies need to be spectroscopically classified (e.g., Riess et al 1998; Astier et al 2006; Kessler et al 2009). This will be impossible for most events detected by LSST due to the limited spectroscopic resources; LSST will rely on photometric classification, using the events that will be spectroscopically classified as its training set
We study classification performance for SNe with different properties within the single simulated observing strategy that is available in PLAsTiCC
Summary
The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST; LSST Science Collaboration et al 2009; Collaboration et al 2017; Ivezić et al 2019) is expected to discover, during its 10 yr duration, at least one order-ofmagnitude more supernovae (SNe) than the currently available SNe samples (Guillochon et al 2017). We make several other improvements to deal with the greater realism of the PLAsTiCC data, including training set augmentation Using this improved classifier, we study the performance of photometric SNe classification for subsets of light curves with different cadence properties, using the single observing strategy simulated for the PLAsTiCC challenge. We study the performance of photometric SNe classification for subsets of light curves with different cadence properties, using the single observing strategy simulated for the PLAsTiCC challenge We note that this approach is different from studying the classification performance for different observing strategies with fixed total exposure time, where a reduced season length could be compensated for with a higher cadence.
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