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Considerations for optimizing photometric classification of supernovae from the Rubin Observatory
Speaker
Catarina Sampaio Alves University College London (UK)
Abstract
Survey telescopes such as 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 the 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). In this work, we quantitatively analyze the impact of the LSST observing strategy on SNe classification using the simulated multi-band light curves from the Photometric LSST Astronomical Time-Series Classification Challenge. First, we model the light curves with Gaussian processes, and augment the simulated training set to be representative of the test set. Then we build a machine learning classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is an important factor, with light curves of 150 days yielding the highest classification performance. Cadence is also crucial for SNe classification; events with median inter-night gap <3.5 days yield higher performance. This analysis is the first exploration of the impact of observing strategy on photometric supernova classification with LSST.
Date and Time
September 16 2021 4pm KST (= 7am UTC)