Considerations for optimizing photometric classification of supernovae from the Rubin Observatory

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 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). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multi-band 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 supernovae 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 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 supernova classification with LSST.

arXiv comment18 pages, 13 figures. Changed to match version accepted by the Astrophysical Journal Supplement Series (accepted 28/10/2021). Software publicly available at https://github.com/LSSTDESC/snmachine

References in corpus (37)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Practical Bayesian Optimization of Machine Learning Algorithms
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The Supernova Legacy Survey: Measurement of Omega_M, Omega_Lambda and w from the First Year Data Set
• LSST Science Book, Version 2.0
• First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters
• An Open Catalog for Supernova Data
• Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
• Photometric Supernova Classification With Machine Learning
• Results from the Supernova Photometric Classification Challenge
• Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
• RAPID: Early Classification of Explosive Transients using Deep Learning
• Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
• Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
• Deep Recurrent Neural Networks for Supernovae Classification
• Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
• SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
• Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae
• GPU-acceleration for Large-scale Tree Boosting
• Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-Collapse Supernova Contamination
• PELICAN: deeP architecturE for the LIght Curve ANalysis
• STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets
• The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals
• zBEAMS: A unified solution for supernova cosmology with redshift uncertainties
• Supernova Photometric Classification Challenge
• Nonparametric Transient Classification using Adaptive Wavelets
• Classification of Multiwavelength Transients with Machine Learning
• Extending BEAMS to incorporate correlated systematic uncertainties
• Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey
• Photometric classification of HSC transients using machine learning
• Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae
• LSST Cadence Optimization White Paper in Support of Observations of Unresolved Tidal Stellar Streams in Galaxies beyond the Local Group
• LSST Cadence Optimization White Paper: The Definitive Map of the Galactic bulge

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