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Title: Considerations for Optimizing the Photometric Classification of Supernovae from the Rubin Observatory

Journal Article · · The Astrophysical Journal. Supplement Series
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [1]; ORCiD logo [1]; ORCiD logo [4]
  1. Univ. College London (United Kingdom)
  2. Univ. College London (United Kingdom); Stockholm Univ. (Sweden)
  3. Univ. of the Western Cape, Bellville (South Africa); South African Radio Astronomy Observatory (SARAO) (South Africa); African Institute for Mathematical Sciences (South Africa)
  4. Stockholm Univ. (Sweden)
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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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Organization:
National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
AC02-05CH11231; AC02-76SF00515
OSTI ID:
1983301
Journal Information:
The Astrophysical Journal. Supplement Series, Journal Name: The Astrophysical Journal. Supplement Series Journal Issue: 2 Vol. 258; ISSN 0067-0049
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English

References (44)

Libraries & Recommended Citations for using PLAsTiCC Models dataset January 2021
Analysis of a complex of statistical variables into principal components. journal January 1933
Array programming with NumPy journal September 2020
SciPy 1.0: fundamental algorithms for scientific computing in Python journal February 2020
Astropy: A community Python package for astronomy journal September 2013
PELICAN: deeP architecturE for the LIght Curve ANalysis journal June 2019
The Supernova Legacy Survey: measurement of $\Omega_{\mathsf{M}}$, $\Omega_\mathsf{\Lambda}$ and w from the first year data set journal January 2006
LIII. On lines and planes of closest fit to systems of points in space journal November 1901
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant journal September 1998
Results from the Supernova Photometric Classification Challenge
  • Kessler, Richard; Bassett, Bruce; Belov, Pavel
  • Publications of the Astronomical Society of the Pacific, Vol. 122, Issue 898 https://doi.org/10.1086/657607
journal December 2010
First-Year Sloan Digital sky Survey-Ii Supernova Results: Hubble Diagram and Cosmological Parameters journal October 2009
Extending BEAMS to incorporate correlated systematic uncertainties journal January 2013
zBEAMS: a unified solution for supernova cosmology with redshift uncertainties journal October 2017
RAPID: Early Classification of Explosive Transients Using Deep Learning journal September 2019
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) journal July 2019
Classification of multiwavelength transients with machine learning journal December 2020
Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae journal August 2021
Non-parametric transient classification using adaptive wavelets journal September 2015
STACCATO: a novel solution to supernova photometric classification with biased training sets journal October 2017
Photometric classification of Hyper Suprime-Cam transients using machine learning journal September 2020
Bayesian estimation applied to multiple species journal May 2007
Matplotlib: A 2D Graphics Environment journal January 2007
Fast Direct Methods for Gaussian Processes journal February 2016
The Dark Energy Survey journal June 2005
Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes journal January 2013
machine. journal October 2001
PyWavelets: A Python package for wavelet analysis journal April 2019
Data Structures for Statistical Computing in Python conference January 2010
Photometric Supernova Classification with Machine Learning journal August 2016
The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package journal August 2018
The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals journal October 2019
Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation journal December 2019
An Open Catalog for Supernova Data journal January 2017
Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples journal February 2017
Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination journal June 2017
Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters journal April 2018
LSST: From Science Drivers to Reference Design and Anticipated Data Products journal March 2019
SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae journal December 2020
Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream journal May 2018
Deep Recurrent Neural Networks for Supernovae Classification journal March 2017
Unblinded Data for PLAsTiCC Classification Challenge dataset January 2019
Libraries & Recommended Citations for using PLAsTiCC Models dataset January 2019
pandas-dev/pandas: Pandas software January 2024
Survey Strategy and Cadence Choices for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) text January 2020

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79 ASTRONOMY AND ASTROPHYSICS
Astronomy data analysis
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