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Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Journal Article · · The Astrophysical Journal. Supplement Series
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Abstract

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Organization:
LSST Dark Energy Science Collaboration
Grant/Contract Number:
AC02-05CH11231; AC02-76SF00515; SC0011636
OSTI ID:
1991984
Alternate ID(s):
OSTI ID: 2007180
OSTI ID: 1999242
Journal Information:
The Astrophysical Journal. Supplement Series, Journal Name: The Astrophysical Journal. Supplement Series Journal Issue: 2 Vol. 267; ISSN 0067-0049
Publisher:
American Astronomical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (34)

Geostatistics book January 2012
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position journal April 1980
Photometric data-driven classification of Type Ia supernovae in the open Supernova Catalog journal April 2021
Deep learning in neural networks: An overview journal January 2015
Learning representations by back-propagating errors journal October 1986
Photometric selection of Type Ia supernovae in the Supernova Legacy Survey journal September 2011
Active anomaly detection for time-domain discoveries journal June 2021
SALT2: using distant supernovae to improve the use of type Ia supernovae as distance indicators journal February 2007
SNANA: A Public Software Package for Supernova Analysis
  • Kessler, Richard; Bernstein, Joseph P.; Cinabro, David
  • Publications of the Astronomical Society of the Pacific, Vol. 121, Issue 883 https://doi.org/10.1086/605984
journal September 2009
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
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) journal July 2019
Imbalance learning for variable star classification journal March 2020
The Foundation Supernova Survey: motivation, design, implementation, and first data release journal December 2017
First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases journal February 2019
Photometric Light Curves Classification with Machine Learning journal March 2020
Hard or Soft Classification? Large-Margin Unified Machines journal March 2011
machine. journal October 2001
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets journal March 2015
4MOST: Project overview and information for the First Call for Proposals text January 2019
4MOST Consortium Survey 10: The Time-Domain Extragalactic Survey (TiDES) text January 2019
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms journal December 1963
Photometric Supernova Classification with Machine Learning journal August 2016
Approximating Photo- z PDFs for Large Surveys journal June 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
A Search for Kilonovae in the Dark Energy Survey journal March 2017
LSST: From Science Drivers to Reference Design and Anticipated Data Products journal March 2019
A Classification Algorithm for Time-domain Novelties in Preparation for LSST Alerts. Application to Variable Stars and Transients Detected with DECam in the Galactic Bulge journal April 2020
Real-time, Value-driven Data Augmentation in the Era of LSST journal April 2020
Constraints on the Physical Properties of GW190814 through Simulations Based on DECam Follow-up Observations by the Dark Energy Survey journal September 2020
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
Enabling Catalog Simulations of Transient and Variable Sources Based on LSST Cadence Strategies journal April 2020
Libraries & Recommended Citations for using PLAsTiCC Models dataset January 2019

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