The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set
Abstract
The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022. LSST will revolutionize our understanding of the changing sky, discovering and measuring millions of time-varying objects. In this challenge, we pose the question: how well can we classify objects in the sky that vary in brightness from simulated LSST time-series data, with all its challenges of non-representativity? In this note we explain the need for a data challenge to help classify such astronomical sources and describe the PLAsTiCC data set and Kaggle data challenge, noting that while the references are provided for context, they are not needed to participate in the challenge.
Keywords
Cite
@article{arxiv.1810.00001,
title = {The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set},
author = {The PLAsTiCC team and Tarek Allam and Anita Bahmanyar and Rahul Biswas and Mi Dai and Lluís Galbany and Renée Hložek and Emille E. O. Ishida and Saurabh W. Jha and David O. Jones and Richard Kessler and Michelle Lochner and Ashish A. Mahabal and Alex I. Malz and Kaisey S. Mandel and Juan Rafael Martínez-Galarza and Jason D. McEwen and Daniel Muthukrishna and Gautham Narayan and Hiranya Peiris and Christina M. Peters and Kara Ponder and Christian N. Setzer and The LSST Dark Energy Science Collaboration and The LSST Transients and Variable Stars Science Collaboration},
journal= {arXiv preprint arXiv:1810.00001},
year = {2018}
}
Comments
Research note to accompany the https://www.kaggle.com/c/PLAsTiCC-2018 challenge