publication . Article . Other literature type . 2019

The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

Malz, A. I.; Hložek, R.; Allam, T.; Bahmanyar, A.; Biswas, R.; Dai, M.; Galbany, L.; Ishida, E. E. O.; Jha, S. W.; Jones, D. O.; ...
Open Access English
  • Published: 10 Oct 2019
  • Publisher: HAL CCSD
  • Country: France
International audience; Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting cl...
Persistent Identifiers
free text keywords: [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Space and Planetary Science, Astronomy and Astrophysics, Light curve, Data mining, computer.software_genre, computer, Methods statistical, Performance metric, Sextant (astronomical), law.invention, law, Physics, Time series classification, Photometry (optics)
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