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---
description: 'The Photometric LSST Astronomical Time-Series Classification Challenge
(PLAsTiCC) is a community-wide challenge to spur development of algorithms to classify
astronomical transients. The Large Synoptic Survey Telescope (LSST) will discover
tens of thousands of transient phenomena every single night. To deal with this massive
onset of data, automated algorithms to classify and sort astronomical transients
are crucial.
PLAsTiCC, based on the highly successful Supernova Photometric Classification Challenge,
will consist of a set of realistic LSST simulations of a variety of transient and
variable phenomena. The challenge will be publicly available on a popular data science
platform, encouraging algorithm submissions from outside the Astronomy community.
'
homepage: https://zenodo.org/records/2539456
version: 0.0.1
citation: "@article{Kessler_2019,\n title={Models and Simulations for the Photometric\
\ LSST Astronomical Time Series Classification Challenge (PLAsTiCC)},\n volume={131},\n\
\ ISSN={1538-3873},\n url={http://dx.doi.org/10.1088/1538-3873/ab26f1},\n \
\ DOI={10.1088/1538-3873/ab26f1},\n number={1003},\n journal={Publications of\
\ the Astronomical Society of the Pacific},\n publisher={IOP Publishing},\n \
\ author={Kessler, R. and Narayan, G. and Avelino, A. and Bachelet, E. and Biswas,\
\ R. and Brown, P. J. and Chernoff, D. F. and Connolly, A. J. and Dai, M. and Daniel,\
\ S. and Stefano, R. Di and Drout, M. R. and Galbany, L. and González-Gaitán, S.\
\ and Graham, M. L. and Hložek, R. and Ishida, E. E. O. and Guillochon, J. and Jha,\
\ S. W. and Jones, D. O. and Mandel, K. S. and Muthukrishna, D. and O’Grady, A.\
\ and Peters, C. M. and Pierel, J. R. and Ponder, K. A. and Prša, A. and Rodney,\
\ S. and Villar, V. A.},\n year={2019},\n month=jul, pages={094501} }\n"
dataset_info:
config_name: train_only
features:
- name: lightcurve
sequence:
- name: band
dtype: string
- name: flux
dtype: float32
- name: flux_err
dtype: float32
- name: time
dtype: float32
- name: hostgal_photoz
dtype: float32
- name: hostgal_specz
dtype: float32
- name: redshift
dtype: float32
- name: obj_type
dtype: string
- name: object_id
dtype: string
splits:
- name: train
num_bytes: 20200662
num_examples: 1000
download_size: 2087385
dataset_size: 20200662
configs:
- config_name: train_only
data_files:
- split: train
path: train_only/train-*
---
# Plasticc Dataset
CC BY 4.0
The Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) is a community-wide challenge to spur development of algorithms to classify astronomical transients. The Large Synoptic Survey Telescope (LSST) will discover tens of thousands of transient phenomena every single night. To deal with this massive onset of data, automated algorithms to classify and sort astronomical transients are crucial.
PLAsTiCC, based on the highly successful Supernova Photometric Classification Challenge, will consist of a set of realistic LSST simulations of a variety of transient and variable phenomena. The challenge will be publicly available on a popular data science platform, encouraging algorithm submissions from outside the Astronomy community.
@article{Kessler_2019,
title={Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)},
volume={131},
ISSN={1538-3873},
url={http://dx.doi.org/10.1088/1538-3873/ab26f1},
DOI={10.1088/1538-3873/ab26f1},
number={1003},
journal={Publications of the Astronomical Society of the Pacific},
publisher={IOP Publishing},
author={Kessler, R. and Narayan, G. and Avelino, A. and Bachelet, E. and Biswas, R. and Brown, P. J. and Chernoff, D. F. and Connolly, A. J. and Dai, M. and Daniel, S. and Stefano, R. Di and Drout, M. R. and Galbany, L. and González-Gaitán, S. and Graham, M. L. and Hložek, R. and Ishida, E. E. O. and Guillochon, J. and Jha, S. W. and Jones, D. O. and Mandel, K. S. and Muthukrishna, D. and O’Grady, A. and Peters, C. M. and Pierel, J. R. and Ponder, K. A. and Prša, A. and Rodney, S. and Villar, V. A.},
year={2019},
month=jul, pages={094501} }
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