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.; Kessler, R.; Lochner, M.; Mahabal, A. A.; Mandel, K. S.; Martínez-Galarza, J. R.; McEwen, J. D.; Muthukrishna, D.; Narayan, G.; Peiris, H.; Peters, C. M.; Ponder, K.; Setzer, C. N.

doi:10.3847/1538-3881/ab3a2f

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

Abstract

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 classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLASTICC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLASTICC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitivemore »« less

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German Centre of Cosmological Lensing, Ruhr-Universität Bochum, Universitätsstraße 150, Bochum, Germany; New York Univ., New York, NY (United States). Center for Cosmology and Particle Physics; New York Univ., New York, NY (United States). Dept. of Physics
Univ. of Toronto, Toronto, ON, Canada. Dept. of Astronomy and Astrophysics; Univ. of Toronto, Toronto, ON, Canada. Dunlap Institute for Astronomy and Astrophysics
University College London, Dorking, United Kingdom. Mullard Space Science Lab, Dept. of Space and Climate Physics
Univ. of Toronto, Toronto, ON, Canada. Dunlap Institute for Astronomy and Astrophysics
Stockholm University, AlbaNova, Stockholm, Sweden. The Oskar Klein Centre for Cosmoparticle Physics, Dept. of Physics
Rutgers, the State University of New Jersey, Piscataway, NJ (United States)
Univ. of Pittsburgh, Pittsburgh, PA (United States); Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada, Spain
Université Clermont Auvergne, CNRS/IN2P3, LPC, F-63000 Clermont-Ferrand, France
Univ. of California, Santa Cruz, Santa Cruz, CA (United States)
KICP, Chicago, IL (United States)
African Institute for Mathematical Sciences, 6 Melrose Road, Muizenberg, 7945, South Africa; South African Radio Astronomy Observatory, The Park, Park Road, Pinelands, Cape Town 7405, South Africa
California Institute of Technology, Pasadena, CA (United States). Div. of Physics, Mathematics, and Astronomy; California Institute of Technology, Pasadena, CA (United States). Center for Data Driven Discovery
Institute of Astronomy and Kavli Institute for Cosmology, Madingley Road, Cambridge, United Kingdom; Univ. of Cambridge, Wilberforce Road, Cambridge, United Kingdom. Statistical Laboratory, DPMMS
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA (United States)
Institute of Astronomy and Kavli Institute for Cosmology, Madingley Road, Cambridge, United Kingdom
Space Telescope Science Institute, Baltimore, MD (United States)
Stockholm University, AlbaNova, Stockholm, Sweden. The Oskar Klein Centre for Cosmoparticle Physics, Dept. of Physics; Department of Physics and Astronomy, University College London, Gower Street, London, United Kingdom
Univ. of California Berkeley, Berkeley, CA (United States). Berkeley Center for Cosmological Physics

Publication Date:: Thu Oct 10 00:00:00 EDT 2019

Research Org.:: Rutgers Univ., Piscataway, NJ (United States); Univ. of California, Oakland, CA (United States); Stanford Univ., CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC); National Science Foundation (NSF); Swedish Research Council (SRC); Ajax Foundation; Marie Sklodowska-Curie; ERC

Contributing Org.:: LSST Dark Energy Science Collaboration; LSST Transients and Variable Stars Science Collaboration

OSTI Identifier:: 1802229

Grant/Contract Number:: SC0011636; AC02-05CH11231; AC02-76SF00515; AST-1517237; Dnr 2016-06012; AST-0909182; AST-1313422; AST-1413600; AST-1518308; 839090; 306478-CosmicDawn

Resource Type:: Accepted Manuscript

Journal Name:: The Astronomical Journal (Online)

Additional Journal Information:: Journal Name: The Astronomical Journal (Online); Journal Volume: 158; Journal Issue: 5; Journal ID: ISSN 1538-3881

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Subject:: 79 ASTRONOMY AND ASTROPHYSICS; astronomy & astrophysics; data analysis; statistical methods; variables; supernovae; surveys; photometric

Citation Formats


                    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., Kessler, R., Lochner, M., Mahabal, A. A., Mandel, K. S., Martínez-Galarza, J. R., McEwen, J. D., Muthukrishna, D., Narayan, G., Peiris, H., Peters, C. M., Ponder, K., and Setzer, C. N. The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals.  United States: N. p., 2019. 
Web.  doi:10.3847/1538-3881/ab3a2f.

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                    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., Kessler, R., Lochner, M., Mahabal, A. A., Mandel, K. S., Martínez-Galarza, J. R., McEwen, J. D., Muthukrishna, D., Narayan, G., Peiris, H., Peters, C. M., Ponder, K., & Setzer, C. N. The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals.  United States.  https://doi.org/10.3847/1538-3881/ab3a2f

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                    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., Kessler, R., Lochner, M., Mahabal, A. A., Mandel, K. S., Martínez-Galarza, J. R., McEwen, J. D., Muthukrishna, D., Narayan, G., Peiris, H., Peters, C. M., Ponder, K., and Setzer, C. N. Thu .  
"The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals".  United States.  https://doi.org/10.3847/1538-3881/ab3a2f.  https://www.osti.gov/servlets/purl/1802229.

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@article{osti_1802229,

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

  author       = {Malz, A. I. and Hložek, R. and Allam, T. and Bahmanyar, A. and Biswas, R. and Dai, M. and Galbany, L. and Ishida, E. E.  O. and Jha, S. W. and Jones, D. O. and Kessler, R. and Lochner, M. and Mahabal, A. A. and Mandel, K. S. and Martínez-Galarza, J. R. and McEwen, J. D. and Muthukrishna, D. and Narayan, G. and Peiris, H. and Peters, C. M. and Ponder, K. and Setzer, C. N.},

  abstractNote = {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 classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLASTICC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLASTICC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLASTICC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products.},

  doi          = {10.3847/1538-3881/ab3a2f},

  journal      = {The Astronomical Journal (Online)},

  number       = 5,

  volume       = 158,

  place        = {United States},

  year         = {Thu Oct 10 00:00:00 EDT 2019},

  month        = {Thu Oct 10 00:00:00 EDT 2019}

}

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

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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,more »« less
https://doi.org/10.3847/1538-4365/ac3479

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Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)

Journal Article Schmidt, S. J. ; Malz, A. I. ; Soo, J. H. ; ... - Monthly Notices of the Royal Astronomical Society

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing twelve photo-z algorithms applied to mock data produced forLarge Synoptic Survey Telescope The Rubin Observatory Legacy Survey of Space and Time (lsst) Dark Energy Science Collaboration (desc). By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs tomore »« less
https://doi.org/10.1093/mnras/staa2799

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Duality between $(2 + 1) d$ quantum critical points

Journal Article Senthil, T. ; Son, Dam Thanh ; Wang, Chong ; ... - Physics Reports

We describe the simulated data sample for the “Photometric LSST Astronomical Time Series Classi-fication Challenge” (PLAsTiCC), a publicly available challenge to classify transient and variable eventsthat will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected tostart in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 tran-sient and variable source models, which were not revealed until after the challenge, and release themodel libraries athttps://doi.org/10.5281/zenodo.2612896. We describe the LSST OperationsSimulator used to predict realisticmore »« less
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https://doi.org/10.1016/j.physrep.2019.09.001

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

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Citation Formats

K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4 journal, December 2015

The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook text, January 2013

Separation of pulsar signals from noise using supervised machine learning algorithms journal, April 2018

Statistical classification techniques for photometric supernova typing: Statistical methods for photometric typing journal, April 2011

Detecting Solar-like Oscillations in Red Giants with Deep Learning journal, May 2018

Kernel PCA for type Ia supernovae photometric classification text, January 2012

Detecting Solar-like Oscillations in Red Giants with Deep Learning text, January 2018

Photometric Supernova Classification With Machine Learning text, January 2016

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning journal, December 2016

K ‐Corrections and Extinction Corrections for Type Ia Supernovae journal, August 2002

Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping journal, July 2014

Validation of the NOAA Space Weather Prediction Center's solar flare forecasting look-up table and forecaster-issued probabilities: VALIDATION OF SWPC FLARE PROBABILITIES journal, June 2012

Observation of a Rapidly Pulsating Radio Source journal, February 1968

Measuring photometric redshifts using galaxy images and Deep Neural Networks journal, July 2016

Results from the Supernova Photometric Classification Challenge journal, December 2010

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework text, January 2015

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook journal, September 2011

zBEAMS: a unified solution for supernova cosmology with redshift uncertainties journal, October 2017

A Hybrid Ensemble Learning Approach to Star-Galaxy Classification text, January 2015

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science text, January 2016

K-corrections and Extinction Corrections for Type Ia Supernovae text, January 2002

Classification and unsupervised clustering of LIGO data with Deep Transfer Learning journal, May 2018

Bayesian High-Redshift Quasar Classification from Optical and Mid-Ir Photometry journal, August 2015

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning text, January 2018

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning text, January 2016

Automated probabilistic classification of transients and variables journal, March 2008

Ensemble modeling of CMEs using the WSA-ENLIL+Cone model text, January 2015

Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era journal, November 2012

Star–galaxy classification using deep convolutional neural networks journal, October 2016

The Third Gravitational Lensing Accuracy Testing (Great3) Challenge Handbook journal, April 2014

Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream text, January 2018

Detecting Quasars in Large-Scale Astronomical Surveys conference, December 2010

Machine-learning selection of optical transients in the Subaru/Hyper Suprime-Cam survey journal, October 2016

Rotation-invariant convolutional neural networks for galaxy morphology prediction journal, April 2015

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning journal, January 2018

Photometric Supernova Classification with Machine Learning journal, August 2016

SiFTO: An Empirical Method for Fitting SN Ia Light Curves journal, July 2008

PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA journal, August 2011

Detection of bars in galaxies using a deep convolutional neural network journal, March 2018

Deep learning classification in asteroseismology journal, May 2017

Approximating Photo- z PDFs for Large Surveys journal, June 2018

K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4 text, January 2015

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY journal, December 2012

Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model journal, May 2015

Unity: Confronting Supernova Cosmology’S Statistical and Systematic Uncertainties in a Unified Bayesian Framework journal, November 2015

Kernel PCA for Type Ia supernovae photometric classification journal, January 2013

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science journal, February 2017

Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era text, January 2011

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters journal, April 2018

Results from the Supernova Photometric Classification Challenge text, January 2010

Matplotlib: A 2D Graphics Environment journal, January 2007

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection journal, February 2017

Python for Scientific Computing journal, January 2007

Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey text, January 2016

The NumPy Array: A Structure for Efficient Numerical Computation journal, March 2011

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data journal, February 2018

Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream journal, May 2018

Radio Galaxy Zoo: Claran – a deep learning classifier for radio morphologies journal, October 2018

Measuring photometric redshifts using galaxy images and Deep Neural Networks preprint, January 2015

A hybrid ensemble learning approach to star–galaxy classification journal, August 2015

Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation text, January 2019

Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation journal, December 2019

K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4
journal, December 2015

The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook
text, January 2013

Separation of pulsar signals from noise using supervised machine learning algorithms
journal, April 2018

Statistical classification techniques for photometric supernova typing: Statistical methods for photometric typing
journal, April 2011

Detecting Solar-like Oscillations in Red Giants with Deep Learning
journal, May 2018

Kernel PCA for type Ia supernovae photometric classification
text, January 2012

Detecting Solar-like Oscillations in Red Giants with Deep Learning
text, January 2018

Photometric Supernova Classification With Machine Learning
text, January 2016

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning
journal, December 2016

K ‐Corrections and Extinction Corrections for Type Ia Supernovae
journal, August 2002

Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping
journal, July 2014

Validation of the NOAA Space Weather Prediction Center's solar flare forecasting look-up table and forecaster-issued probabilities: VALIDATION OF SWPC FLARE PROBABILITIES
journal, June 2012

Observation of a Rapidly Pulsating Radio Source
journal, February 1968

Measuring photometric redshifts using galaxy images and Deep Neural Networks
journal, July 2016

Results from the Supernova Photometric Classification Challenge
journal, December 2010

UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework
text, January 2015

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook
journal, September 2011

zBEAMS: a unified solution for supernova cosmology with redshift uncertainties
journal, October 2017

A Hybrid Ensemble Learning Approach to Star-Galaxy Classification
text, January 2015

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
text, January 2016

K-corrections and Extinction Corrections for Type Ia Supernovae
text, January 2002

Classification and unsupervised clustering of LIGO data with Deep Transfer Learning
journal, May 2018

Bayesian High-Redshift Quasar Classification from Optical and Mid-Ir Photometry
journal, August 2015

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
text, January 2018

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning
text, January 2016

Automated probabilistic classification of transients and variables
journal, March 2008

Ensemble modeling of CMEs using the WSA-ENLIL+Cone model
text, January 2015

Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
journal, November 2012

Star–galaxy classification using deep convolutional neural networks
journal, October 2016

The Third Gravitational Lensing Accuracy Testing (Great3) Challenge Handbook
journal, April 2014

Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
text, January 2018

Detecting Quasars in Large-Scale Astronomical Surveys
conference, December 2010

Machine-learning selection of optical transients in the Subaru/Hyper Suprime-Cam survey
journal, October 2016

Rotation-invariant convolutional neural networks for galaxy morphology prediction
journal, April 2015

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
journal, January 2018

Photometric Supernova Classification with Machine Learning
journal, August 2016

SiFTO: An Empirical Method for Fitting SN Ia Light Curves
journal, July 2008

PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA
journal, August 2011

Detection of bars in galaxies using a deep convolutional neural network
journal, March 2018

Deep learning classification in asteroseismology
journal, May 2017

Approximating Photo- z PDFs for Large Surveys
journal, June 2018

K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
text, January 2015

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY
journal, December 2012

Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model
journal, May 2015

Unity: Confronting Supernova Cosmology’S Statistical and Systematic Uncertainties in a Unified Bayesian Framework
journal, November 2015

Kernel PCA for Type Ia supernovae photometric classification
journal, January 2013

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science
journal, February 2017

Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
text, January 2011

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
journal, April 2018

Results from the Supernova Photometric Classification Challenge
text, January 2010

Matplotlib: A 2D Graphics Environment
journal, January 2007

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
journal, February 2017

Python for Scientific Computing
journal, January 2007

Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
text, January 2016

The NumPy Array: A Structure for Efficient Numerical Computation
journal, March 2011

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data
journal, February 2018

Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
journal, May 2018

Radio Galaxy Zoo: Claran – a deep learning classifier for radio morphologies
journal, October 2018

Measuring photometric redshifts using galaxy images and Deep Neural Networks
preprint, January 2015

A hybrid ensemble learning approach to star–galaxy classification
journal, August 2015

Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
text, January 2019

Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
journal, December 2019