Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
Abstract
Upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will rely on photometric classification to identify the majority of the transients and variables that they discover. We present a set of techniques for photometric classification that can be applied even when the training set of spectroscopically confirmed objects is heavily biased toward bright, low-redshift objects. Using Gaussian process regression to model arbitrary light curves in all bands simultaneously, we "augment" the training set by generating new versions of the original light curves covering a range of redshifts and observing conditions. We train a boosted decision tree classifier on features extracted from the augmented light curves, and we show how such a classifier can be designed to produce classifications that are independent of the redshift distributions of objects in the training sample. Our classification algorithm was the best-performing among the 1094 models considered in the blinded phase of the Photometric LSST Astronomical Time-Series Classification Challenge, scoring 0.468 on the organizers' logarithmic-loss metric with flat weights for all object classes in the training set, and achieving an AUC of 0.957 for classification of SNe Ia. Furthermore, our results suggest that spectroscopic campaigns used for training photometric classifiers should focus onmore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Univ. of Washington, Seattle, WA (United States)
- Publication Date:
- Research Org.:
- Univ. of Chicago, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1593890
- Grant/Contract Number:
- SC0009924
- Resource Type:
- Accepted Manuscript
- Journal Name:
- The Astronomical Journal (Online)
- Additional Journal Information:
- Journal Name: The Astronomical Journal (Online); Journal Volume: 158; Journal Issue: 6; Journal ID: ISSN 1538-3881
- Publisher:
- IOP Publishing - AAAS
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; photometric classification; LSST; transients; supernovae
Citation Formats
Boone, Kyle. Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation. United States: N. p., 2019.
Web. doi:10.3847/1538-3881/ab5182.
Boone, Kyle. Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation. United States. https://doi.org/10.3847/1538-3881/ab5182
Boone, Kyle. Wed .
"Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation". United States. https://doi.org/10.3847/1538-3881/ab5182. https://www.osti.gov/servlets/purl/1593890.
@article{osti_1593890,
title = {Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation},
author = {Boone, Kyle},
abstractNote = {Upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will rely on photometric classification to identify the majority of the transients and variables that they discover. We present a set of techniques for photometric classification that can be applied even when the training set of spectroscopically confirmed objects is heavily biased toward bright, low-redshift objects. Using Gaussian process regression to model arbitrary light curves in all bands simultaneously, we "augment" the training set by generating new versions of the original light curves covering a range of redshifts and observing conditions. We train a boosted decision tree classifier on features extracted from the augmented light curves, and we show how such a classifier can be designed to produce classifications that are independent of the redshift distributions of objects in the training sample. Our classification algorithm was the best-performing among the 1094 models considered in the blinded phase of the Photometric LSST Astronomical Time-Series Classification Challenge, scoring 0.468 on the organizers' logarithmic-loss metric with flat weights for all object classes in the training set, and achieving an AUC of 0.957 for classification of SNe Ia. Furthermore, our results suggest that spectroscopic campaigns used for training photometric classifiers should focus on typing large numbers of well-observed, intermediate-redshift transients, instead of attempting to type a sample of transients that is directly representative of the full data set being classified.},
doi = {10.3847/1538-3881/ab5182},
journal = {The Astronomical Journal (Online)},
number = 6,
volume = 158,
place = {United States},
year = {2019},
month = {12}
}
Web of Science
Works referenced in this record:
THE HUBBLE SPACE TELESCOPE CLUSTER SUPERNOVA SURVEY. V. IMPROVING THE DARK-ENERGY CONSTRAINTS ABOVE z > 1 AND BUILDING AN EARLY-TYPE-HOSTED SUPERNOVA SAMPLE
journal, January 2012
- Suzuki, N.; Rubin, D.; Lidman, C.
- The Astrophysical Journal, Vol. 746, Issue 1
How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
journal, August 2007
- Bailey, S.; Aragon, C.; Romano, R.
- The Astrophysical Journal, Vol. 665, Issue 2
The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package
journal, August 2018
- Price-Whelan, A. M.; Sipőcz, B. M.; Günther, H. M.
- The Astronomical Journal, Vol. 156, Issue 3
Measures of location and scale for velocities in clusters of galaxies - A robust approach
journal, July 1990
- Beers, Timothy C.; Flynn, Kevin; Gebhardt, Karl
- The Astronomical Journal, Vol. 100
HUBBLE RESIDUALS OF NEARBY TYPE Ia SUPERNOVAE ARE CORRELATED WITH HOST GALAXY MASSES
journal, May 2010
- Kelly, Patrick L.; Hicken, Malcolm; Burke, David L.
- The Astrophysical Journal, Vol. 715, Issue 2
Results from the Supernova Photometric Classification Challenge
journal, December 2010
- Kessler, Richard; Bassett, Bruce; Belov, Pavel
- Publications of the Astronomical Society of the Pacific, Vol. 122, Issue 898
The Calculation of Posterior Distributions by Data Augmentation
journal, June 1987
- Tanner, Martin A.; Wong, Wing Hung
- Journal of the American Statistical Association, Vol. 82, Issue 398
SNEMO: Improved Empirical Models for Type Ia Supernovae
journal, December 2018
- Saunders, C.; Aldering, G.; Antilogus, P.
- The Astrophysical Journal, Vol. 869, Issue 2
Bayesian Single-Epoch Photometric Classification of Supernovae
journal, July 2007
- Poznanski, Dovi; Maoz, Dan; Gal-Yam, Avishay
- The Astronomical Journal, Vol. 134, Issue 3
SNANA: A Public Software Package for Supernova Analysis
journal, September 2009
- Kessler, Richard; Bernstein, Joseph P.; Cinabro, David
- Publications of the Astronomical Society of the Pacific, Vol. 121, Issue 883
Improved Cosmological Constraints from New, Old, and Combined Supernova Data Sets
journal, October 2008
- Kowalski, M.; Rubin, D.; Aldering, G.
- The Astrophysical Journal, Vol. 686, Issue 2
The Pan-STARRS wide-field optical/NIR imaging survey
conference, July 2010
- Kaiser, Nick; Burgett, William; Chambers, Ken
- SPIE Astronomical Telescopes + Instrumentation, SPIE Proceedings
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
journal, September 1998
- Riess, Adam G.; Filippenko, Alexei V.; Challis, Peter
- The Astronomical Journal, Vol. 116, Issue 3
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
journal, July 2019
- Kessler, R.; Narayan, G.; Avelino, A.
- Publications of the Astronomical Society of the Pacific, Vol. 131, Issue 1003
Energy Distributions, K Corrections, and the Stebbins-Whitford Effect for Giant Elliptical Galaxies
journal, October 1968
- Oke, J. B.; Sandage, Allan
- The Astrophysical Journal, Vol. 154
Understanding the Lomb–Scargle Periodogram
journal, May 2018
- VanderPlas, Jacob T.
- The Astrophysical Journal Supplement Series, Vol. 236, Issue 1
Photometric Supernova Cosmology with Beams and Sdss-Ii
journal, May 2012
- Hlozek, Renée; Kunz, Martin; Bassett, Bruce
- The Astrophysical Journal, Vol. 752, Issue 2
STANDARDIZING TYPE Ia SUPERNOVA ABSOLUTE MAGNITUDES USING GAUSSIAN PROCESS DATA REGRESSION
journal, March 2013
- Kim, A. G.; Thomas, R. C.; Aldering, G.
- The Astrophysical Journal, Vol. 766, Issue 2
Improving Cosmological Distance Measurements Using twin type ia Supernovae
journal, December 2015
- Fakhouri, H. K.; Boone, K.; Aldering, G.
- The Astrophysical Journal, Vol. 815, Issue 1
The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey
journal, April 2014
- Okumura, Jun E.; Ihara, Yutaka; Doi, Mamoru
- Publications of the Astronomical Society of Japan, Vol. 66, Issue 2
The Art of Data Augmentation
journal, March 2001
- van Dyk, David A.; Meng, Xiao-Li
- Journal of Computational and Graphical Statistics, Vol. 10, Issue 1
Astropy: A community Python package for astronomy
journal, September 2013
- Robitaille, Thomas P.; Tollerud, Erik J.; Greenfield, Perry
- Astronomy & Astrophysics, Vol. 558
TYPE Ia SUPERNOVA RATE MEASUREMENTS TO REDSHIFT 2.5 FROM CANDELS: SEARCHING FOR PROMPT EXPLOSIONS IN THE EARLY UNIVERSE
journal, June 2014
- Rodney, Steven A.; Riess, Adam G.; Strolger, Louis-Gregory
- The Astronomical Journal, Vol. 148, Issue 1
The Supernova Legacy Survey: measurement of $\Omega_{\mathsf{M}}$, $\Omega_\mathsf{\Lambda}$ and w from the first year data set
journal, January 2006
- Astier, P.; Guy, J.; Regnault, N.
- Astronomy & Astrophysics, Vol. 447, Issue 1
Evidence of environmental dependencies of Type Ia supernovae from the Nearby Supernova Factory indicated by local H α
journal, December 2013
- Rigault, M.; Copin, Y.; Aldering, G.
- Astronomy & Astrophysics, Vol. 560
Photometric Supernova Classification with Machine Learning
journal, August 2016
- Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.
- The Astrophysical Journal Supplement Series, Vol. 225, Issue 2
PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA
journal, August 2011
- Sako, Masao; Bassett, Bruce; Connolly, Brian
- The Astrophysical Journal, Vol. 738, Issue 2
The Difference Imaging Pipeline for the Transient Search in the dark Energy Survey
journal, November 2015
- Kessler, R.; Marriner, J.; Childress, M.
- The Astronomical Journal, Vol. 150, Issue 6
Type Ia Supernova Discoveries at z > 1 from the Hubble Space Telescope : Evidence for Past Deceleration and Constraints on Dark Energy Evolution
journal, June 2004
- Riess, Adam G.; Strolger, Louis‐Gregory; Tonry, John
- The Astrophysical Journal, Vol. 607, Issue 2
The rate of core Collapse Supernovae to Redshift 2.5 from the Candels and Clash Supernova Surveys
journal, November 2015
- Strolger, Louis-Gregory; Dahlen, Tomas; Rodney, Steven A.
- The Astrophysical Journal, Vol. 813, Issue 2
The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals
journal, October 2019
- Malz, A. I.; Hložek, R.; Allam, T.
- The Astronomical Journal, Vol. 158, Issue 5
PELICAN: deeP architecturE for the LIght Curve ANalysis
journal, June 2019
- Pasquet, Johanna; Pasquet, Jérôme; Chaumont, Marc
- Astronomy & Astrophysics, Vol. 627
Unity: Confronting Supernova Cosmology’S Statistical and Systematic Uncertainties in a Unified Bayesian Framework
journal, November 2015
- Rubin, D.; Aldering, G.; Barbary, K.
- The Astrophysical Journal, Vol. 813, Issue 2
Bayesian estimation applied to multiple species
journal, May 2007
- Kunz, Martin; Bassett, Bruce A.; Hlozek, Renée A.
- Physical Review D, Vol. 75, Issue 10
Kernel PCA for Type Ia supernovae photometric classification
journal, January 2013
- Ishida, E. E. O.; de Souza, R. S.
- Monthly Notices of the Royal Astronomical Society, Vol. 430, Issue 1
Fast Direct Methods for Gaussian Processes
journal, February 2016
- Ambikasaran, Sivaram; Foreman-Mackey, Daniel; Greengard, Leslie
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 2
A simple and robust method for automated photometric classification of supernovae using neural networks
journal, December 2012
- Karpenka, N. V.; Feroz, F.; Hobson, M. P.
- Monthly Notices of the Royal Astronomical Society, Vol. 429, Issue 2
SALT2: using distant supernovae to improve the use of type Ia supernovae as distance indicators
journal, February 2007
- Guy, J.; Astier, P.; Baumont, S.
- Astronomy & Astrophysics, Vol. 466, Issue 1
STACCATO: a novel solution to supernova photometric classification with biased training sets
journal, October 2017
- Revsbech, E. A.; Trotta, R.; van Dyk, D. A.
- Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 3
Supernova Simulations and Strategies for the dark Energy Survey
journal, June 2012
- Bernstein, J. P.; Kessler, R.; Kuhlmann, S.
- The Astrophysical Journal, Vol. 753, Issue 2
Matplotlib: A 2D Graphics Environment
journal, January 2007
- Hunter, John D.
- Computing in Science & Engineering, Vol. 9, Issue 3
New Constraints on Ω M , Ω Λ , and w from an Independent Set of 11 High‐Redshift Supernovae Observed with the Hubble Space Telescope
journal, November 2003
- Knop, R. A.; Aldering, G.; Amanullah, R.
- The Astrophysical Journal, Vol. 598, Issue 1
Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
journal, November 2018
- Ishida, E. E. O.; Beck, R.; González-Gaitán, S.
- Monthly Notices of the Royal Astronomical Society, Vol. 483, Issue 1
The Complete Light-curve Sample of Spectroscopically Confirmed SNe Ia from Pan-STARRS1 and Cosmological Constraints from the Combined Pantheon Sample
journal, May 2018
- Scolnic, D. M.; Jones, D. O.; Rest, A.
- The Astrophysical Journal, Vol. 859, Issue 2
Semi-supervised learning for photometric supernova classification★: Semi-supervised SN classification
journal, October 2011
- Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.
- Monthly Notices of the Royal Astronomical Society, Vol. 419, Issue 2
The LSST operations simulator
conference, August 2014
- Delgado, Francisco; Saha, Abhijit; Chandrasekharan, Srinivasan
- SPIE Astronomical Telescopes + Instrumentation, SPIE Proceedings
Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination
journal, June 2017
- Jones, D. O.; Scolnic, D. M.; Riess, A. G.
- The Astrophysical Journal, Vol. 843, Issue 1
Measurements of Ω and Λ from 42 High‐Redshift Supernovae
journal, June 1999
- Perlmutter, S.; Aldering, G.; Goldhaber, G.
- The Astrophysical Journal, Vol. 517, Issue 2
The NumPy Array: A Structure for Efficient Numerical Computation
journal, March 2011
- van der Walt, Stéfan; Colbert, S. Chris; Varoquaux, Gaël
- Computing in Science & Engineering, Vol. 13, Issue 2
Deep Recurrent Neural Networks for Supernovae Classification
journal, March 2017
- Charnock, Tom; Moss, Adam
- The Astrophysical Journal, Vol. 837, Issue 2
Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
journal, August 2014
- Betoule, M.; Kessler, R.; Guy, J.
- Astronomy & Astrophysics, Vol. 568
A survey on Image Data Augmentation for Deep Learning
journal, July 2019
- Shorten, Connor; Khoshgoftaar, Taghi M.
- Journal of Big Data, Vol. 6, Issue 1
CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy
journal, February 2021
- George, Blesson; Assaiya, Anshul; Roy, Robin J.
- Communications Biology, Vol. 4, Issue 1
Interpolation of Spatial Data
journal, September 2018
- Aliyev, Rae Zh
- Biomedical Journal of Scientific & Technical Research, Vol. 9, Issue 1
Improving Cosmological Distance Measurements Using Twin Type IA Supernovae
text, January 2015
- Fakhouri, H. K.; Boone, K.; Aldering, G.
- Deutsches Elektronen-Synchrotron, DESY, Hamburg
SNEMO: Improved Empirical Models for Type Ia Supernovae
text, January 2018
- Saunders, Claire; Aldering, G.; Antilogus, P.
- Deutsches Elektronen-Synchrotron, DESY, Hamburg
The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals
text, January 2019
- Malz, Ai; Hlozek, R.; T., Jr, Allam
- Apollo - University of Cambridge Repository
Models and simulations for the photometric lsst astronomical time series classification challenge (Plasticc)
text, January 2019
- Kessler, R.; Narayan, G.; Avelino, A.
- Apollo - University of Cambridge Repository
Unblinded Data for PLAsTiCC Classification Challenge
dataset, January 2019
- Team, Plasticc; Modelers, Plasticc
- Zenodo
How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
text, January 2007
- Bailey, S.; Aragon, C.; Romano, R.
- arXiv
Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
text, January 2008
- Kowalski, M.; Rubin, D.; Aldering, G.
- arXiv
Hubble Residuals of Nearby Type Ia Supernovae Are Correlated with Host Galaxy Masses
text, January 2009
- Kelly, Patrick L.; Hicken, Malcolm; Burke, David L.
- arXiv
Results from the Supernova Photometric Classification Challenge
text, January 2010
- Kessler, Richard; Bassett, Bruce; Belov, Pavel
- arXiv
Supernova Simulations and Strategies For the Dark Energy Survey
text, January 2011
- Bernstein, J. P.; Kessler, R.; Kuhlmann, S.
- arXiv
Photometric Supernova Cosmology with BEAMS and SDSS-II
text, January 2011
- Hlozek, Renée; Kunz, Martin; Bassett, Bruce
- arXiv
Kernel PCA for type Ia supernovae photometric classification
text, January 2012
- Ishida, Emille E. O.; de Souza, Rafael S.
- arXiv
Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression
text, January 2013
- Kim, A. G.; Thomas, R. C.; Aldering, G.
- arXiv
The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey
text, January 2014
- Okumura, Jun E.; Ihara, Yutaka; Doi, Mamoru
- arXiv
Fast Direct Methods for Gaussian Processes
preprint, January 2014
- Ambikasaran, Sivaram; Foreman-Mackey, Daniel; Greengard, Leslie
- arXiv
UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework
text, January 2015
- Rubin, David; Aldering, Greg; Barbary, Kyle
- arXiv
The Rate of Core Collapse Supernovae to Redshift 2.5 From The CANDELS and CLASH Supernova Surveys
text, January 2015
- Strolger, Louis-Gregory; Dahlen, Tomas; Rodney, Steven A.
- arXiv
Improving Cosmological Distance Measurements Using Twin Type Ia Supernovae
text, January 2015
- Fakhouri, H. K.; Boone, K.; Aldering, G.
- arXiv
Photometric Supernova Classification With Machine Learning
text, January 2016
- Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.
- arXiv
Deep Recurrent Neural Networks for Supernovae Classification
text, January 2016
- Charnock, Tom; Moss, Adam
- arXiv
Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
text, January 2018
- Ishida, E. E. O.; Beck, R.; Gonzalez-Gaitan, S.
- arXiv
SNEMO: Improved Empirical Models for Type Ia Supernovae
text, January 2018
- Saunders, C.; Aldering, G.; Antilogus, P.
- arXiv
Bayesian Single-Epoch Photometric Classification of Supernovae
text, January 2006
- Poznanski, Dovi; Maoz, Dan; Gal-Yam, Avishay
- arXiv