tdescore: An Accurate Photometric Classifier for Tidal Disruption Events
Creators
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1.
California Institute of Technology
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2.
Deutsches Elektronen-Synchrotron DESY
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3.
Humboldt-Universität zu Berlin
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4.
Space Telescope Science Institute
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5.
Johns Hopkins University
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6.
University of Maryland, College Park
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7.
Goddard Space Flight Center
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8.
Queen's University Belfast
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9.
Stockholm University
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10.
Leiden University
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11.
New York State Office for People With Developmental Disabilities
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12.
University of California, Berkeley
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13.
Infrared Processing and Analysis Center
Abstract
Optical surveys have become increasingly adept at identifying candidate tidal disruption events (TDEs) in large numbers, but classifying these generally requires extensive spectroscopic resources. Here we present tdescore, a simple binary photometric classifier that is trained using a systematic census of ∼3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing ∼2% of the total. tdescore is nonetheless able to reject non-TDEs with 99.6% accuracy, yielding a sample of probable TDEs with recall of 77.5% for a precision of 80.2%. tdescore is thus substantially better than any available TDE photometric classifier scheme in the literature, with performance not far from spectroscopy as a method for classifying ZTF nuclear transients, despite relying solely on ZTF data and multiwavelength catalog cross matching. In a novel extension, we use "Shapley additive explanations" to provide a human-readable justification for each individual tdescore classification, enabling users to understand and form opinions about the underlying classifier reasoning. tdescore can serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory.
Acknowledgement
We thank Adam Stein, Ludwig Rauch, and Niharika Sravan for fruitful discussions about machine-learning classification.
R.S. and M.M.K acknowledge support from grants by the National Science Foundation (AST 2206730) and the David and Lucille Packard Foundation (PI Kasliwal). M.N. is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 948381) and by UK Space Agency grant No. ST/Y000692/1. E.K.H. acknowledges support by NASA under award number 80GSFC21M0002. S.J.N. is supported by the US National Science Foundation (NSF) through grant AST-2108402.
Based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under grants Nos. AST-1440341 and AST-2034437 and a collaboration including current partners Caltech, IPAC, the Weizmann Institute of Science, the Oskar Klein Center at Stockholm University, the University of Maryland, Deutsches Elektronen-Synchrotron and Humboldt University, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, Trinity College Dublin, Lawrence Livermore National Laboratories, IN2P3, University of Warwick, Ruhr University Bochum, Northwestern University and former partners the University of Washington, Los Alamos National Laboratories, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW. SED Machine is based upon work supported by the National Science Foundation under grant No. 1106171. The Gordon and Betty Moore Foundation, through both the Data-Driven Investigator Program and a dedicated grant, provided critical funding for SkyPortal.
Facilities
PO:1.2m - Palomar Observatory's 1.2 meter Samuel Oschin Telescope (ZTF; Bellm et al. 2019), PO:1.5m (SEDm; Blagorodnova et al. 2018; Rigault et al. 2019; Kim et al. 2022)
Software References
AMPEL (Nordin et al. 2019), astropy (Astropy Collaboration et al. 2022), astroquery (Ginsburg et al. 2019), numpy (Harris et al. 2020), pandas (Wes McKinney 2010), scikit-learn (Pedregosa et al. 2011), scipy (Virtanen et al. 2020), SHAP (Lundberg & Lee 2017), sncosmo (Barbary et al. 2016), tdescore (Stein 2024), XGBoost (Chen & Guestrin 2016)
Files
Stein_2024_ApJL_965_L14.pdf
Files
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Additional details
Identifiers
- ISSN
- 2041-8213
Funding
- National Science Foundation
- AST-2206730
- David and Lucile Packard Foundation
- European Research Council
- 948381
- United Kingdom Space Agency
- ST/Y000692/1
- National Aeronautics and Space Administration
- 80GSFC21M0002
- National Science Foundation
- AST-2108402
- National Science Foundation
- AST-1440341
- National Science Foundation
- AST-2034437
- National Science Foundation
- AST-1106171
- Gordon and Betty Moore Foundation