$\texttt{tdescore}$: An Accurate Photometric Classifier for Tidal Disruption Events
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2023
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| author | Stein, Robert Mahabal, Ashish Reusch, Simeon Graham, Matthew Kasliwal, Mansi M. Kowalski, Marek Gezari, Suvi Hammerstein, Erica Nakoneczny, Szymon J. Nicholl, Matt Sollerman, Jesper van Velzen, Sjoert Yao, Yuhan Laher, Russ R. Rusholme, Ben |
| author_facet | Stein, Robert Mahabal, Ashish Reusch, Simeon Graham, Matthew Kasliwal, Mansi M. Kowalski, Marek Gezari, Suvi Hammerstein, Erica Nakoneczny, Szymon J. Nicholl, Matt Sollerman, Jesper van Velzen, Sjoert Yao, Yuhan Laher, Russ R. Rusholme, Ben |
| contents | 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 $\texttt{tdescore}$, a simple binary photometric classifier that is trained using a systematic census of $\sim$3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing $\sim$2% of the total. $\texttt{tdescore}$ 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%. $\texttt{tdescore}$ 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 multi-wavelength catalogue cross-matching. In a novel extension, we use `SHapley Additive exPlanations' ($\texttt{SHAP}$) to provide a human-readable justification for each individual $\texttt{tdescore}$ classification, enabling users to understand and form opinions about the underlying classifier reasoning. $\texttt{tdescore}$ can serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_00139 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | $\texttt{tdescore}$: An Accurate Photometric Classifier for Tidal Disruption Events Stein, Robert Mahabal, Ashish Reusch, Simeon Graham, Matthew Kasliwal, Mansi M. Kowalski, Marek Gezari, Suvi Hammerstein, Erica Nakoneczny, Szymon J. Nicholl, Matt Sollerman, Jesper van Velzen, Sjoert Yao, Yuhan Laher, Russ R. Rusholme, Ben Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena 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 $\texttt{tdescore}$, a simple binary photometric classifier that is trained using a systematic census of $\sim$3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing $\sim$2% of the total. $\texttt{tdescore}$ 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%. $\texttt{tdescore}$ 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 multi-wavelength catalogue cross-matching. In a novel extension, we use `SHapley Additive exPlanations' ($\texttt{SHAP}$) to provide a human-readable justification for each individual $\texttt{tdescore}$ classification, enabling users to understand and form opinions about the underlying classifier reasoning. $\texttt{tdescore}$ can serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory. |
| title | $\texttt{tdescore}$: An Accurate Photometric Classifier for Tidal Disruption Events |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2312.00139 |