$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility

Adam A. Miller; Michael W. Coughlin; Nabeel Rehemtulla; Theophile Jegou Du Laz

$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility

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arxiv 2307.07618 v2 pith:7EMWE6RV submitted 2023-07-14 astro-ph.IM

Nabeel Rehemtulla , Adam A. Miller , Michael W. Coughlin , Theophile Jegou du Laz This is my paper

classification astro-ph.IM

keywords transientbrightbtsbottextttconvolutionalfacilityidentificationmulti-input

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abstract

The Bright Transient Survey (BTS) relies on visual inspection ("scanning") to select sources for accomplishing its mission of spectroscopically classifying all bright extragalactic transients found by the Zwicky Transient Facility (ZTF). We present $\texttt{BTSbot}$, a multi-input convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 14 extracted features. $\texttt{BTSbot}$ eliminates the need for scanning by automatically identifying and requesting follow-up observations of new bright ($m\,<18.5\,\mathrm{mag}$) transient candidates. $\texttt{BTSbot}$ outperforms BTS scanners in terms of completeness (99% vs. 95%) and identification speed (on average, 7.4 hours quicker). See Rehemtulla et al. 2024, ApJ, 972, 7R for the full BTSbot publication

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