$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility
Instrumentation and Methods for Astrophysics2025-06-05v2
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 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. BTSbot eliminates the need for scanning by automatically identifying and requesting follow-up observations of new bright (m<18.5mag) transient candidates. 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
@article{arxiv.2307.07618,
title = {$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility},
author = {Nabeel Rehemtulla and Adam A. Miller and Michael W. Coughlin and Theophile Jegou du Laz},
journal= {arXiv preprint arXiv:2307.07618},
year = {2025}
}
Comments
Accepted at the ICML 2023 Workshop on ML for Astrophysics; see Rehemtulla et al. 2024, ApJ, 972, 7R for the full BTSbot publication