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

TL;DR

BTSbot is a neural network that automates bright transient detection in ZTF data, surpassing manual scanning in speed and completeness, thus streamlining the survey process.

Contribution

It introduces a multi-input CNN model that automates bright transient identification, reducing reliance on manual visual inspection in the ZTF survey.

Findings

Achieves 99% completeness in transient detection.

Speeds up identification by an average of 7.4 hours.

Outperforms traditional scanning methods.

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