Bibliographic Details
| Title: |
The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning |
| Authors: |
Nabeel Rehemtulla, Adam A. Miller, Theophile Jegou Du Laz, Michael W. Coughlin, Christoffer Fremling, Daniel A. Perley, Yu-Jing Qin, Jesper Sollerman, Ashish A. Mahabal, Russ R. Laher, Reed Riddle, Ben Rusholme, Shrinivas R. Kulkarni |
| Source: |
The Astrophysical Journal, Vol 972, Iss 1, p 7 (2024) |
| Publisher Information: |
IOP Publishing, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Astrophysics |
| Subject Terms: |
Time domain astronomy, Sky surveys, Supernovae, Convolutional neural networks, Astrophysics, QB460-466 |
| More Details: |
The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m _peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1538-4357 |
| Relation: |
https://doaj.org/toc/1538-4357 |
| DOI: |
10.3847/1538-4357/ad5666 |
| Access URL: |
https://doaj.org/article/ca29736ac631485b89e1e7b0a02e3a8d |
| Accession Number: |
edsdoj.29736ac631485b89e1e7b0a02e3a8d |
| Database: |
Directory of Open Access Journals |
