BatAnalysis - A Comprehensive Python Pipeline for Swift BAT Time-tagged Event Data Analysis
Creators
- 1. NASA Goddard Space Flight Center
-
2.
Los Alamos National Laboratory
-
3.
New Mexico Consortium
-
4.
Pennsylvania State University
-
5.
California Institute of Technology
-
6.
University of Tampa
-
7.
National Science Foundation
Abstract
The Neil Gehrels Swift Observatory (Swift) Burst Alert Telescope (BAT) is a coded aperture gamma-ray instrument with a large field of view that was designed to detect and localize transient events. When a transient is detected, either on board or externally, the BAT saves time-tagged event (TTE) data, which provide the highest-quality information of the locations of the photons on the detector plane and their energies. These data can be used to produce spectra, lightcurves, and sky images of a transient event. While these data products are produced by the Swift Data Center and can be produced by current software, they are often preset to certain time and energy intervals, which have limited their use in the current time domain and multimessenger environment. Here, we introduce a new capability for the BatAnalysis Python package to download and process TTE data under an open-source Python framework that allows for easy interfacing with other Python packages. The new capabilities of the BatAnalysis software allow for TTE data to be used by the community in a variety of advanced customized analyses of astrophysical sources which BAT may have TTE data for, such as fast radio bursts (FRBs), gamma-ray bursts (GRBs), low-mass X-ray binaries (LMXB), soft gamma repeaters, magnetars, and many other sources. We highlight the usefulness of the BatAnalysis package in analyzing TTE data produced by an onboard GRB trigger, an FRB external trigger, a subthreshold detection of the LMXB EXO 0748–676, and an external trigger of a GRB that BAT detected during a slew.
Copyright and License
© 2025. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
The material is based upon work supported by NASA under award number 80GSFC21M0002.
This material is based upon work supported by the National Aeronautics and Space Administration under Agreement No.80NSSC23K0552 issued through the Office of Science. In accordance with Federal law, NMC is prohibited from discriminating on the basis of race, color, national origin, sex, age, or disability.
This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC. We thank Bryan Irby and Abdu Zoghbi for help with HEASoftpy, Israel Martinez-Castellanos for his development of Histpy, and Jamie Kennea for his assistance with swifttools.
Code Availability
Astropy (Astropy Collaboration et al. 2013,2018, 2022), Astropy/reproject (T. Robitaille et al. 2024), Astroquery (A. Ginsburg et al. 2019), BatAnalysis (T. Parsotan et al. 2024) NumPy (C. R. Harris et al. 2020), matplotlib (J. D. Hunter 2007), SciPy (P. Virtanen et al. 2020), HEASoft (Nasa High Energy Astrophysics Science Archive Research Center Heasarc 2014), swiftbat_Python, Xspec (K. A. Arnaud 1996), swifttools (J. Kennea et al. 2024), Histpy (I. Martinez-Castellanos 2024).
Files
Parsotan_2025_ApJ_988_23.pdf
Files
(1.2 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:e3c20c387f0a7b4d63d3cf60c7239811
|
1.2 MB | Preview Download |
Additional details
Funding
- National Aeronautics and Space Administration
- 80GSFC21M0002
- National Aeronautics and Space Administration
- Office of Science 80NSSC23K0552
Dates
- Available
-
2025-07-11Published online