BatAnalysis 1.1.0

Routines for analyzing data from BAT on the Neil Gehrels Swift Observatory

The BatAnalysis Pipeline

The BatAnalysis python pipeline is brings the analysis of BAT data to the convenience offered by Python.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgements

About The Project

BatAnalysis is a python package that allows for the convenient analysis of BAT Survey data. This code allows users to:

• easily download BAT survey data,
• batch process the survey observations,
• extract light curves and spectra for each survey observation for a given source,
• create mosaiced images at different time bins, and
• extract light curves and spectra from the mosaiced images for a given source.

This project was developed in collaboration with the members of the BAT Team including: Sibasish Laha, David Palmer, Amy Lien and Craig Markwardt.

Built With

• Python 3
• HEASoftPy
• PyXspec
• swiftbat_python
• Swifttools
• typical python modules (astropy, numpy, scipy, etc.)

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

The following software are necessary for the BatAnalysis python module.

1. Python 3.8 or larger
   • We recommend installing via Anaconda
2. swiftbat_python
   • This can be easily installed via pip
3. Swifttools
   • This can also be easily installed via pip
4. HEASoftPy >= v1.2.1 (HEASoft >= v6.31.1)
   • The installation is included with HEASoft and the steps can be found here: https://heasarc.gsfc.nasa.gov/lheasoft/install.html
5. PyXspec
   • The installation is included with HEASoft but there may be additional steps to take if you have changed your anaconda version after building HEASoft. More information can be found here: https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/buildinstall.html
6. Swift BAT Pattern Noise Maps
   • The pattern maps used by the BAT team are available at: https://zenodo.org/record/7595904#.Y9q7pS-B3T8 for download. These maps shoud be downloaded and placed into a place where the BatAnalysis code will be able to access them.

Installation

1a. Install the BatAnalysis package with Pip

pip install BatAnalysis

1b. Clone the repo and install

git clone https://github.com/parsotat/BatAnalysis.git
cd BatAnalysis/
pip install -e .

Usage

The typical BatAnalysis workflow is as follows:

1. Use astroquery to queue HEASARC for obervations within a given date range for an RA/DEC coordinate corresponding to a source of interest
2. Download the data
3. Run batsurvey on each observation ID
4. Calculate the detector response matrix for each survey pointing
5. Calculate the spectrum in each pointing
6. Fit the spectra to obtain spectral information
7. Calculate the light curve for the time period of interest
8. Plot the light curve/spectral information
9. Create mosaiced images for the time binning of interest (days, weeks, months, years)
10. Create the total "time-integrated" mosaiced image
11. Extract the spectra of the mosaiced images and fit them
12. Calculate the light curve of the source from the mosaiced images
13. Plot the light curve/spectral information for the mosaiced images

Following these steps for the first 5 survey observation IDs for the Crab (although see the notebooks directory for recommended analysis steps):

import batanalysis as ba
import swiftbat
import numpy as np 
import matplotlib.pyplot as plt
from pathlib import Path
plt.ion()

object_name='Crab'
catalog_name="Crab_Nebula_Pulsar"
table = ba.from_heasarc(object_name)
result = ba.download_swiftdata(table[:5])

batsurvey_obs=ba.parallel.batsurvey_analysis(table["OBSID"][:5], nprocs=2)

#the name of the source in the included survey catalog must be used here 
noise_map_dir=Path("/path/to/downloaded/PATTERN_MAPS/")
batsurvey_obs=ba.parallel.batspectrum_analysis(batsurvey_obs, catalog_name, patt_noise_dir=noise_map_dir, nprocs=2)

ba.plot_survey_lc(batsurvey_obs, id_list=catalog_name, calc_lc=True)

outventory_file=ba.merge_outventory(batsurvey_obs)

#bin into 1 month time bins
time_bins=ba.group_outventory(outventory_file, np.timedelta64(1,'M'))

#bin into daily time bin
mosaic_list, total_mosaic=ba.parallel.batmosaic_analysis(batsurvey_obs, outventory_file, time_bins, nprocs=3)

mosaic_list=ba.parallel.batspectrum_analysis(mosaic_list, catalog_name, nprocs=2)

ba.plot_survey_lc(mosaic_list, id_list=catalog_name, calc_lc=True)

For more details and additional examples please refer to the Notebooks directory

Roadmap

This package will soon be modified to include analysis of BAT event data.

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Tyler Parsotan - Personal Website - [e-mail]

Project Link: https://github.com/parsotat/BatAnalysis.git

Acknowledgements

• In using the BatAnalysis code, we ask that you cite the following paper:
  • Parsotan et. al. 2023 (submitted to ApJ)
• README Template from: othneildrew/Best-README-Template