https://github.com/zpleunis/dfdt

Linear drift rate measurements for fast radio bursts
https://github.com/zpleunis/dfdt

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Linear drift rate measurements for fast radio bursts

• Host: GitHub
• URL: https://github.com/zpleunis/dfdt
• Owner: zpleunis
• License: bsd-3-clause
• Created: 2020-09-18T01:48:08.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-09-04T22:35:25.000Z (2 months ago)
• Last Synced: 2024-09-05T23:11:47.952Z (2 months ago)
• Language: Python
• Size: 15.5 MB
• Stars: 8
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# dfdt

Measure a linear drift rate for a fast radio burst, (for now) using a 2D auto-correlation analysis and Monte Carlo resampling. Different methods might be added in the future.

Created by Ziggy Pleunis, with contributions from Alex Josephy, Deborah Good and Affan Khadir.

Feel free to open an issue on GitHub or email [email protected] with questions or comments.

## Method

A 2D auto-correlation analysis for measuring linear drift rates was first used by [Hessels et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...876L..23H/abstract).

The addition of Monte Carlo resampling has been developed to analyze CHIME/FRB bursts, and it has so far been used in [CHIME/FRB Collaboration et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...885L..24C/abstract) and [Fonseca et al. 2020](https://ui.adsabs.harvard.edu/abs/2020ApJ...891L...6F/abstract). More details are provided in my [PhD thesis](https://escholarship.mcgill.ca/concern/theses/f4752n54q), the relevant sections of which are extracted and put into the `docs` directory here. The `docs` also contain a video that illustrates the drift rate definition, as well as a Jypter notebook used to make the video.

## Installation

- Download source code:

```

git clone https://github.com/zpleunis/dfdt.git

```

- Install in develop mode:

```

cd dfdt/

python setup.py develop [--user]

```

## Usage

The `data` directory contains a dedispersed waterfall from repeating sources of FRBs 180916.J0158+65 as detected by CHIME/FRB. After installing `dfdt` you can measure the drift rate in this burst using:

```python

import numpy as np

import dfdt

fname = "[path to package]/dfdt/data/23891929_DM348.8_waterfall.npy"

dedispersed_intensity = np.load(fname)

# burst parameters

dm_uncertainty = 0.2  # pc cm-3

source = "R3"

eventid = "23891929"

# instrument parameters

dt_s = 0.00098304

df_mhz = 0.0244140625

nchan = 16384

freq_bottom_mhz = 400.1953125

freq_top_mhz = 800.1953125

ds = dfdt.DynamicSpectrum(dt_s, df_mhz, nchan, freq_bottom_mhz, freq_top_mhz)

constrained, dfdt_data, dfdt_mc, dfdt_mc_low, dfdt_mc_high = dfdt.ac_mc_drift(

    dedispersed_intensity, dm_uncertainty, source, eventid, ds,

    dm_trials=100, mc_trials=100

)

```

The output of a run, two diagnostic figures and a `.npz` file with all the drift rates, with 100x100=10,000 Monte Carlo trials is saved into a `results` directory here. For explanations of the diagnostic figures see the `docs` directory. See the docstring of the `ac_mc_drift()` function for more details.