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. 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 and Fonseca et al. 2020. More details are provided in my PhD thesis, 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:

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.