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Detecting Fast Radio Bursts with Spectral Structures

Project description

kalman_detector

GitHub CI codecov License Code style: black

A Python Implementation of a Kalman filter detector for detecting smoothly variying signals hidden in gaussian noise, such as Fast Radio Bursts (FRBs).

The detection statistic is designed to process I(f), a sequence of observed "amplitudes" (where f is an arbitrary indexed parameter), and decide between the following hypotheses:

H0: I(f) = N(f)         Pure gaussian noise
H1: I(f) = A(f) + N(f)  A(f) is a smooth gaussian process with an unknown smoothness parameter. 

Installation

The quickest way to install the package is to use pip:

pip install -U kalman_detector

Usage

from kalman_detector.main import KalmanDetector

kalman = KalmanDetector(spectrum_std)
kalman.prepare_fits(ntrials=10000)
kalman.get_significance(spectrum)

Example

An example script demonstrating how to use the Kalman detector can be found in the examples directory.

Kalman demo for FRB171003 Kalman demo for FRB171004

Efficiency

An example efficiency plot can be generates using:

python -m kalman_detector.efficiency

Citation

Please cite Kumar, Zackay & Law (2024) if you find this code useful in your research. The BibTeX entry for the paper is:

@ARTICLE{2024ApJ...960..128K,
       author = {{Kumar}, Pravir and {Zackay}, Barak and {Law}, Casey J.},
        title = "{Detecting Fast Radio Bursts with Spectral Structure Using the Continuous Forward Algorithm}",
      journal = {\apj},
     keywords = {Radio astronomy, Radio transient sources, Astronomy data analysis, Astrostatistics techniques, Interstellar scintillation, 1338, 2008, 1858, 1886, 855, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2024,
        month = jan,
       volume = {960},
       number = {2},
          eid = {128},
        pages = {128},
          doi = {10.3847/1538-4357/ad0964},
archivePrefix = {arXiv},
       eprint = {2306.07914},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024ApJ...960..128K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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