kalman_detector

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.

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}
}