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19 April 2024 |
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Article overview
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A machine learning approach for GRB detection in AstroSat CZTI data | | Sheelu Abraham
; Nikhil Mukund
; Ajay Vibhute
; Vidushi Sharma
; Shabnam Iyyani
; Dipankar Bhattacharya
; A. R. Rao
; Santosh Vadawale
; Varun Bhalerao
; | | Date: |
24 Jun 2019 | | Abstract: | We present a machine learning (ML) based method for automated detection of
Gamma-Ray Bursts (GRBs) from the AstroSat CZTI data. We make use of
density-based spatial clustering to detect excess power and carry out an
unsupervised hierarchical clustering across all such events to identify the
different categories of light curves present in the data. This representation
helps in understanding the sensitivity of the instrument to the various GRB
populations and identifies the major non-astrophysical noise artifacts present
in the data. We make use of dynamic time wrapping (DTW) to carry out template
matching to ensure the morphological similarity of the detected events with
that of known typical GRB light curve. DTW alleviates the need for a dense
template repository often required in matched filtering like searches, and the
use of a similarity metric facilitates outlier detection suitable for capturing
previously unmodeled events. Using the pipeline, we detect 35 new GRB events
and briefly report their characteristics in this paper. Augmenting the existing
data analysis pipeline with ML capabilities enables the instrument to quickly
respond to alerts received from observatories such as the gravitational wave
detectors and carry out robust follow up studies without the need for an
onboard classification facility. | | Source: | arXiv, 1906.9670 | | Services: | Forum | Review | PDF | Favorites | |
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