Details
| Original language | English |
|---|---|
| Pages (from-to) | 3084-3091 |
| Number of pages | 8 |
| Journal | Monthly Notices of the Royal Astronomical Society |
| Volume | 504 |
| Issue number | 2 |
| Early online date | 19 Apr 2021 |
| Publication status | Published - 1 Jun 2021 |
Abstract
We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60-250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps us to understand the instrument's sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.
Keywords
- gamma rays: general, methods: data analysis, methods: statistical, X-rays: bursts
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Astronomy and Astrophysics
- Earth and Planetary Sciences(all)
- Space and Planetary Science
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In: Monthly Notices of the Royal Astronomical Society, Vol. 504, No. 2, 01.06.2021, p. 3084-3091.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A machine learning approach for GRB detection in AstroSat CZTI data
AU - Abraham, Sheelu
AU - Mukund, Nikhil
AU - Vibhute, Ajay
AU - Sharma, Vidushi
AU - Iyyani, Shabnam
AU - Bhattacharya, DIpankar
AU - Rao, A. R.
AU - Vadawale, Santosh
AU - Bhalerao, Varun
PY - 2021/6/1
Y1 - 2021/6/1
N2 - We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60-250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps us to understand the instrument's sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.
AB - We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60-250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps us to understand the instrument's sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.
KW - gamma rays: general
KW - methods: data analysis
KW - methods: statistical
KW - X-rays: bursts
UR - http://www.scopus.com/inward/record.url?scp=85107989783&partnerID=8YFLogxK
U2 - 10.1093/mnras/stab1082
DO - 10.1093/mnras/stab1082
M3 - Article
AN - SCOPUS:85107989783
VL - 504
SP - 3084
EP - 3091
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
SN - 0035-8711
IS - 2
ER -