Statistical Signatures of Gamma-Ray Burst 221009A in X-Ray Observations

Abstract We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data. After standard reduction (barycentric correction, pile-up, background subtraction via HEASOFT), we extracted light curves for each observational ID and for their aggregation. Count-rate histograms were fitted using various statistical distributions; fit quality was assessed by chi-squared and the Bayesian Information Criterion. The first observational segment is best described by a Gaussian distribution ( χ 2 = 68.4; BIC = 7651.2), and the second by a Poisson distribution ( χ 2 = 33.5; BIC = 4413.3). When all segments are combined, the lognormal model provides the superior fit ( χ 2 = 541.9; BIC = 34365.5), indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process. These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics, the collective emission profile across multiple observations is better captured by a lognormal distribution, consistent with complex, compounded variability in GRB afterglows.

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Publication Details

Journal
Research in Astronomy and Astrophysics
Published
2025-05-19
DOI
https://doi.org/10.1088/1674-4527/adda7f
Primary Topic
Gamma-ray bursts and supernovae
Type
article
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article

Statistical Signatures of Gamma-Ray Burst 221009A in X-Ray Observations

N. Dhital, Manjeet Kunwar, Nabin Bhusal
Research in Astronomy and Astrophysics
Gamma-ray bursts and supernovae
article

Statistical Signatures of Gamma-Ray Burst 221009A in X-Ray Observations

N. Dhital, Manjeet Kunwar, Nabin Bhusal
article en

Abstract

Abstract We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data. After standard reduction (barycentric correction, pile-up, background subtraction via HEASOFT), we extracted light curves for each observational ID and for their aggregation. Count-rate histograms were fitted using various statistical distributions; fit quality was assessed by chi-squared and the Bayesian Information Criterion. The first observational segment is best described by a Gaussian distribution ( χ 2 = 68.4; BIC = 7651.2), and the second by a Poisson distribution ( χ 2 = 33.5; BIC = 4413.3). When all segments are combined, the lognormal model provides the superior fit ( χ 2 = 541.9; BIC = 34365.5), indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process. These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics, the collective emission profile across multiple observations is better captured by a lognormal distribution, consistent with complex, compounded variability in GRB afterglows.

Research in Astronomy and AstrophysicsVol. 25(7)
Tribhuvan University (NP)
Openalex Percentile: Top 93%
Gamma-ray bursts and supernovae
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Statistical Signatures of Gamma-Ray Burst 221009A in X-Ray Observations — N. Dhital, Manjeet Kunwar, et al. · Research in Astronomy and Astrophysics (2025) | TGRS Research Map | TGRS