Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission

被引:3
作者
Salmon, Lana [1 ]
Hanlon, Lorraine
Martin-Carrillo, Antonio
机构
[1] Univ Coll Dublin, Sch Phys, Dublin D04 V1W8, Ireland
来源
GALAXIES | 2022年 / 10卷 / 04期
基金
爱尔兰科学基金会;
关键词
gamma-ray burst; feature extraction; machine learning; SPECTRAL EVOLUTION; EXTENDED EMISSION; WAVELET ANALYSIS; SHORT GRBS; LONG; DURATION; CLASSIFICATION; LUMINOSITY; SUPERNOVA; PULSES;
D O I
10.3390/galaxies10040078
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Studies of Gamma-Ray Burst (GRB) properties, such as duration and spectral hardness, have found evidence for additional classes, beyond the short/hard and long/soft prototypes, using model-dependent methods. In this paper, a model-independent approach was used to analyse the gamma-ray light curves of large samples of GRBs detected by BATSE, Swift/BAT and Fermi/GBM. All the features were extracted from the GRB time profiles in four energy bands using the Stationary Wavelet Transform and Principal Component Analysis. t-distributed Stochastic Neighbourhood Embedding (t-SNE) visualisation of the features revealed two distinct groups of Swift/BAT bursts using the T-100 interval with 64 ms resolution data. When the same analysis was applied to 4 ms resolution data, two groups were seen to emerge within the first second (T-1) post-trigger. These two groups primarily consisted of short/hard (Group 1) and long/soft (Group 2) bursts, and were 95% consistent with the groups identified using the T-100 64 ms resolution data. Kilonova candidates, arising from compact object mergers, were found to belong to Group 1, while those events with associated supernovae fell into Group 2. Differences in cumulative counts between the two groups in the first second, and in the minimum variability timescale, identifiable only with the 4 ms resolution data, may account for this result. Short GRBs have particular significance for multi-messenger science as a distinctive EM signature of a binary merger, which may be discovered by its gravitational wave emissions. Incorporating the T-1 interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.
引用
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页数:22
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