Two Dimensional Clustering of Swift/BAT and Fermi/GBM Gamma-ray Bursts

Salmon, Lána; Hanlon, L.; Martín-Carrillo, A.

doi:10.3390/galaxies10040077

Cited by 6 publications

(3 citation statements)

References 103 publications

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“…MCLUST identifies five clusters. This may indicate that the application of a Gaussian model does not adequately represent the underlying complex distributions [17].…”

Section: Ms Resultsmentioning

confidence: 99%

“…In Salmon et al [17], Gaussian Mixture Model-based clustering of the hardness-T 90 plane identified two classes of bursts in the Swift/BAT and Fermi/GBM samples. The results suggest that the intermediate duration class may be an artefact of the application of unsuitable models, as was also suggested by Tarnopolski [25], Koen and Bere [103], Tarnopolski [104], Tarnopolski [105].…”

Section: Light Curve Classificationsmentioning

confidence: 99%

“…Clustering of the durationhardness plane and multi-dimensional analyses of GRB samples from different satellites have also revealed evidence of more than two classes of bursts. Salmon et al [17] presents a review of previous studies and reports on an updated clustering analysis of Swift/BAT and Fermi/GBM bursts which finds that Gaussian models applied to Swift/BAT and Fermi/GBM GRB samples recover three clusters, including an intermediate-duration one. However, the latter is identified as an excess Gaussian component when an entropy criterion is used and the resulting best-fit solution contains two classes, which are broadly consistent with the typical short-and long-duration groups.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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 T100 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 (T1) 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 T100 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 T1 interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.

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“…MCLUST identifies five clusters. This may indicate that the application of a Gaussian model does not adequately represent the underlying complex distributions [17].…”

Section: Ms Resultsmentioning

confidence: 99%

Section: Light Curve Classificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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Some statistical remarks on GRBs jointly detected by Fermi and Swift satellites

Pinter,

Balazs,

Bagoly

et al. 2023

Monthly Notices of the Royal Astronomical Society

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We made statistical analysis of the Fermi GBM and Swift BAT observational material, accumulated over 15 yr. We studied how GRB parameters (T90 duration, fluence, and peak flux) that were observed by only one satellite differ from those observed by both. In the latter case, it was possible to directly compare the values of the parameters that both satellites measured. The GRBs measured by both satellites were identified using the k-nearest neighbours algorithm in Euclidean distance. In the parameter space we determined the direction in which the jointly detected GRBs differ most from those detected by only one of the instruments using linear discriminant analyses. To get the strength of the relationship between the parameters obtained from the GBM and BAT, a canonical correlation was performed. The GBM and BAT T90 distributions were fitted with a linear combination of lognormal functions. The optimal number of such functions required for fit is two for GBM and three for BAT. Contrary to the widely accepted view, we found that the number of lognormal functions required for fitting the observed distribution of GRB durations does not allow us to deduce the number of central engine types responsible for GRBs.

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Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Luo,

Wang,

Zhu-Ge

et al. 2023

ApJ

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The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multiparameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine-learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner’s GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing features in prompt emission to be T 90, the hardness ratio, and fluence. After building the machine-learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.

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Two Dimensional Clustering of Swift/BAT and Fermi/GBM Gamma-ray Bursts

Cited by 6 publications

References 103 publications

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

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

Some statistical remarks on GRBs jointly detected by Fermi and Swift satellites

Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

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