Machine learning challenges earlier hypotheses about the formation of long- and short-duration gamma-ray bursts


Machine learning algorithms have revealed unexpected complexity in gamma-ray bursts (GRBs), challenging earlier hypotheses that long-period GRBs arise from the collapse of massive stars and short-period GRBs arise from compact binary mergers. Are generated from. Two distinct populations of compact binary merger (kilonova-associated) GRBs identified through the algorithm revealed that the two groups of kilonova-associated GRBs have similar compactness but differ in their periods. This new unexpected complexity provides new insights into the nature of these powerful cosmic explosions.

GRBs are one of the brightest transient astronomical phenomena in the universe. These events are traditionally classified as long or short based on the duration of their rapid emission. Long-period GRBs, those lasting longer than 2 seconds, are believed to result from the collapse of massive stars. In contrast, short-period GRBs with durations less than 2 seconds are thought to originate from compact binary mergers. However, recent observations have challenged this traditional classification, with some long-period GRBs originating from compact binary mergers (associated with kilonovae) and some short-period GRBs originating from the deaths of massive stars (associated with supernovae). Suspected to happen. ,

A study was recently published to solve this puzzle. The Astrophysical Journal Letters By a team of researchers from Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital, an autonomous institute of the Department of Science and Technology (DST), Government of India. The Government of India and the Chennai Mathematical Institute (CMI), Chennai employed accelerated emission light curves of GRBs and machine learning techniques to find evidence of two distinct populations of compact binary mergers (associated with kilonovae).

The algorithm identified five distinct groups of GRBs, with GRBs associated with kilonovae located in two distinct groups. This suggests that these kilonova-related GRBs with similar intensities but different durations were produced by different progenitor systems or by neutron star-neutron star (NS-NS) and/or neutron star-black hole mergers (NS-BH). Subclasses may arise. ,

gamma-ray burst

Figure 1: Locations of gamma-ray bursts (GRBs) associated with kilonovae on a two-dimensional map obtained using PCA-TSNE and PCA-UMAP. Coral colored labels represent GRBs with confirmed kilonova associations, while turquoise colored labels represent GRBs with potential kilonova candidates. GRBs associated with long- and short-period kilonovae occupy two locations on the map (right and left corners). Filled gray squares show the location of the GRB-SNE on the map. The map is color-coded in relation to the five clusters identified by the algorithm.

Overall, the study demonstrates the power of machine learning techniques to uncover unexpected complexity within astronomical populations, with the discovery of two distinct kilonova-related GRB populations and may have important implications for future gravitational wave observations. Is. This could help understand different groups of GRBs in more detail and potentially provide new insights into the nature of these powerful cosmic explosions.

Publication link: DOI 10.3847/2041-8213/acd4c4

Contact Dimple for more information[at]ARIS[dot]r e[dot]In, Kuntal[at]ARIS[dot]r e[dot]in, kilograms[at]cmi[dot]ac[dot]In


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