# Clustering of Gamma-Ray bursts through kernel principal component analysis

## TL;DR

This paper introduces a kernel PCA method for clustering gamma-ray bursts, demonstrating improved accuracy and revealing three meaningful groups, with applications to noisy, nonlinear data.

## Contribution

It proposes a new kernel for kernel PCA that outperforms existing kernels in clustering gamma-ray bursts and other datasets.

## Key findings

- The proposed kernel yields higher clustering accuracy.
- Three physically interpretable gamma-ray burst groups identified.
- Effective in noisy, nonlinear data reduction.

## Abstract

We consider the problem related to clustering of gamma-ray bursts (from "BATSE" catalogue) through kernel principal component analysis in which our proposed kernel outperforms results of other competent kernels in terms of clustering accuracy and we obtain three physically interpretable groups of gamma-ray bursts. The effectivity of the suggested kernel in combination with kernel principal component analysis in revealing natural clusters in noisy and nonlinear data while reducing the dimension of the data is also explored in two simulated data sets.