# Confirmation of Binary Clustering in Gamma-Ray Bursts through an Integrated p-value from Multiple Nonparametric Tests of Hypotheses

arXiv: https://arxiv.org/abs/2605.04739 · HTML: https://mlsanity.com/paper/2605.04739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 88 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 3 of 3)

- [A new measure for assessment of clustering based on kernel density estimation](https://mlsanity.com/paper/2201.02030/) (2022)
- [A new nonparametric interpoint distance-based measure for assessment of clustering](https://mlsanity.com/paper/2210.08972/) (2022)
- [A new interpoint distance-based clustering algorithm using kernel density estimation](https://mlsanity.com/paper/2305.00901/) (2023)

## Built on by (most cited first, 1 of 1)

- [A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts](https://mlsanity.com/paper/2605.30175/) (2026)

## Abstract (arXiv)

The paper applies a new, nonparametric, interpoint distance-based measure to confirm the inherent groups prevailing in the brightest source of light in the universe: gamma-ray bursts. Our effective metric, in association with clustering methods like Gaussian-mixture model-based and K-means algorithms, resolves the conflict regarding the possibility about existence of more than binary clusters in the gamma-ray burst population. Here we carry out multiple nonparametric statistical tests of hypotheses, as many as the number of bursts available from the `BATSE' catalog. An integrated p-value achieved from the aforesaid dependent tests solves our concern confirming two groups of short and long bursts.