Developing Applications to Science Analysis for Astroparticle Physics using Self-Organizing Networks

Title: Developing Applications to Science Analysis for Astroparticle Physics using Self-Organizing Networks

Abstract: This research aims to develop applications for science analysis, particularly in astroparticle physics, using Self-Organizing Networks (SONs). The study focuses on classifying gamma ray bursts (GRBs) and associating them with known sources, which is a crucial task in astroparticle physics. The authors propose using SONs, a type of unsupervised classifier, to analyze large datasets from various detectors and telescopes. This approach allows for the efficient organization and analysis of complex data, enabling the identification of new phenomena and patterns. The research suggests that SONs can be a valuable tool for data mining and analysis in astroparticle physics, leading to improved understanding and discovery of new cosmic phenomena.

Main Research Question: Can Self-Organizing Networks be effectively used to develop applications for science analysis in astroparticle physics, particularly for classifying and associating gamma ray bursts with known sources?

Methodology: The study uses a Self-Organizing Network (SON), a type of unsupervised classifier, to analyze and classify gamma ray bursts (GRBs). The SON is applied to large datasets from various detectors and telescopes, allowing for the efficient organization and analysis of complex data. The research team compares the performance of SONs to other existing classifiers and assesses their effectiveness in identifying new phenomena and patterns in the data.

Results: The research shows that SONs can be effectively used to develop applications for science analysis in astroparticle physics. The classifier successfully organized and analyzed large datasets from various detectors and telescopes, enabling the identification of new phenomena and patterns in the data. The study also found that SONs performed better than other existing classifiers in certain aspects, such as the ability to handle high-dimensional data and the capacity for visualization.

Implications: The research suggests that SONs can be a valuable tool for data mining and analysis in astroparticle physics. By using SONs to classify and associate gamma ray bursts with known sources, scientists can improve their understanding of cosmic phenomena and potentially discover new ones. Additionally, the study demonstrates that SONs can effectively handle large and complex datasets, making them a promising tool for other applications in astroparticle physics and related fields.

Link to Article: https://arxiv.org/abs/0402014v1 Authors: arXiv ID: 0402014v1