Astrophysical Narratives: Poetic Representations of Gamma-Ray Emission from FermiLAT via Markov Chains

TL;DR

This paper introduces a novel method that uses Markov chains to generate poetic texts inspired by gamma-ray emission maps from the universe, blending astrophysics with artistic language.

Contribution

It presents a new algorithm that converts gamma-ray data into poetic narratives using Markov chain modeling, bridging science and art.

Findings

Successful generation of poetic texts from gamma-ray maps

Demonstrates the potential for artistic interpretation of astrophysical data

Provides a novel interdisciplinary approach combining astrophysics, NLP, and poetry

Abstract

The intersection of art and science offers novel ways to interpret and represent complex phenomena. This project explores the convergence of high-energy astrophysics, concrete poetry, and natural language processing (NLP) by proposing a Markov chain-based algorithm that generates poetic texts from gamma-ray emission maps of the universe. Gamma rays, the most energetic form of electromagnetic radiation, arise from violent astrophysical processes such as supernovae, pulsars, and black hole accretion, observable through instruments like the \textit{Fermi Large Area Telescope} (FermiLAT). These high-energy events are mapped and processed into matrices, treated as Markov chains, where each state's transition probability is determined by the intensity of gamma-ray sources in a region of interest.