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

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 $\mathit{\text{Fermi}}\mathit{\text{Large}}\mathit{\text{Area}}\mathit{\text{Telescope}}$ (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.