Toward a topological data analysis for heavy-ion collisions

The collective expansion of the quark-gluon plasma (QGP) created in heavy-ion collisions suggests that geometry-inspired approaches can be useful in extracting information about the QGP. In this work, a systematic study of observables based on topological data analysis is provided for simulations of heavy-ion collisions. Specifically, we implement persistent homology observables for metric-based complexes in the heavy-ion model and provide predictions for Pb-Pb and O-O collisions, where the tunable model parameters are taken from a Bayesian analysis performed in Pb-Pb collisions. This, in particular, allows us to compute systematic uncertainties on our observables from the uncertainties in the model parameters. To bridge between new and already established observables, we build a dictionary linking the topological observables to traditional ones, such as particle multiplicities, momentum distributions, and the elliptic flow coefficient. While the persistent homology observables largely reflect known phenomenology and do not show enhanced sensitivity to the model's tunable parameters compared to conventional observables, this study demonstrates the viability and robustness of topological techniques in the context of heavy-ion physics. They may offer alternative perspectives and potential applications in heavy-ion physics.