Speaker
Description
Koexai is a deep-tech company working at the interface between machine learning, scientific software, and astrophysical data analysis. Over the last year, within the INAF / PNRR / ICSC Spoke 3 context, Koexai has supported several research groups on projects spanning supernova characterisation, cosmological simulations, pulsar-timing inference, X-ray background mitigation, and gamma-ray transient analysis.
This contribution presents two case studies with published or submitted scientific outputs. The first is ASTRAI, a framework for the automatic characterisation of low-interacting hydrogen-rich supernovae, combining generative models for synthetic light-curve production with deep learning methods for physical-parameter inference. The second is DeepCosmoNet, focused on deep-learning approaches for the analysis of cosmological N-body simulations and the identification of large-scale structures in the Cosmic Web, including halos, subhalos, and cosmic voids.
The talk will discuss Koexai’s experience in translating astrophysical research problems into robust AI workflows, highlighting practical lessons on data preparation, model design, validation, and collaboration between domain experts and machine-learning teams.