Artificial Intelligence and complex networks in multimodal X-ray astrophysics

Sep 19, 2025·
Nicolò Pinciroli
Nicolò Pinciroli
· 0 min read
Abstract
Astrophysical observations are described by multimodal data such as light curves, spectra, and scientific texts. The extraction of compact representations from these data helps identify meaningful patterns and understand their physical meaning. This talk focuses on three works that use deep learning and complex networks to achieve these goals. The first work shows how autoencoders can compress X-ray spectra into a low-dimensional latent space while preserving physical information. The second work shows that light curves can be aligned with textual summaries using contrastive learning. This alignment generates a shared compact multimodal representation of both modalities. Finally, the third work shows that complex networks can capture patterns in multimodal data, revealing anomalies (such as rare astrophysical sources).
Date
Sep 19, 2025 10:00 AM — 10:30 AM
Event
Location

In-person

24 Baghramyan Avenue, Yerevan,

events
Artificial Intelligence Astrophysics
Nicolò Pinciroli
Authors
Nicolò Pinciroli (he/him)
Postdoctoral Research Fellow

I am a postdoctoral research fellow at Politecnico di Milano, working at the intersection of artificial intelligence and astrophysics. I develop methods that combine spectra, images, time series, and scientific literature to study rare astronomical sources and transient phenomena. My research spans multimodal representation learning, explainable AI, gravitationally lensed transients, and the search for candidate pulsating ultraluminous X-ray sources.

I collaborate with INAF’s Osservatorio Astronomico di Roma and AstroAI at the Center for Astrophysics | Harvard & Smithsonian. I also work on time-series anomaly detection, predictive maintenance, and language-model applications for energy efficiency, and lead Work Package 3 of the Horizon Europe ENERGENIUS project.

I completed my PhD in Information Technology at Politecnico di Milano in June 2026, with the thesis “Making discoveries with multimodal astrophysics.”