Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields.

Jun 12, 2021·
Nicolò Pinciroli
Nicolò Pinciroli
,
Ibrahim A. Hameed
,
Michael Kachelriess
· 0 min read
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Abstract
The presence of non-zero helicity in intergalactic magnetic fields is a smoking gun for their primordial origin since they have to be generated by processes that break CP invariance. As an experimental signature for the presence of helical magnetic fields, an estimator Q based on the triple scalar product of the wave-vectors of photons generated in electromagnetic cascades from, e.g., TeV blazars, has been suggested previously. We propose to apply deep learning to helicity classification employing Convolutional Neural Networks and show that this method outperforms the Q estimator.
Type
Publication
Proceedings of Science - ICRC
publications
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.”

Authors
Ibrahim A. Hameed
Authors
Michael Kachelriess