7 papers
Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
Andrea Addazi, Konstantin Belotsky, Vitaly Beylin +18
The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the…
Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs
Lorenzo Colantonio, Andrea Cacioppo, Federico Scarpati +3
Combinatorial optimization problems near algorithmic phase transitions represent a fundamental challenge for both classical algorithms and machine learning approaches. Among them,…
Physics Briefing Book: Input for the 2026 update of the European Strategy for Particle Physics
Jorge de Blas, Monica Dunford, Emanuele Bagnaschi +127
The European Strategy for Particle Physics (ESPP) reflects the vision and presents concrete plans of the European particle physics community for advancing human knowledge in fundam…
Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
Martino Errico, Davide Fiacco, Stefano Giagu +3
The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorith…
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
Sascha Caron, Andreas Ipp, Gert Aarts +16
Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across parti…
Convolutional neural network based decoders for surface codes
Simone Bordoni, Stefano Giagu
The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible to implement decoding algorith…