Conveners
Special Session on Machine Learning
- David Kapukchyan (University of California, Riverside)
Special Session on Machine Learning
- Roberto Rossin (Universita e INFN, Padova (IT))
Special Session on Machine Learning
- Tommaso Dorigo (Universita e INFN, Padova (IT))
Special Session on Machine Learning
- Catalin Frosin (Universita e INFN, Firenze (IT))
Special Session on Machine Learning
- Mario Merola (University of Napoli Federico II and INFN - National Institute for Nuclear Physics)
In 2012 --the same year when ML methods proven super-human image classification power in the ImageNet challenge-- the CMS and ATLAS collaborations employed for the first time supervised learning tools for a major physics discovery (the Higgs boson). That constituted a revolution in how inference is extracted from complex data in high-energy physics: without ML tools before 2012, with ML tools...
Invited tak
In this contribution we propose a data-driven technique based on self-supervised deep neural networks, specifically convolutional and variational autoencoders (AE), developed to improve the sensitivity to signal pulses over a significant background in long waveforms.
The dataset consists of synthetic waveforms with around 10,000 samples; each time-series is composed of non-gaussian noise,...
High granularity 3D calorimeters offer the opportunity to precisely reconstruct the 3D topology of electromagnetic and hadronic showers originating from isotropic sources. This distinctive capability not only allows for the reconstruction of events from a much wider field of view, but also enable analysis strategies that could yield additional information compared to those based on the...
The integration of advanced artificial intelligence (AI) techniques into astroparticle experiments marks a transformative step in both data analysis and experimental design. As space missions grow increasingly complex, the adoption of AI technologies becomes critical for optimizing performance and achieving robust scientific outcomes. In this context, we explore two innovative AI-driven...
In current-generation particle detectors, traditional backend data processing is increasingly migrating toward front-end electronics. This shift enables earlier event selection and real-time signal processing within the data acquisition chain, reducing bandwidth and improving system responsiveness. In large-scale neutrino experiments, identifying low-energy events is particularly difficult due...
