Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence...
Geometric deep learning models are being adopted across science and engineering to estimate large-scale PDE solutions for varying boundary conditions. While accurate uncertainty quantification (UQ) is essential for better decision-making for a variety of downstream tasks like optimisation and control, these models rarely produce efficient and effective UQ. Moreover, most UQ methods focus on...
Adversarial machine learning is a collection of techniques used to study attacks on machine learning algorithms. It is commonly used in cybersecurity and still has few applications in fundamental physics. Since systematic effects in detector data, often absent in Monte Carlo simulations, challenge the performance of machine learning models in particle physics, in this work we model the...
We discuss about applications of hybrid quantum-classical computing and present Qiboml, an open-source software library for Quantum Machine Learning (QML) integrated with the Qibo quantum computing framework. Qiboml interfaces most used classical Machine Learning frameworks such as TensorFlow, PyTorch and Jax with Qibo. This combination enables users to construct quantum or hybrid...
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of...
Reservoir computing (RC) has emerged as a powerful paradigm for processing temporal data and pattern recognition, leveraging the intrinsic dynamics of complexcsystems to perform high-dimensional nonlinear transformations without the need for training highly sophisticated networks. Our recent achievements show that colloidal systems — specifically engineered suspensions of nanoparticles in...
Pulse pile-up is a common issue in nuclear spectroscopy and nuclear reaction studies, degrading energy and timing accuracy in particle identification. This work presents a novel method for reconstructing pile-up events using a one-dimensional convolutional autoencoder (1D-CAE). The method effectively separates and reconstructs overlapping pulses, enabling acceptance of these events and...
In this work we simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions in the form of spike trains and employs a fully connected spiking neural...
Current Imaging Atmospheric Cherenkov Telescopes (IACT) use combined analog and digital electronics for their trigger systems, implementing simple but fast algorithms. Such trigger techniques are needed in order to cope with the extremely high data rates and strict timing requirements. In recent years, in the context of the Advanced camera design for the Large-Sized Telescopes (LSTs) of the...
Monte-Carlo (MC) simulations are essential for designing particle physics experiments, as they enable us to evaluate and optimize key objectives—such as enhancing experimental sensitivity and performance. Since exhaustively sampling the full parameter space of experimental configurations is computationally prohibitive, sample-efficient methods to identify promising configurations are...
The traditional design of gravitational wave detectors follows a human-centric, rational approach based on domain expertise and ingenuity. However, the vast space encompassing all possible experimental configurations suggests that some powerful and unconventional detection strategies lay outside the reach of such human-driven design. An AI-based approach that scales with increasing computation...
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods (arXiv:2412.10237). It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well longitudinal placement of trackers in a...
Setup design is a critical aspect of experiment development, particularly in high-energy physics, where decisions influence research trajectories for decades. Within the MODE Collaboration, we aim to generalize Machine Learning methodologies to construct a fully differentiable pipeline for optimizing the geometry of the Muon Collider Electromagnetic Calorimeter.
Our approach leverages...
Graph Neural Networks (GNNs) have emerged as powerful tools for particle reconstruction in high-energy physics experiments, particularly in calorimeters with irregular geometries, such as those used in the ATLAS experiment. In this work, we present a GNN-based approach to reconstruct particle showers, improve energy resolution, spatial localization, and particle identification. We discuss the...
A(i)DAPT is a program which aims to utilize AI techniques, in particular generative modeling, to support Nuclear and High Energy Physics experiments. Its purpose is to extract physics directly from data in the most complete manner possible. Generative models such GANs are employed to capture the full correlations between particles in the final state of nuclear reactions. This many-fold program...
The integration of advanced artificial intelligence techniques into astroparticle experiments marks a transformative step in data analysis and experimental design. As space missions grow increasingly complex, the adoption of AI technologies becomes critical to optimizing performance and achieving robust scientific outcomes.
This study focuses on the development of innovative AI-driven...
