Francesco Armando Di Bello

Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer et al.

A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.

Andrea Coccaro, Francesco Armando Di Bello, Stefano Giagu et al.

Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider at CERN. In this context, we present two machine-learning algorithms for selecting events where neutral long-lived particles decay within the detector volume studying their accuracy and inference time when accelerated on commercially available Xilinx FPGA accelerator cards. The inference time is also confronted with a CPU- and GPU-based hardware setup. The proposed new algorithms are proven efficient for the considered benchmark physics scenario and their accuracy is found to not degrade when accelerated on the FPGA cards. The results indicate that all tested architectures fit within the latency requirements of a second-level trigger farm and that exploiting accelerator technologies for real-time processing of particle-physics collisions is a promising research field that deserves additional investigations, in particular with machine-learning models with a large number of trainable parameters.

Malte Algren, Tobias Golling, Francesco Armando Di Bello et al.

Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of physical processes. However, due to the sophistication of modern machine learning algorithms and their reliance on high-quality training samples, discrepancies between simulation and experimental data can significantly limit their effectiveness. In this work, we present a solution to this ``misspecification'' problem: a model calibration approach based on optimal transport, which we apply to high-dimensional simulations for the first time. We demonstrate the performance of our approach through jet tagging, using a dataset inspired by the CMS experiment at the Large Hadron Collider. A 128-dimensional internal jet representation from a powerful general-purpose classifier is studied; after calibrating this internal ``latent'' representation, we find that a wide variety of quantities derived from it for downstream tasks are also properly calibrated: using this calibrated high-dimensional representation, powerful new applications of jet flavor information can be utilized in LHC analyses. This is a key step toward allowing the unbiased use of ``foundation models'' in particle physics. More broadly, this calibration framework has broad applications for correcting high-dimensional simulations across the sciences.

Francesco Armando Di Bello, Sanmay Ganguly, Eilam Gross et al.

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.