Muon tomography is a powerful imaging technique that leverages cosmic-ray muons to probe the internal structure of large-scale objects. However, traditional reconstruction methods, such as the Point of Closest Approach (POCA), introduce significant bias, leading to suboptimal image quality and inaccurate material characterization. To address this issue, we propose an approach based on...
GPUs have become increasingly popular for their ability to perform parallel operations efficiently, driving interest in General-Purpose GPU Programming. Scientific computing, in particular, stands to benefit greatly from these capabilities. However, parallel programming systems such as CUDA introduce challenges for code transformation tools due to their reliance on low-level hardware...
Current optimization of ground Cherenkov telescopes arrays relies on brute-force approaches based on large simulations requiring both high amount of storage and long computation time. To explore the full phase space of telescope positioning of a given array even more simulations would be required. To optimize any array layout, we explore the possibility of developing a differential program...
In modern particle detectors, calorimeters provide critical energy measurements of particles produced in high-energy collisions. The demanding requirements of next-generation collider experiments would benefit from a systematic approach to the optimization of calorimeter designs. The performance of calorimeters is primarily characterized by their energy resolution, parameterized by a...
Differentiability in detector simulation can enable efficient and effective detector optimisation. We are developing an AD-enabled detector simulation of a liquid argon time projection chamber to facilitate simultaneous detector calibration through gradient-based optimisation. This approach allows us to account for the correlations of the detector modeling parameters comprehensively and avoid...
The increasing importance of high-granularity calorimetry in particle physics origins from its ability to enhance event reconstruction and jet substructure analysis. In particular, the identification of hadronic decays within boosted jets and the application of particle flow techniques have demonstrated the advantages of fine spatial resolution in calorimeters. In this study, we investigate...
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and...
Objective:
Proton therapy is an emerging approach in cancer treatment. A key challenge is improving the accuracy of Bragg-peak position calculations, which requires more precise relative stopping power (RSP) measurements. Proton computed tomography (pCT) is a promising technique, as it enables imaging under conditions identical to treatment by using the same irradiation device and hadron...
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...
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...
Medical imaging—including X-rays and MRI scans—is crucial for diagnostics and research. However, the development and training of AI diagnostic models are hindered by limited access to large, high-quality datasets due to privacy concerns, high costs, and data scarcity. Synthetic image generation via differentiable programming has emerged as an effective strategy to augment real datasets with...
