The rapidly increasing demands of LHC physics pose a major challenge for Monte Carlo simulation. With the High-Luminosity LHC expected to deliver an order of magnitude more data, generating sufficiently large and high-fidelity simulated datasets will become increasingly computationally demanding. Recent developments demonstrate that generative machine learning can provide substantial...
Fast simulation of calorimeter showers is critical for the High-Luminosity LHC. Traditional Monte Carlo methods are computationally prohibitive for Run 4 of the LHC. We present DSSGAN3D (Directional State Space GAN), a generative adversarial network for volumetric simulations evaluated on the CaloChallenge benchmark (electron showers).
The GAN generator is conditioned on layer-wise energy...
Designing the next generation colliders and detectors involves solving optimization problems in high-dimensional spaces where the optimal solutions may nest in regions that human experts would normally not explore. Meanwhile, the staggering simulation demands of existing and future high-energy physics facilities call for a new paradigm for event generation and reconstruction.
Differentiable...
High-fidelity Monte Carlo simulations (e.g., Geant4, CORSIKA, Pythia) are indispensable in high-energy and medical physics. However, aligning simulated and experimental distributions remains a laborious task, traditionally relying on manual selection tuning or computationally expensive grid scans. This challenge is compounded when quality selection cuts must be jointly optimized across a...
Electron-impact processes are fundamental to analytical chemistry, plasma physics, radiation science, and astrochemistry, yet experimental measurements remain costly and incomplete. We present machine learning approaches for predicting two key electron-impact observables directly from molecular structure: electron ionization mass spectra (EI-MS) and total electron-impact ionization...
