Even Faster Simulations with Flow Matching: A Study of Zero Degree Calorimeter Responses
This work addresses computational bottlenecks for high-energy physics researchers by providing faster simulations with minimal accuracy trade-offs, though it is incremental as it builds on existing flow matching methods.
The paper tackles the problem of accelerating high-energy physics simulations by applying flow matching to generate surrogate models for zero degree calorimeters in the ALICE experiment, achieving state-of-the-art fidelity with a Wasserstein distance of 1.27 for neutron detectors and 1.30 for proton detectors while reducing inference time to as low as 0.026 ms per sample.
Recent advances in generative neural networks, particularly flow matching (FM), have enabled the generation of high-fidelity samples while significantly reducing computational costs. A promising application of these models is accelerating simulations in high-energy physics (HEP), helping research institutions meet their increasing computational demands. In this work, we leverage FM to develop surrogate models for fast simulations of zero degree calorimeters in the ALICE experiment. We present an effective training strategy that enables the training of fast generative models with an exceptionally low number of parameters. This approach achieves state-of-the-art simulation fidelity for both neutron (ZN) and proton (ZP) detectors, while offering substantial reductions in computational costs compared to existing methods. Our FM model achieves a Wasserstein distance of 1.27 for the ZN simulation with an inference time of 0.46 ms per sample, compared to the current best of 1.20 with an inference time of approximately 109 ms. The latent FM model further improves the inference speed, reducing the sampling time to 0.026 ms per sample, with a minimal trade-off in accuracy. Similarly, our approach achieves a Wasserstein distance of 1.30 for the ZP simulation, outperforming the current best of 2.08. The source code is available at https://github.com/m-wojnar/faster_zdc.