Learning to simulate high energy particle collisions from unlabeled data.

ABSTRACT: In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theor

SUBMITTER: Howard JN 

PROVIDER: S-EPMC9085893 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature