Learning to simulate high energy particle collisions from unlabeled data
Jan 21, 2021
18 pages
Published in:
- Sci.Rep. 12 (2022) 7567
- Published: May 9, 2022
e-Print:
- 2101.08944 [hep-ph]
View in:
Citations per year
Abstract: (Springer)
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 theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the
space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential
to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle
physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.Note:
- Accepted by Scientific Reports; Changes: Updated title and abstract, rearranged order of sections, added section 4.2, Figure 2, supplementary ablation study, and supplementary figures 2-4; 32 pages, 12 figures, 4 tables
- network
- top: semileptonic decay
- Z0: decay
- machine learning
- Monte Carlo
- statistical analysis
- numerical calculations
- new physics
References(47)
Figures(24)
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