Machine Learning-Based Event Generator

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers. In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the efforts ignore faithful reproductions, and detector effects due to their complexity and rely on theories for detector simulations. In this work, we present a new machine learning-based event generator framework free of theoretical particle dynamics assumption. We first create a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) that selects a set of transformed features to faithfully reproduce simulated and experimental data. Then, we extend FATGAN by conditioning the component neural networks according to the given reaction energy and develop a Conditional FAT-GAN (cFAT-GAN) that can generate events at unrelated beam energies. Next, we implement a conditional folding model that learns the correlations between vertex-level and detector-level events and simulates the distortions produced by the detector machines. The folding model is then integrated into a generator to reconstruct vertex-level events. This serves as a practical framework in a real experimental analysis where such effects must be incorporated. We finally evaluate different Neural Network architectures and use machine learning techniques for model interpretation and evaluation. In addition, we analyze the GANs latent variables to extract physics resonance regions, illustrating the ability of the developed model to distinguish between the underlying physics mechanisms. This framework has been validated on simulated inclusive deep-inelastic scattering data along with the existing parametrizations for detector simulation. The generated results provide a realistic proof of concept for designing a machine learning-based event generator that will be a valuable tool in nuclear and particle physics programs to facilitate the studies of high-energy scattering reactions and understand different physical mechanisms

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.Comment: 11 pages, 8 figure

Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.Comment: AAAI-MLPS 2021 Spring Symposium at Stanford Universit

Machine learning at the energy and intensity frontiers of particle physics

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics

Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN

This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research)