The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
arXiv:2105.14933
Abstract
Semi-supervision in Machine Learning can be used in searches for new physics where the signal plus background regions are not labelled. This strongly reduces model dependency in the search for signals Beyond the Standard Model. This approach displays the drawback in that over-fitting can give rise to fake signals. Tossing toy Monte Carlo (MC) events can be used to estimate the corresponding trials factor through a frequentist inference. However, MC events that are based on full detector simulations are resource intensive. Generative Adversarial Networks (GANs) can be used to mimic MC generators. GANs are powerful generative models, but often suffer from training instability. We henceforth show a review of GANs. We advocate the use of Wasserstein GAN (WGAN) with weight clipping and WGAN with gradient penalty (WGAN-GP) where the norm of gradient of the critic is penalized with respect to its input. Following the emergence of multi-lepton anomalies, we apply GANs for the generation of di-leptons final states in association with -quarks at the LHC. A good agreement between the MC and the WGAN-GP generated events is found for the observables selected in the study.
arXiv comment18 pages, 5 figures, 1 table, journal (JHEP)
References in corpus (31)
- Auto-Encoding Variational Bayes
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- A Brief Introduction to PYTHIA 8.1
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Generative Adversarial Network in Medical Imaging: A Review
- Improved Training of Wasserstein GANs
- Which Training Methods for GANs do actually Converge?
- Searching for Activation Functions
- CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
- Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
- Event Generation with Normalizing Flows
- How to GAN LHC Events
- Dijet resonance search with weak supervision using TeV collisions in the ATLAS detector
- Fast and accurate simulation of particle detectors using generative adversarial networks
- Higgs Quantum Numbers in Weak Boson Fusion
- Unfolding with Generative Adversarial Networks
- Probing the spin-parity of the Higgs boson via jet kinematics in vector boson fusion
- Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
- Deep Learning as a Parton Shower
- Lund jet images from generative and cycle-consistent adversarial networks
- Anatomy of inclusive production at hadron colliders
- Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
- LHC analysis-specific datasets with Generative Adversarial Networks
- The compatibility of LHC Run 1 data with a heavy scalar of mass around 270\,GeV
- Multi-lepton signatures of additional scalar bosons beyond the Standard Model at the LHC
- Impact of additional bosons on the exploration of the Higgs boson at the LHC
- How to GAN Event Unweighting
- Machine learning approach for the search of resonances with topological features at the Large Hadron Collider