PIPPIN: Generating variable length full events from partons

Abstract

This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between parton and reconstructed object spaces, we employ transformers, score-based models and normalizing flows. Our method tackles the inherent complexities of the stochastic transition between these two spaces and achieves remarkably accurate results. The combination of innovative techniques and the achieved accuracy demonstrates the potential of our approach in advancing the field and opens avenues for further exploration. This research contributes to the ongoing efforts in high-energy physics and generative modelling, providing a promising direction for enhanced precision in fast detector simulation.

References in corpus (27)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Parton distributions with LHC data
• LHAPDF6: parton density access in the LHC precision era
• Tuning PYTHIA 8.1: the Monash 2013 Tune
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• How to GAN LHC Events
• Score-based Generative Models for Calorimeter Shower Simulation
• Invertible Networks or Partons to Detector and Back Again
• Generative Networks for Precision Enthusiasts
• A factorisation-aware Matrix element emulator
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• Targeting Multi-Loop Integrals with Neural Networks
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• MadNIS -- Neural Multi-Channel Importance Sampling
• Learning to Simulate High Energy Particle Collisions from Unlabeled Data
• Two Invertible Networks for the Matrix Element Method
• Deep generative models for fast photon shower simulation in ATLAS
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• TURBO: The Swiss Knife of Auto-Encoders
• Set-Conditional Set Generation for Particle Physics

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