Conveners
Simulation and Generative Models: Detector Simulation
- Dalila Salamani (CERN)
- Ramon Winterhalder (Universität Heidelberg)
Simulation and Generative Models: Event and Jet Generation
- Ramon Winterhalder (Universität Heidelberg)
- Dalila Salamani (CERN)
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce high-granularity calorimeter simulations with extremely high fidelity, providing a fresh alternative to computationally expensive GEANT4 simulations, as well as other state-of-the-art fast simulation frameworks based on GANs and VAEs....
Modern high energy physics crucially relies on simulation to connect experimental observations to underlying theory. While traditional methods relying on Monte Carlo techniques produce powerful simulation tools, they prove to be computationally expensive. This is particularly true when they are applied to calorimeter shower simulation, where many particle interactions occur. The strain on...
Generative machine learning models are a promising way to efficiently amplify classical Monte Carlo generators' statistics for event simulation and generation in particle physics. The high computational cost of the simulation and the expected increase in data in the high-precision era of the LHC and at future colliders indicate that we urgently need such fast surrogate simulators. We present a...
AtlFast3 is the next generation of high precision fast simulation in ATLAS that is being deployed by the collaboration and will replace AtlFastII, the fast simulation tool that was successfully used until now. AtlFast3 combines a parametrization-based Fast Calorimeter Simulation and a new machine-learning based Fast Calorimeter Simulation based on Generative Adversarial Networks (GANs). The...
Typically, high-energy physics (HEP) data analysis heavily relies on the production and the storage of large datasets of simulated events. At the LHC, the end-to-end simulation workflow can require up to 50% of the available computing resources of an experiment. Speeding up the simulation process would be crucial to save resources that could be otherwise utilized.
In our study, we investigate...
We introduce a novel strategy for machine-learning-based fast simulators, which is the first that can be trained in an unsupervised manner using observed data samples to learn a predictive model of detector response and other difficult-to-model transformations. Across the physical sciences, a barrier to interpreting observed data is the lack of knowledge of a detector's imperfect resolution,...
There has been significant development recently in generative models for accelerating LHC simulations. Work on simulating jets has primarily used image-based representations, which tend to be sparse and of limited resolution. We advocate for the more natural ‘particle cloud’ representation of jets, i.e. as a set of particles in momentum space, and discuss four physics- and...
We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a...
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain...
A key aspect for the study of particle collisions is the comparison of the experiments data with those resulting from computer simulations, mainly obtained using Monte Carlo-based generators. However the amount of data required in simulations makes this task very time consuming. One approach to avoid this issue is by using machine learning techniques to speed up this process.
In this work,...
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim...
