Conveners
Session 3
- Rudiger Haake (CERN)
- Lorenzo Moneta (CERN)
This is a merger of three individual contributions:
- https://indico.cern.ch/event/668017/contributions/2947026/
- https://indico.cern.ch/event/668017/contributions/2947027/
- https://indico.cern.ch/event/668017/contributions/2947028/
Simulating detector response for the Monte Carlo-generated
collisions is a key component of every high-energy physics experiment.
The methods used currently for this purpose provide high-fidelity re-
sults, but their precision comes at a price of high computational cost.
In this work, we present a proof-of-concept solution for simulating the
responses of detector clusters to particle...
In this contribution, we present a method for tuning perturbative parameters in Monte Carlo simulation using a classifier loss in high dimensions. We use an LSTM trained on the radiation pattern inside jets to learn the parameters of the final state shower in the Pythia Monte Carlo generator. This represents a step forward compared to unidimensional distributional template-matching methods.
Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We will describe an R&D activity, aimed at providing a...
The increased instantaneous luminosity at HL-LHC will raise the computing requirements for event reconstruction and analysis for current LHC-based experiments, hence limiting the available resources for the simulation of particles traversing matter. Developments of the performance of state-of-the-art simulation frameworks such as Geant4 are proceeding but are unlikely to fully compensate for...
Developing and building an analysis in high energy particle physics requires a large amount of simulated events. Simulations at the LHC are usually complex and computationally intensive due to sophisticated detector architectures. In this context, Generative Adversarial Networks (GANs) have recently caught a wide interest. GANs can learn to generate complex data distributions and produce...
