Conveners
Applications in Particle Physics: Part 1
- Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
Applications in Particle Physics: Part 2
- Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
Applications in Particle Physics: Part 4
- Ruben Lopez Ruiz (Universidad de Cantabria and CSIC (ES))
Applications in Particle Physics: Part 3
- Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
Applications in Particle Physics: Part 4
- Federico Nardi (Universita e INFN, Padova (IT))
Accurate detector simulations are key components of any measurement or search for new physics. Due to their stochastic nature, ML-based generative models are natural opportunities for fast, differentiable simulations. We present two such graph- and attention-based models for generating LHC-like data using sparse and efficient point cloud representations, with state-of-the-art results. We...
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks, both in space and time. A reduction in pixel size by a factor of four in next-generation detectors will lead to unprecedented data rates, exceeding those foreseen at the High Luminosity Large Hadron Collider. Despite this increase in data volume, smart data reduction within the pixelated...
Differentiable Programming could open even more doors in HEP analysis and computing to Artificial Intelligence/Machine Learning. Current common uses of AI/ML in HEP are deep learning networks – providing us with sophisticated ways of separating signal from background, classifying physics, etc. This is only one part of a full analysis – normally skims are made to reduce dataset sizes by...
Long-lived hadrons have different cross sections with nuclear matter, and they give rise to different reactions when they interact in dense media. Until now, calorimeters have not been designed to try and exploit these differences for particle identification; yet that information would be highly beneficial in detectors at future facilities.
In this presentation we will explore the...
In this work, we use machine learning to optimize the design of a hadronic calorimeter to be used in the upcoming Electron Ion collider to be built in Long Island’s Brookhaven National Laboratory over the next decade. We use a full GEANT4 simulation of the calorimeter to train surrogate models that are conditional on the set parameters. We use a deep neural network trained to predict the...
The Geant4 particle transport simulation toolkit, widely used in high energy and nuclear physics, biomedical, space science, etc. applications, will be introduced briefly. The main concepts, design choices and interfaces that enable to simulate the passage of different particles through complex geometrical setups, while modelling their interactions with rather diverse characteristics, will...
Simulating high-resolution detector responses is a
storage-costly and computationally intensive process that has long
been challenging in particle physics. Despite the ability of deep
generative models to make this process more cost-efficient,
ultra-high-resolution detector simulation still proves to be difficult
as it contains correlated and fine-grained mutual information within...
