Fourth MODE Workshop on Differentiable Programming for Experiment Design

Session

Particle Physics

23 Sept 2024, 11:30

Valencia (Spain)

Conveners

Particle Physics

• Emma Torro Pastor (Univ. of Valencia and CSIC (ES))
• Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

Particle Physics

• Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

1. Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Jan Kaiser

23/09/2024, 11:30

Particle Physics

Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of optimisation problems pose significant challenges in generating the required data for training state-of-the-art machine learning models. In this work, we introduce Cheetah, a...

6. Identification of Particle Tracks in CMS with Neuromorphic Computing

Dr Emanuele Coradin (University of Padova)

23/09/2024, 11:50

Particle Physics

Using a spiking neural network and a modeling of the silicon tracker for the CMS upgraded detector, we demonstrate the unsupervised learning application of identification of charged particle tracks in presence of background, and characterize the detection efficiency, fake rate, and differentiation of output signals for particles of different momenta and charge.

15. Machine Learning approach to Beam Induced Background Shield at 3 TeV Muon Collider

Luca Castelli

23/09/2024, 12:10

Particle Physics

Muon collisions are considered a promising mean for exploring the energy frontier, leading to a detailed study of the possible feasibility issues. Beam intensities of the order of $10^{12}$ muons per bunch are needed to achieve the necessary luminosity, generating a high flux of secondary and tertiary particles that reach both the machine elements and the detector region. To limit the impact...

39. Stable neural network models for calorimeter optimization

Alexey Boldyrev (NRU Higher School of Economics (Moscow, Russia))

23/09/2024, 12:30

Particle Physics

The rapid development of ML and AI applications requires training a large number of models. One of the ways to organize training of them is the automated machine learning (AutoML) approach, where there is no human control over the training result. A crucial prerequisite for AutoML is the stability of the training model incorporated within it. This study presents an approach to identifying the...

44. Magnet Design Optimization with Supervised Deep Neural Networks

Florian Wolfgang Stummer (University of London (GB))

24/09/2024, 11:30

Particle Physics

...

45. A Surrogate Model to Optimize Injection Efficiency in PSI muEDM Experiment

Ritwika Chakraborty (PSI)

24/09/2024, 11:50

Particle Physics

...

46. Towards the optimization of a Muon Collider Calorimeter

Federico Nardi (Universita e INFN, Padova (IT) - LPC Clermont)

24/09/2024, 12:10

Particle Physics

Setup design plays a pivotal role in experiment development, particularly in high-energy physics, where vast temporal and spatial scales dictate the course of research for decades. Our research, embedded in the MODE Collaboration, aims to generalize Machine Learning tools for creating a differentiable pipeline capable of suggesting optimal configurations for the Muon Collider Electromagnetic...

47. Studies on detector optimisation through end-to-end surrogate models including discrete parameters.

Jan Kieseler (KIT - Karlsruhe Institute of Technology (DE))

24/09/2024, 12:30

Particle Physics