scholarly journals Full Detector Simulation with Unprecedented Background Occupancy at a Muon Collider

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Nazar Bartosik ◽  
Paolo Andreetto ◽  
Laura Buonincontri ◽  
Massimo Casarsa ◽  
Alessio Gianelle ◽  
...  
Keyword(s):  
High Energy Physics ◽  
Cost Effective ◽  
High Energy ◽  
Track Reconstruction ◽  
Detector Performance ◽  
Event Reconstruction ◽  
Physics Potential ◽  
Muon Collider ◽  
Detector Simulation ◽  
The Impact

AbstractIn recent years, a Muon collider has attracted a lot of interest in the high-energy physics community, thanks to its ability of achieving clean interaction signatures at multi-TeV collision energies in the most cost-effective way. Estimation of the physics potential of such an experiment must take into account the impact of beam-induced background on the detector performance, which has to be carefully evaluated using full detector simulation. Tracing of all the background particles entering the detector region in a single bunch crossing is out of reach for any realistic computing facility due to the unprecedented number of such particles. To make it feasible a number of optimisations have been applied to the detector simulation workflow. This contribution presents an overview of the main characteristics of the beam-induced background at a Muon collider, the detector technologies considered for the experiment and how they are taken into account to strongly reduce the number of irrelevant computations performed during the detector simulation. Special attention is dedicated to the optimisation of track reconstruction with the conformal tracking algorithm in this high-occupancy environment, which is the most computationally demanding part of event reconstruction.

scholarly journals Impact of detector simulation in particle physics collider experiments - highlights

2019 ◽  
Vol 214 ◽  
pp. 02019
Author(s):  
V. Daniel Elvira
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Design And Optimization ◽  
The Cost ◽  
Detector Simulation ◽  
The Impact ◽  
Journal Submission ◽  
Energy Physics

Detector simulation has become fundamental to the success of modern high-energy physics (HEP) experiments. For example, the Geant4-based simulation applications developed by the ATLAS and CMS experiments played a major role for them to produce physics measurements of unprecedented quality and precision with faster turnaround, from data taking to journal submission, than any previous hadron collider experiment. The material presented here contains highlights of a recent review on the impact of detector simulation in particle physics collider experiments published in Ref. [1]. It includes examples of applications to detector design and optimization, software development and testing of computing infrastructure, and modeling of physics objects and their kinematics. The cost and economic impact of simulation in the CMS experiment is also presented. A discussion on future detector simulation needs, challenges and potential solutions to address them is included at the end.

scholarly journals The Impact of Microelectronics on High Energy Physics Innovation: The Role of 65 nm CMOS Technology on New Generation Particle Detectors

2021 ◽  
Vol 9 ◽  
Author(s):  
N. Demaria
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
Cmos Technology ◽  
High Energy ◽  
Main Role ◽  
Noise Power ◽  
Recent Developments ◽  
The Impact ◽  
Energy Physics

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will constitute a new frontier for the particle physics after the year 2027. Experiments will undertake a major upgrade in order to stand this challenge: the use of innovative sensors and electronics will have a main role in this. This paper describes the recent developments in 65 nm CMOS technology for readout ASIC chips in future High Energy Physics (HEP) experiments. These allow unprecedented performance in terms of speed, noise, power consumption and granularity of the tracking detectors.

scholarly journals Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

2021 ◽  
Vol 251 ◽  
pp. 03055
Author(s):  
John Blue ◽  
Braden Kronheim ◽  
Michelle Kuchera ◽  
Raghuram Ramanujan
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Event Simulation ◽  
Simulation Process ◽  
Adversarial Networks ◽  
Wide Range ◽  
Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

scholarly journals Interactions of Beams with Surroundings

2020 ◽  
pp. 183-203
Author(s):  
M. Brugger ◽  
H. Burkhardt ◽  
B. Goddard ◽  
F. Cerutti ◽  
R. G. Alia
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Fixed Target ◽  
Fixed Target Experiments ◽  
Secondary Particles ◽  
Radiation Sources ◽  
Residual Gas ◽  
The Impact ◽  
High Energy Particles ◽  
Energy Physics

AbstractWith the exceptions of Synchrotron Radiation sources, beams of accelerated particles are generally designed to interact either with one another (in the case of colliders) or with a specific target (for the operation of Fixed Target experiments, the production of secondary beams and for medical applications). However, in addition to the desired interactions there are unwanted interactions of the high energy particles which can produce undesirable side effects. These interactions can arise from the unavoidable presence of residual gas in the accelerator vacuum chamber, or from the impact of particles lost from the beam on aperture limits around the accelerator, as well as the final beam dump. The wanted collisions of the beams in a collider to produce potentially interesting High Energy Physics events also reduces the density of the circulating beam and can produce high fluxes of secondary particles.

scholarly journals Reconstruction Techniques in IceCube using Convolutional and Generative Neural Networks

2019 ◽  
Vol 207 ◽  
pp. 05005 ◽  
Author(s):  
Mirco Huennefeld
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Reconstruction Method ◽  
Network Architectures ◽  
Reconstruction Methods ◽  
Physics Potential ◽  
Reconstruction Performance ◽  
Maximum Likelihood Methods ◽  
First Results

Reliable and accurate reconstruction methods are vital to the success of high-energy physics experiments such as IceCube. Machine learning based techniques, in particular deep neural networks, can provide a viable alternative to maximum-likelihood methods. However, most common neural network architectures were developed for other domains such as image recogntion. While these methods can enhance the reconstruction performance in IceCube, there is much potential for tailored techniques. In the typical physics use-case, many symmetries, invariances and prior knowledge exist in the data, which are not fully exploited by current network architectures. Novel and specialized deep learning based reconstruction techniques are desired which can leverage the physics potential of experiments like IceCube. A reconstruction method using convolutional neural networks is presented which can significantly increase the reconstruction accuracy while greatly reducing the runtime in comparison to standard reconstruction methods in Ice- Cube. In addition, first results are discussed for future developments based on generative neural networks.

scholarly journals The TrackML high-energy physics tracking challenge on Kaggle

2019 ◽  
Vol 214 ◽  
pp. 06037
Author(s):  
Moritz Kiehn ◽  
Sabrina Amrouche ◽  
Paolo Calafiura ◽  
Victor Estrade ◽  
Steven Farrell ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Training Dataset ◽  
High Luminosity ◽  
Test Dataset ◽  
Event Reconstruction ◽  
Computer Scientists ◽  
Set Up ◽  
Energy Physics

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.

Event reconstruction in high-energy physics

Computer ◽  
1993 ◽  
Vol 26 (6) ◽  
pp. 68-77 ◽  
Author(s):  
F.J. Rinaldo ◽  
M.R. Fausey
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Event Reconstruction ◽  
Energy Physics
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