scholarly journals Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 53
Author(s):  
Massimo Giovannozzi ◽  
Ewen Maclean ◽  
Carlo Emilio Montanari ◽  
Gianluca Valentino ◽  
Frederik F. Van der Veken
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Performance Optimization ◽  
Hadron Collider ◽  
High Energy ◽  
Beam Dynamics ◽  
Machine Learning Techniques ◽  
Nonlinear Beam ◽  
Dynamic Aperture ◽  
Advanced Analysis

A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.

scholarly journals Application of machine learning techniques at the CERN Large Hadron Collider

2020 ◽  
Author(s):  
Frederik Van Der Veken ◽  
Gabriella Azzopardi ◽  
Fred Blanc ◽  
Loic Coyle ◽  
Elena Fol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Machine Learning Techniques ◽  
Cern Large Hadron Collider ◽  
Learning Techniques

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

scholarly journals Creativity and modelling the measurement process of the Higgs self-coupling at the LHC and HL-LHC

Synthese ◽  
2021 ◽  
Author(s):  
Sophie Ritson
Keyword(s):  
Large Hadron Collider ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Measurement Process ◽  
The Self ◽  
Epistemic Value ◽  
Philosophical Study ◽  
Epistemic Function ◽  
Improved Model

AbstractThis paper provides an account of the nature of creativity in high-energy physics experiments through an integrated historical and philosophical study of the current and planned attempts to measure the self-coupling of the Higgs boson by two experimental collaborations (ATLAS and CMS) at the Large Hadron Collider (LHC) and the planned High Luminosity Large Hadron Collider (HL-LHC). A notion of creativity is first identified broadly as an increase in the epistemic value of a measurement outcome from an unexpected transformation, and narrowly as a condition for knowledge of the measurement of the self-coupling of the Higgs. Drawing upon Tal’s model-based epistemology of measurement (2012) this paper shows how without change to ‘readings’ (or ‘instrument indicators’) a transformation to the model of the measurement process can increase the epistemic value of the measurement outcome. Such transformations are attributed to the creativity of the experimental collaboration. Creativity, in this context, is both a product, a creative and improved model, and the distributed collaborative process of transformation to the model of the measurement process. For the case of the planned measurements at the HL-LHC, where models of the measurement process perform the epistemic function of prediction, creativity is included in the models of the measurement process, both as projected quantified creativity and as an assumed property of the future collaborations.

scholarly journals Correlation of Heavy and Light Flavors in Simulations

Universe ◽  
2019 ◽  
Vol 5 (5) ◽  
pp. 118
Author(s):  
Eszter Frajna ◽  
Róbert Vértesi
Keyword(s):  
Large Hadron Collider ◽  
Heavy Ion Collisions ◽  
Hadron Collider ◽  
D Meson ◽  
Heavy Ion ◽  
High Energy ◽  
Secondary Vertex ◽  
Alice Experiment ◽  
Ion Collisions ◽  
B Quarks

The ALICE experiment at the Large Hadron Collider (LHC) ring is designed to study the strongly interacting matter at extreme energy densities created in high-energy heavy-ion collisions. In this paper we investigate correlations of heavy and light flavors in simulations at LHC energies at mid-rapidity, with the primary purpose of proposing experimental applications of these methods. Our studies have shown that investigating the correlation images can aid the experimental separation of heavy quarks and help understanding the physics that create them. The shape of the correlation peaks can be used to separate the electrons stemming from b quarks. This could be a method of identification that, combined with identification in silicon vertex detectors, may provide much better sample purity for examining the secondary vertex shift. Based on a correlation picture it is also possible to distinguish between prompt and late contributions to D meson yields.

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