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42nd International Conference on High Energy Physics, ICHEP 2024

作者： Connor, Patrick L.S. Center for Data and Computing in Natural Sciences Universität Hamburg Germany

A vast program of determinations of the strong coupling αs is being undertaken by CMS. These measurements exploit several QCD dominated processes that are sensitive to αs, and present different theoretical and experimental challenges. A review of the current public results and perspective is given. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

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Determination of the strong coupling from jet measurements at CMS

arXiv

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arXiv 2024年

作者： Connor, Patrick L.S. Center for Data and Computing in Natural Sciences Universität Hamburg Germany

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Recent jet measurements at CMS

Recent jet measurements at CMS

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2021 European Physical Society Conference on High Energy Physics, EPS-HEP 2021

作者： Connor, Patrick L.S. Institut für Experimentalphysik Center for Data Computing in Natural Sciences Universität Hamburg Hamburg Germany

Several measurements of jet cross sections in proton-proton collisions at 5.02 and 13 TeV with the CMS experiment are presented. Jets are reconstructed using the anti-kT clustering algorithm with R = 0.4 and R = 0.7. Double-differential measurements of inclusive jet production at 5.02 and 13 TeV are performed as a function of the jet transverse momentum and jet rapidity;furthermore, a triple-differential measurement as a function of leading jet transverse momentum, dijet azimuthal correlations, and jet multiplicity and a double-differential measurement as a function of jet multiplicity and single-jet transverse momentum are also provided for multijet production at 13 TeV. The measured jet cross sections are corrected for detector effects and compared with the predictions from perturbative QCD. Finally, a QCD interpretation of the inclusive jet measurement with R = 0.7 at 13 TeV is also presented. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Clustering algorithms

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Feature selection with distance correlation

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Physical Review D 2024年第5期109卷 054009-054009页

作者： Ranit Das Gregor Kasieczka David Shih NHETC Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA Institut für Experimentalphysik Universität Hamburg 22761 Hamburg Germany Center for Data and Computing in Natural Sciences (CDCS) 22607 Hamburg Germany

Choosing which properties of the data to use as input to multivariate decision algorithms—also known as feature selection—is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on distance correlation, and demonstrate its effectiveness on the tasks of boosted top- and W-tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters.

关键词： Phenomenology Signatures with jets Signatures with top quarks Strong interaction Machine learning Particle data analysis

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Fast Simulation of Highly Granular Calorimeters with Generative Models: Towards a First Physics Application

Fast Simulation of Highly Granular Calorimeters with Generat...

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2023 European Physical Society Conference on High Energy Physics, EPS-HEP 2023

作者： Buhmann, Erik Buss, Thorsten Diefenbacher, Sascha Eren, Engin Gaede, Frank Kasieczka, Gregor Korcari, William Korol, Anatolii Krüger, Katja Madlener, Thomas McKeown, Peter Rustige, Lennart Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany

While simulation plays a crucial role in high energy physics, it also consumes a significant fraction of the available computational resources, with these computing pressures being set to increase drastically for the upcoming high luminosity phase of the LHC and for future colliders. At the same time, the significantly higher granularity present in future detectors increases the physical accuracy required of a surrogate simulator. Machine learning methods based on deep generative models hold promise to provide a computationally efficient solution, while retaining a high degree of physical fidelity. Significant strides have already been taken towards developing these models for the generation of particle showers in highly granular calorimeters, the subdetector which constitutes the most computationally intensive part of a detector simulation. However, to apply these models to a general detector simulation, methods must be developed to cope with particles incident at various points and under varying angles in the detector. This contribution will address steps taken to tackle the challenges faced when applying these simulators in more general scenarios, as well as the effects on physics observables after interfacing with reconstruction algorithms. In particular, results achieved with bounded information bottleneck and normalising flow architectures based on regular grid geometries, as well as a more flexible diffusion model using point clouds, will be discussed. Combined with progress on integrating these surrogate simulators into existing full simulation chains, these developments bring an application to benchmark physics analyses closer. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Calorimeters

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Resonant anomaly detection without background sculpting

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Physical Review D 2023年第11期107卷 114012-114012页

作者： Anna Hallin Gregor Kasieczka Tobias Quadfasel David Shih Manuel Sommerhalder NHETC Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA Institut für Experimentalphysik Universität Hamburg 22761 Hamburg Germany Center for Data and Computing in Natural Sciences (CDCS) 22607 Hamburg Germany

We introduce a new technique named latent CATHODE (LaCATHODE) for performing “enhanced bump hunts,” a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is that it provides an anomaly score that is well behaved when evaluating it beyond the signal region, which is essential to prevent the sculpting of background distributions in the bump hunt. LaCATHODE accomplishes this by constructing the anomaly score directly in the latent space learned by a conditional normalizing flow trained on sideband regions. We demonstrate the superior stability and comparable performance of LaCATHODE for enhanced bump hunting in an illustrative toy example as well as on the LHC Olympics R&D dataset.

关键词： Hadronic decays Particle phenomena Hypothetical particles Artificial neural networks data analysis Hadron colliders Machine learning

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Inclusive jet measurements in CMS 41

Inclusive jet measurements in CMS

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41st International Conference on High Energy Physics, ICHEP 2022

作者： Connor, Patrick L.S. HamburgD-22761 Germany Center for Data Computing in Natural Sciences Universität Hamburg Notkestraße 11 HamburgD-22607 Germany

Measurements of jet production in proton-proton collisions at the LHC are crucial for precise tests of QCD, improving the understanding of the proton structure and are important tools for searches for physics beyond the standard model. We describe the most recent set of inclusive jet measurements performed using CMS data, mentioning methodological specificities with respect to other analyses. Finally, we compare them to various theoretical predictions. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

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Generative Models for Fast Simulation of Electromagnetic and Hadronic Showers in Highly Granular Calorimeters 41

Generative Models for Fast Simulation of Electromagnetic and...

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41st International Conference on High Energy Physics, ICHEP 2022

作者： Bieringer, S. Buhmann, E. Diefenbacher, S. Eren, E. Gaede, F. Kasieczka, G. Korcari, W. Korol, A. Krüger, K. McKeown, P. Rustige, L. Shekhzadeh, I. Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany

While simulation is a crucial cornerstone of modern high energy physics, it places a heavy burden on the available computing resources. These computing pressures are expected to become a major bottleneck for the upcoming high luminosity phase of the LHC and for future colliders, motivating a concerted effort to develop computationally efficient solutions. Methods based on generative machine learning models hold promise to alleviate the computational strain produced by simulation, while providing the physical accuracy required of a surrogate simulator. This contribution provides an overview of a growing body of work focused on simulating showers in highly granular calorimeters, which is making significant strides towards realising fast simulation tools based on deep generative models. Progress on the simulation of both electromagnetic and hadronic showers will be reported, with a focus on the high degree of physical fidelity achieved. Additional steps taken to address the challenges faced when broadening the scope of these simulators, such as those posed by multi-parameter conditioning, will also be discussed. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Calorimeters

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Characterizing Complex Network Properties of Knowledge Graphs 15

Characterizing Complex Network Properties of Knowledge Graph...

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15th International Conference on Knowledge Engineering and Ontology Development, KEOD 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Rossanez, Anderson da Silva Torres, Ricardo dos Reis, Julio Cesar Institute of Computing University of Campinas SP Campinas Brazil Agricultural Biosystems Engineering Wageningen Data Competence Center Wageningen University & Research Wageningen Netherlands Department of ICT and Natural Sciences Norwegian University of Science and Technology Ålesund Norway

ISBN: (纸本)9789897586712

Knowledge Graphs have been established as one the most relevant representations to encode knowledge, with relevant applications in the public and private sectors. One common research direction concerning the analysis of created knowledge graphs relies on the assumption that their intrinsic properties and structure are similar to what is observed in complex networks. However, studies concerning identifying typical complex network structures in knowledge graphs are lacking in the literature. This paper bridges this gap by analyzing commonly and recently used knowledge graphs in the semantic web field, seeking to demonstrate their complex network properties. Evaluation involving DBpedia and Wikidata data confirms the occurrence of intrinsic complex network structures in their respective knowledge graphs. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Complex networks

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Tree-based algorithms for weakly supervised anomaly detection

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Physical Review D 2024年第3期109卷 034033-034033页

作者： Thorben Finke Marie Hein Gregor Kasieczka Michael Krämer Alexander Mück Parada Prangchaikul Tobias Quadfasel David Shih Manuel Sommerhalder Institut für Theoretische Teilchenphysik und Kosmologie RWTH Aachen University 52074 Aachen Germany Institut für Experimentalphysik Universität Hamburg 22761 Hamburg Germany Center for Data and Computing in Natural Sciences (CDCS) 22607 Hamburg Germany NHETC Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA

Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as dijet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. In this paper, we show that using boosted decision trees as classifiers in weakly supervised anomaly detection gives superior performance compared to deep neural networks. Boosted decision trees are well known for their effectiveness in tabular data analysis. Our results show that they not only offer significantly faster training and evaluation times, but they are also robust to a large number of noisy input features. By using advanced gradient boosted decision trees in combination with ensembling techniques and an extended set of features, we significantly improve the performance of weakly supervised methods for anomaly detection at the LHC. This advance is a crucial step toward a more model-agnostic search for new physics.

关键词： Hadronic decays Particle production Hypothetical particles data analysis Hadron colliders Machine learning

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