Measurement of Lepton-Jet Correlation in Deep-Inelastic Scattering with the H1 Detector Using Machine Learning for Unfolding

Andreev, V.; Arratia, M.; Baghdasaryan, A.; Baty, A.; Begzsuren, K.; Belousov, A.; Bolz, A. E.; Boudry, V.; Brandt, G.; Britzger, D.; Buniatyan, A.; Bystritskaya, L.; Campbell, A. J.; Avila, K. B. Cantun; C̆erný, K.; Chekelian, V.; Chen, Z.; Contreras, J. G.; Cunqueiro, L.; Cvach, J.; Dainton, J. B.; Daum, K.; Deshpande, A.; Diaconu, C.; Eckerlin, G.; Egli, S.; Elsen, E.; Favart, L.; Fedotov, A.; Feltesse, J.; Fleischer, M.; Fomenko, A.; Gal, C.; Gayler, J.; Goerlich, L.; Gogitidze, N.; Gouzevitch, M.; Grab, C.; Greenshaw, T.; Grindhammer, G.; Haidt, D.; Henderson, R. C. W.; Hessler, J.; Hladký, J.; Hoffmann, D.; Horisberger, R.; Hreus, T.; Huber, Florian; Jacobs, P.; Jacquet, M.; Janssen, Tahys; Jung, A. W.; Jung, H.; Kapichine, M.; Katzy, J.; Kiesling, C.; Klein, M.; Kleinwort, C.; Klest, H. T.; Kogler, R.; Kostka, P.; Kretzschmar, J.; Krücker, D.; Krüger, K.; Landon, M. P. J.; Lange, W.; Laycock, P.; Lee, S. H.; Levonian, S.; Li, W.; Lin, J.; Lipka, K.; List, B.; List, Jenny; Łobodziński, B.; Malinovski, E.; Martyn, H. U.; Maxfield, S. J.; Mehta, A.; Meyer, A.; Meyer, J.; Mikocki, S.; Mondal, M. M.; Morozov, A.; Müller, K.; Nachman, B. P.; Naumann, T.; Newman, P. R.; Niebuhr, C.; Nowak, G.; Olsson, J. E.; Ozerov, D.; Park, S.; Pascaud, C.; Patel, G. D.; Pérez, E.; Petrukhin, A.; Pićurić, I.; Pitzl, D.; Polifka, R.; Preins, Sean; Radescu, V.; Raičević, N.; Ravdandorj, T.; Reimer, P.; Rizvi, E.; Robmann, P.; Roosen, R.; Rostovtsev, A.; Rotaru, M.; Sankey, D. P. C.; Sauter, M.; Sauvan, E.; Schmitt, S.; Schmookler, Barak; Schoeffel, L.; Schöning, A.; Sefkow, F.; Shushkevich, S.; Soloviev, Y.; Sopicki, P.; South, D.; Spaskov, V.; Specka, A.; Steder, M.; Stella, B.; Straumann, U.; Sun, ChuanLe; Sýkora, T.; Thompson, P. D.; Traynor, D.; Tseepeldorj, B.; Tu, Z.; Valkárová, A.; Vallée, C.; Mechelen, P. Van; Wegener, D.; Wünsch, E.; Žáček, J.; Zhang, J.; Zhang, Z.; Žlebčík, R.; Zohrabyan, H.; Zomer, F.

doi:10.1103/physrevlett.128.132002

Cited by 34 publications

(11 citation statements)

References 129 publications

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“…Building in a model of uncertainty would also be a central aspect of model tuning. It may also be possible to tune with unbinned, and higher-dimensional results from ep and pp data [90,[107][108][109][110][111].…”

Section: Discussionmentioning

confidence: 99%

Towards a Deep Learning Model for Hadronization

Ghosh¹,

Ju²,

Nachman³

et al. 2022

Preprint

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Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical Processing Unit (GPUs). We make the first step towards a data-driven machine learning-based hadronization model by replacing a component of the hadronization model within the Herwig event generator (cluster model) with a Generative Adversarial Network (GAN). We show that a GAN is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate this model into Herwig to generate entire events that can be compared with the output of the public Herwig simulator as well as with e + e − data.

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Section: Discussionmentioning

confidence: 99%

Towards a Deep Learning Model for Hadronization

Ghosh¹,

Ju²,

Nachman³

et al. 2022

Preprint

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“…Because classification-based and density-based unfolding techniques have distinct strengths and weaknesses, the natural next step will be to combine the two methods to benefit from both. While there is an extensive R&D program required to integrate both methodologies and to achieve precision, these tools are starting to be applied to data analysis in collider physics [107]. Looking ahead, it is clear that future versions of these tools will play an important role in the data analysis of future colliders.…”

Section: Detector Unfoldingmentioning

confidence: 99%

Machine learning and LHC event generation

et al. 2023

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First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

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“…Azimuthal decorrelation between hadrons (instead of jets) has also been studied in di-jet production in DIS and in e + e − annihilation [10,11]. Additionally, lepton-jet azimuthal decorrelation has been studied in DIS single-jet production [12,13]. The observable of interest in this paper, i.e., the azimuthal decorrelation between a Z boson and a jet produced in hadron-hadron collisions, has been studied in refs.…”

Section: Jhep10(2022)006mentioning

confidence: 99%

Azimuthal decorrelation between a jet and a Z boson at hadron colliders

Bouaziz

Delenda

Khelifa-Kerfa

2022

J. High Energ. Phys.

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We revisit the azimuthal decorrelation δϕ between a jet and a Z boson produced at hadron colliders. Employing different recombination schemes for the jets leads to significantly different NLL-resummed predictions for the distribution of this quantity. Specifically when the jets are reconstructed with the E-scheme (i.e., four-momentum addition) in the kt or anti-kt clustering algorithms, then the resummation becomes highly non-trivial due to the presence of non-global and/or clustering logarithms. We evaluate these logarithms analytically at two loops and numerically to all orders in the large-Nc limit, and present a full NLL resummation of δϕ. We extend the accuracy of the perturbative expansion of the resummed distribution at fixed order to NNLL accuracy by including $$ \mathcal{O} $$ O (αs) NLO corrections obtained with MadGraph5_aMC@NLO. We compare our findings with results of various Monte Carlo event generators and with experimental data from the CMS collaboration.

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Measurement of Lepton-Jet Correlation in Deep-Inelastic Scattering with the H1 Detector Using Machine Learning for Unfolding

Cited by 34 publications

References 129 publications

Towards a Deep Learning Model for Hadronization

Towards a Deep Learning Model for Hadronization

Machine learning and LHC event generation

Azimuthal decorrelation between a jet and a Z boson at hadron colliders

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