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

• DOI: 10.1103/physrevlett.128.132002
• Publication Date: 2022-03-31T16:42:28Z
• Authors: V. Andreev; M. Arratia; A. Baghdasaryan; A. Baty; K. Begzsuren; A. Belousov; A. Bolz; V. Boudry; G. Brandt; D. Britzger; A. Buniatyan; L. Bystritskaya; A. J. Campbell; K. B. Cantun Avila; K. Cerny; V. Chekelian; Z. Chen; J. G. Contreras; L. Cunqueiro Mendez; J. Cvach; J. B. Dainton; K. Daum; A. Deshpande; C. Diaconu; G. Eckerlin; S. Egli; E. Elsen; L. Favart; A. Fedotov; J. Feltesse; M. Fleischer; A. Fomenko; C. Gal; J. Gayler; L. Goerlich; N. Gogitidze; M. Gouzevitch; C. Grab; T. Greenshaw; G. Grindhammer; D. Haidt; R. C. W. Henderson; J. Hessler; J. Hladký; D. Hoffmann; R. Horisberger; T. Hreus; F. Huber; P. M. Jacobs; M. Jacquet; T. Janssen; A. W. Jung; H. Jung; M. Kapichine; J. Katzy; C. Kiesling; M. Klein; C. Kleinwort; H. T. Klest; R. Kogler; P. Kostka; J. Kretzschmar; D. Krücker; K. Krüger; M. P. J. Landon; W. Lange; P. Laycock; S. H. Lee; S. Levonian; W. Li; J. Lin; K. Lipka; B. List; J. List; B. Lobodzinski; E. Malinovski; H.-U. Martyn; S. J. Maxfield; A. Mehta; A. B. Meyer; J. Meyer; S. Mikocki; M. M. Mondal; A. Morozov; K. Müller; B. Nachman; Th. Naumann; P. R. Newman; C. Niebuhr; G. Nowak; J. E. Olsson; D. Ozerov; S. Park; C. Pascaud; G. D. Patel; E. Perez; A. Petrukhin; I. Picuric; D. Pitzl; R. Polifka; S. Preins; V. Radescu; N. Raicevic; T. Ravdandorj; P. Reimer; E. Rizvi; P. Robmann; R. Roosen; A. Rostovtsev; M. Rotaru; D. P. C. Sankey; M. Sauter; E. Sauvan; S. Schmitt; B. A. Schmookler; L. Schoeffel; A. Schöning; F. Sefkow; S. Shushkevich; Y. Soloviev; P. Sopicki; D. South; V. Spaskov; A. Specka; M. Steder; B. Stella; U. Straumann; C. Sun; T. Sykora; P. D. Thompson; D. Traynor; B. Tseepeldorj; Z. Tu; A. Valkárová; C. Vallée; P. Van Mechelen; D. Wegener; E. Wünsch; J. Žáček; J. Zhang; Z. Zhang; R. Žlebčík; H. Zohrabyan; F. Zomer
• Keywords: electron p: deep inelastic scattering; correlation: two-particle; General Physics; data analysis method; [PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex]; neural network; Mathematical sciences; parton: distribution function; collinear; Bioengineering; transverse momentum dependence; 530; 01 natural sciences; Mathematical Sciences; energy dependence; Engineering; statistical analysis; factorization; quantum chromodynamics; 0103 physical sciences; Machine Learning and Artificial Intelligence; [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]; Mathematical Physics; H1 Collaboration; positron p: deep inelastic scattering; angular correlation; Particle and High Energy Physics; Nuclear and Plasma Physics; Physical sciences; [PHYS.HPHE] Physics [physics]/High Energy Physics - Phenomenology [hep-ph]; DESY HERA Stor; Networking and Information Technology R&D (NITRD); kinematics; [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph]; Physical Sciences; H1; numerical calculations: Monte Carlo; momentum transfer: high; experimental results

Published by APS, College Park, Md.<br/>Physical review letters 128(13), 132002 (2022). doi:10.1103/PhysRevLett.128.132002<br/>The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm (multifold), which considers eight observables simultaneously in this first application. The unfolded cross sections are compared with calculations performed within the context of collinear or transverse-momentum-dependent factorization in quantum chromodynamics as well as Monte Carlo event generators.<br/>