Article
Physics, Multidisciplinary
M. Czakon, R. Harlander, J. Klappert, M. Niggetiedt
Summary: In this Letter, the impact of the finite top-quark mass on the inclusive Higgs production cross section at higher perturbative orders was calculated at next-to-next-to-leading order QCD. The overall effect was found to be -0.26% at a pp collider energy of 13 TeV, and -0.1% at 8 TeV, eliminating one of the main theoretical uncertainties to inclusive Higgs production cross section at the LHC.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Particles & Fields
Luca Buonocore, Massimiliano Grazzini, Juerg Haag, Luca Rottoli
Summary: In this study, we investigate the associated production of a vector or Higgs boson with a jet in hadronic collisions. When the transverse momentum of the boson jet system is much smaller than its invariant mass, the QCD perturbative expansion is affected by large logarithmic terms which need to be resummed to all orders. We discuss the structure of the resummation of logarithmically enhanced contributions up to next-to-leading logarithmic accuracy and provide explicit analytical results for the resummation coefficients.
EUROPEAN PHYSICAL JOURNAL C
(2022)
Article
Physics, Multidisciplinary
N. Bethencourt de Leon, G. Chachamis, A. Sabio Vera
Summary: Multi-particle production studies have been a crucial tool in understanding the strong force for decades. With the advancements in hadron colliders, particularly the Large Hadron Collider (LHC), the focus has shifted towards studying multi-jet final states. This paper compares the predictions of the old phenomenological Chew-Pignotti model with the QCD-based BFKL model for multi-jet final states. The results show differences in single jet rapidity distributions and jet-jet rapidity correlations.
Article
Physics, Particles & Fields
Zhong-Bo Kang, Kyle Lee, Ding Yu Shao, John Terry
Summary: The study focuses on the single spin asymmetry in back-to-back dijet production in transversely polarized proton-proton collisions, generated by Sivers functions in incoming polarized protons. A QCD formalism using transverse momentum dependent parton distribution functions is proposed to resum large logarithms in perturbative calculations. Predictions are made for the Sivers asymmetry of hadronic dijet production at RHIC, with spin asymmetries computed in selected jet charge bins to separate contributions from u- and d-quark Sivers functions, showing rough consistency with preliminary results from the STAR collaboration.
JOURNAL OF HIGH ENERGY PHYSICS
(2021)
Article
Astronomy & Astrophysics
Hua-Xing Chen
Summary: In this study, the interpolating currents and decay constants of D(*())(D) over bar (()*()), D-(*())(K) over bar (()*()), and D-(*D-)(s)(*()-) hadronic molecular states were calculated using QCD sum rules. The relative production rates in B and B* decays and relative branching ratios were then calculated through current algebra and Fierz rearrangement.
Article
Astronomy & Astrophysics
Hana Benslama, Yazid Delenda, Kamel Khelifa-Kerfa
Summary: This study investigates non-global and clustering logarithms in the distribution of the azimuthal decorrelation between two jets in e+e- -> dijet events. The leading global single logarithms in the distribution are calculated at one loop and to all orders. The non-global and clustering logarithms are also computed up to four loops at finite Nc and numerically resummed to all orders in the large-Nc approximation. The impact of non-global logarithms on the resummed distribution is found to be substantial in the anti-kt algorithm, but significantly smaller in the kt algorithm.
Article
Astronomy & Astrophysics
D. Kantzas, S. Markoff, M. Lucchini, C. Ceccobello, K. Chatterjee
Summary: The origin of cosmic rays remains unclear, but jets launched by supermassive black holes and stellar-mass black holes in X-ray binaries are among the candidate sources for cosmic ray acceleration. The acceleration of cosmic rays in astrophysical jets leads to the production of gamma-rays and neutrinos. To address the energy supply issue in cosmic ray acceleration and the evolution of energy flux along the flows, a novel treatment including hadronic content is explored.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2023)
Article
Astronomy & Astrophysics
Zoltan Nagy, Davison E. Soper
Summary: The proposed method aims to examine how a parton shower sums large logarithms, by working with integral transforms and reformulating the shower to obtain the transformed distribution as an exponential. This program was applied to the thrust distribution in electron-positron annihilation, with the approach of computing some perturbative coefficients in the exponent and testing their consistency with next-to-leading-log summation of the thrust logarithms.
Article
Astronomy & Astrophysics
Hai-Yang Cheng, Cheng-Wei Chiang, Zhi-Qing Zhang
Summary: In this study, we investigate the three-body D decays proceeding through intermediate tensor resonances and the quasi-two-body D-TP decays. We find that the decay rates calculated using light cone sum rules and the covariant light-front quark model show better agreement with experimental data. We also discover that, unlike three-body B decays, the tensor-mediated D decays are more affected by finite-width effects and require corrections.
Article
Physics, Particles & Fields
Jannis Lang, Stefan Liebler, Heiko Schaefer-Siebert, Dieter Zeppenfeld
Summary: Effective field theories are used to parameterize effects of beyond standard model physics in vector boson scattering, but their validity range may be limited at high energy. By studying UV-complete toy models, it is found that dimension-eight operators are crucial for an adequate description of models, but significant effects are only seen outside the validity range of EFT.
EUROPEAN PHYSICAL JOURNAL C
(2021)
Article
Astronomy & Astrophysics
Edward Shuryak, Ismail Zahed
Summary: This paper is part of our series of papers on quark models of hadronic structure on the light front, motivated by the QCD vacuum structure and lattice results. The focus of this paper is on the importance of diquark correlations, which are described by a quasilocal four-fermion effective 't Hooft interaction induced by instantons. This same interaction is also shown to generate extra quark-antiquark pairs of the sea. The inclusion of higher order iteration through pion mediation, along with the quark-antiquark pairs, provide a quantitative description of the observed flavor asymmetry of antiquarks sea. Finally, the paper discusses the final step needed to bridge the gap between hadronic spectroscopy and parton observables, which is the forward DGLAP evolution towards the chiral upper scale of similar to 1 GeV2.
Article
Physics, Particles & Fields
Jeremy Baron, Daniel Reichelt, Steffen Schumann, Niklas Schwanemann, Vincent Theeuwes
Summary: Soft-drop grooming of hadron-collision final states has the potential to reduce the impact of non-perturbative corrections, enabling more direct comparison of accurate perturbative predictions with experimental measurements. The study shows that soft-drop grooming is efficient in removing the underlying event, motivating future experimental measurements to be compared with precise QCD predictions and constrain non-perturbative models in Monte Carlo simulations.
JOURNAL OF HIGH ENERGY PHYSICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Martin Rohrmoser
Summary: This paper presents a Monte Carlo algorithm and program to obtain parton jets formed in a Quark Gluon Plasma through multiple scatterings and coherent medium induced radiations. The program takes into account the increase in momentum components transverse to the jet-axis and energy loss due to these processes. The program provides distributions of jet-particles as a function of their emission time.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Physics, Multidisciplinary
Xiu-Wu Wang, Zhi-Gang Wang, Guo-Liang Yu, Qi Xin
Summary: In this study, color singlet-singlet-type five-quark currents were constructed to explore pentaquark states using quantum chromodynamics sum rules for the first time. The numerical results support the identification of certain states as pentaquark states, and indicate that states with higher isospin have slightly larger masses. Observations of high pentaquark candidates in the J/psi Delta invariant mass spectrum could provide insights on the nature of these states and help distinguish between different scenarios.
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
(2022)
Article
Physics, Nuclear
Gorazd Cvetic, Reinhart Koegerler
Summary: We present an updated version of a QCD coupling that satisfies various physical conditions, from high momenta to low momenta, and apply it to the analysis of semihadronic tau decay and muon anomalous magnetic moment. By evaluating the Adler function and regulating V-channel higher-twist OPE terms, we achieve a correct value a(mu)(had(1)). This analysis leads to a restriction on the value of the QCD coupling constant alpha(s)( M-Z(2); (MS) over bar) between 0.1171 and 0.1180, ensuring acceptable fit quality to various sum rules.
JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
(2021)
Review
Physics, Multidisciplinary
Gregor Kasieczka, Benjamin Nachman, David Shih, Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H. Collins, Biwei Dai, Felipe F. De Freitas, Barry M. Dillon, Ioan-Mihail Dinu, Zhongtian Dong, Julien Donini, Javier Duarte, D. A. Faroughy, Julia Gonski, Philip Harris, Alan Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, Luc Le Pottier, Pablo Martin-Ramiro, Andrej Matevc, Eric Metodiev, Vinicius Mikuni, Christopher W. Murphy, Ines Ochoa, Sang Eon Park, Maurizio Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Uro Seljak, Aleks Smolkovic, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu-Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, Daniel Williams, Mikaeel Yunus
Summary: The LHC Olympics 2020 challenge introduces a new paradigm for data-driven, model-agnostic new physics searches by leveraging anomaly detection and machine learning techniques. Participants developed and tested their methods using modern machine learning tools on a standard dataset, resulting in impressive outcomes.
REPORTS ON PROGRESS IN PHYSICS
(2021)
Article
Physics, Multidisciplinary
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Summary: This paper presents a machine learning framework for performing frequentist maximum likelihood inference with Gaussian uncertainty estimation, which also quantifies the mutual information between the unobservable and measured quantities. By extracting jet energy corrections and resolution factors from a simulation of the CMS detector, the framework achieves an improvement in jet resolution.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
Patrick T. Komiske, Ian Moult, Jesse Thaler, Hua Xing Zhu
Summary: Jets of hadrons produced at high-energy colliders provide experimental access to the dynamics of quarks and gluons and their confinement into hadrons. In this study, the high energies and exceptional resolution of the LHC allowed the direct measurement of multipoint correlation functions of energy flow operators within jets. By reformulating jet substructure in terms of these correlators, new ways of investigating the dynamics of QCD jets were found, enabling the direct imaging of the transition from confinement to free hadrons and precise measurements of quark and gluon properties and interactions. This study demonstrates the untapped potential of high-quality LHC data sets in advancing our understanding of QCD dynamics.
PHYSICAL REVIEW LETTERS
(2023)
Article
Physics, Multidisciplinary
Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Goncalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Hoche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janssen, Stefan Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kroeninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winerhalder, Jure Zupan
Summary: First-principle simulations play a crucial role in high-energy physics research, connecting the data output of multipurpose detectors with fundamental theory predictions. This review demonstrates the various applications of modern machine learning in event generation and simulation-based inference, showing conceptual developments driven by the specific requirements of particle physics. The development of new ideas and tools 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.
Article
Physics, Particles & Fields
Samuel Alipour-fard, Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
Summary: This paper introduces a new continuous jet grooming method, Piranha, which overcomes the discontinuity and infrared sensitivity issues of traditional hard-cutoff methods. We explain the principle of Piranha from the perspective of optimal transport and Energy Mover's Distance, and use Apollonius and Voronoi subtraction as examples. We also propose a new tree-based implementation, Recursive Subtraction, to reduce computational costs. Finally, we demonstrate the performance of Recursive Subtraction in mitigating soft distortions and additive contamination.
JOURNAL OF HIGH ENERGY PHYSICS
(2023)
Article
Physics, Particles & Fields
Andrew J. Larkoski, Jesse Thaler
Summary: By quantifying the distance between collider events, the data analysis can be reframed as computational geometry. One approach is to represent events as energy flow on a celestial sphere and define the metric in terms of optimal transport. In this paper, the authors propose using a spectral function to represent events, which enables a metric distance based on one-dimensional optimal transport. This approach incorporates isometries of the data and allows for first-principles calculations. The authors also speculate on the potential use of the spectral approach in quantum field theories.
JOURNAL OF HIGH ENERGY PHYSICS
(2023)
Article
Astronomy & Astrophysics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Summary: This paper highlights the issue of prior dependence in machine learning calibration strategies and discusses how both simulation-based and data-based calibrations can inherit properties from the training sample, leading to biases in the results. While the recently proposed Gaussian Ansatz approach can help avoid these issues in simulation-based calibration, achieving prior-independent data-based calibration remains an open problem.
Article
Astronomy & Astrophysics
Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Summary: We studied quark and gluon jets separately using public collider data from the CMS experiment. By employing jet topic modeling, we extracted individual distributions for the maximally separable categories. We determined the fractions of quark jets in each sample by considering different methods for extracting reducibility factors. We also mitigated detector effects using the OMNIFOLD method for central value unfolding.
Article
Astronomy & Astrophysics
Andrea Delgado, Jesse Thaler
Summary: This study benchmarks quantum annealing strategies for jet clustering based on optimizing a quantity called thrust in electron-positron collision events. The results show that quantum annealing performs similarly to exact classical approaches and classical heuristics, after tuning the annealing parameters. Comparable performance can be achieved through a hybrid quantum/classical approach without tuning parameters.
Article
Astronomy & Astrophysics
Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
Summary: This paper introduces a method to enhance the performance of annealing algorithms by using degeneracy engineering, illustrated through the example of l(0)-norm regularization for sparse linear regression. The results show that degeneracy engineering substantially improves the annealing performance, motivating its application to various regularized optimization problems.
Article
Astronomy & Astrophysics
Krish Desai, Benjamin Nachman, Jesse Thaler
Summary: This paper provides a rigorous statistical definition of dataset symmetries and proposes SymmetryGAN, a deep learning method based on generative adversarial networks, for automatically discovering symmetries. It also considers procedures to infer the underlying symmetry group from empirical data.
Article
Astronomy & Astrophysics
Benjamin Nachman, Jesse Thaler
Summary: The paper introduces neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly important in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information.
Article
Astronomy & Astrophysics
Benjamin Nachman, Jesse Thaler
Summary: The study examines the relationship between single-event classifiers and multievent classifiers in the context of collider physics, exploring how optimal classifiers can be built from either type. While training a single-event classifier was found to be more effective in the studied cases, it is suggested that multievent classifiers may hold potential value in scenarios involving approximate independence, such as jet substructure studies.
Article
Physics, Nuclear
Jasmine Brewer, Jesse Thaler, Andrew P. Turner
Summary: This study demonstrates a data-driven method for separating quark and gluon contributions to jet observables using topic modeling, showing potential for experimental determination of quark- and gluon-jet modifications in heavy-ion collisions.
Article
Astronomy & Astrophysics
Taylor Faucett, Jesse Thaler, Daniel Whiteson
Summary: The study introduces a technique to translate a black-box machine-learned classifier into human-interpretable observables for classification decisions. It evaluates the similarity of these observables to the black box decisions using a newly introduced metric. This method simplifies the machine learning strategy and provides results with a clear physical interpretation.
