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Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We…

High Energy Physics - Phenomenology · Physics 2021-09-01 Joseph Aylett-Bullock , Simon Badger , Ryan Moodie

This letter details and discusses the next-to-leading order QCD corrections to $t$-channel electro-weak $W^+ b j$ production, where finite top-width effects are consistently taken into account. The computation is done within the aMC@NLO…

High Energy Physics - Phenomenology · Physics 2015-06-16 A. S. Papanastasiou , R. Frederix , S. Frixione , V. Hirschi , F. Maltoni

The calculation of NLO QCD corrections to the $t\bar{t}\to W^{+}W^{-}b\bar{b}\to e^{+}\nu_e \mu^{-}\bar{\nu}_{\mu}b\bar{b}$ process with complete off-shell effects, is briefly summarized. Besides the total cross section and its scale…

High Energy Physics - Phenomenology · Physics 2011-11-24 Malgorzata Worek

The production of top-quark pairs in hadronic collisions is among the most important reactions in modern particle physics phenomenology and constitutes an instrumental avenue to study the properties of the heaviest quark observed in nature.…

High Energy Physics - Phenomenology · Physics 2022-02-25 Javier Mazzitelli , Pier Francesco Monni , Paolo Nason , Emanuele Re , Marius Wiesemann , Giulia Zanderighi

A brief summary of the calculation of the NLO QCD corrections to the process pp -> e+ ve mu- v_mu bb~ j + X is reported. This provides a complete description of the process of t-tbar + jet production with leptonic decays beyond the…

High Energy Physics - Phenomenology · Physics 2016-07-01 Giuseppe Bevilacqua

Recent discrepancies between theoretical predictions and experimental data in multi-lepton plus $b$-jets analyses for the $t\bar{t}W^\pm$ process, as reported by the ATLAS collaboration, have indicated that more accurate theoretical…

High Energy Physics - Phenomenology · Physics 2021-08-04 Giuseppe Bevilacqua , Huan-Yu Bi , Heribertus Bayu Hartanto , Manfred Kraus , Jasmina Nasufi , Malgorzata Worek

The computation of convolution layers in deep neural networks typically rely on high performance routines that trade space for time by using additional memory (either for packing purposes or required as part of the algorithm) to improve…

Machine Learning · Computer Science 2018-09-28 Jiyuan Zhang , Franz Franchetti , Tze Meng Low

We present a comparative study of various approaches for modelling of the $e^+ \nu_e \mu^- \bar{\nu}_\mu b\bar{b} \gamma$ final state in $t\bar{t}\gamma$ production at the LHC. Working at the NLO in QCD we compare the fully realistic…

High Energy Physics - Phenomenology · Physics 2020-03-31 G. Bevilacqua , H. B. Hartanto , M. Kraus , T. Weber , M. Worek

We calculate the cross section of the electron scattering from a bound nucleon within light-front approximation. The advantage of this approximation is the possibility of systematic account for the off-shell effects which become essential…

Nuclear Theory · Physics 2018-10-03 Frank Vera , Misak M. Sargsian

We consider QCD radiative corrections to $W^+W^-b {\bar b}$ production with leptonic decays and massive bottom quarks at the LHC. We perform an exact next-to-leading order (NLO) calculation within the $q_T$-subtraction formalism and…

High Energy Physics - Phenomenology · Physics 2025-07-17 Luca Buonocore , Massimiliano Grazzini , Stefan Kallweit , Jonas M. Lindert , Chiara Savoini

We present an improved method for handling off-shell effects in deep inelastic nuclear scattering. With a firm understanding of the effects of the nuclear wave function, including these off-shell corrections as well as binding and…

High Energy Physics - Phenomenology · Physics 2009-10-30 C. D. Cothran , D. B. Day , S. Liuti

LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs. Such networks…

High Energy Physics - Phenomenology · Physics 2020-08-20 Anja Butter , Tilman Plehn

We investigate the observable effects of off-shell propagation of nucleons in heavy-ion collisions at SIS energies. Within a semi-classical BUU transport model we find a strong enhancement of subthreshold particle production when off-shell…

Nuclear Theory · Physics 2009-10-31 M. Effenberger , U. Mosel

The study of long-range effects arising from the higher order exchange of massless particles via summation of Feynman diagrams is well known, but recently it has been shown that the use of on-shell methods can provide a streamlined route to…

Nuclear Theory · Physics 2023-07-26 Barry R. Holstein

We discuss the production of photon pairs in hadronic collisions, from fixed target to LHC energies. The study which follows is based on a QCD calculation at full next-to-leading order accuracy, including single and double fragmentation…

High Energy Physics - Phenomenology · Physics 2011-09-13 T. Binoth , J. Ph. Guillet , E. Pilon , M. Werlen

We present state-of-the-art predictions for off-shell $t\bar{t}b\bar{b}$ production with di-lepton decays at the LHC with $\sqrt{s}=13$ TeV. Results are accurate at NLO in QCD and include all resonant and non-resonant diagrams,…

High Energy Physics - Phenomenology · Physics 2022-01-04 Giuseppe Bevilacqua

We compute the virtual next-to-leading corrections to the impact factors or off-shell coefficient functions in the high-energy limit. When combined with the known real corrections, these results will provide the complete NLO corrections to…

High Energy Physics - Phenomenology · Physics 2016-08-25 Vittorio Del Duca , Carl R. Schmidt

Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology…

High Energy Physics - Phenomenology · Physics 2023-02-20 Ryan Moodie

Spiking Neural Networks (SNNs) have gained increasing attention due to their potential for low-power computation on neuromorphic hardware. A widely adopted training strategy for SNNs is direct coding, which enable backpropagation on neuron…

Neural and Evolutionary Computing · Computer Science 2026-05-11 Nhan Trong Luu , Duong Trung Luu , Pham Ngoc Nam , Truong Cong Thang