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Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-order structures that…

Machine Learning · Computer Science 2025-05-22 Jingzhe Liu , Zhigang Hua , Yan Xie , Bingheng Li , Harry Shomer , Yu Song , Kaveh Hassani , Jiliang Tang

Lightly triggered events may yield surprises about the nature of "soft" particle production at LHC energies. I suggest that event displays in coordinates matched to the dynamics of particle production (rapidity and transverse momentum) may…

High Energy Physics - Phenomenology · Physics 2022-03-02 Chris Quigg

We show that lepton-pair production in Virtual Compton Scattering offers, through interference with the well-known Bethe-Heitler process, a sensitive probe to learn the longitudinal response of resonances and the electromagnetic nucleon…

Nuclear Theory · Physics 2016-08-15 A. Yu. Korchin , O. Scholten , F. de Jong

We present a Monte Carlo generator that implements significant theoretical improvements in the simulation of top-quark pair production and decay at the LHC. Spin correlations and off-shell effects in top-decay chains are described in terms…

High Energy Physics - Phenomenology · Physics 2017-02-01 Tomáš Ježo , Jonas M. Lindert , Paolo Nason , Carlo Oleari , Stefano Pozzorini

Data-enabled predictive control (DeePC) for linear systems utilizes data matrices of recorded trajectories to directly predict new system trajectories, which is very appealing for real-life applications. In this paper we leverage the…

Optimization and Control · Mathematics 2024-12-20 Mircea Lazar

We present NLO electroweak corrections to Higgs production in association with off-shell top-antitop quark pairs. The full process $\text{p}\text{p}\to\text{e}^+\nu_{\text{e}} \mu^-\bar{\nu}_\mu\text{b}\bar{\text{b}} \text{H}$ is…

High Energy Physics - Phenomenology · Physics 2017-02-15 Ansgar Denner , Jean-Nicolas Lang , Mathieu Pellen , Sandro Uccirati

This paper explores the integration of Diophantine equations into neural network (NN) architectures to improve model interpretability, stability, and efficiency. By encoding and decoding neural network parameters as integer solutions to…

Machine Learning · Computer Science 2024-09-12 Ronald Katende

A new linked cluster expansion for the calculation of ground state observables of complex nuclei with realistic interactions has been developed [1-3]; using the V8' potential [4] the ground state energy, density and momentum distribution of…

Nuclear Theory · Physics 2007-05-23 M. Alvioli , C. Ciofi degli Atti , I. Marchino , H. Morita

In view of the persisting tension between theoretical predictions and the LHC data for the $pp \to t\bar{t}W^\pm$ production process, we present the state-of-the-art full off-shell NLO QCD result for $pp \to t\bar{t}W^+\, j+X$. We…

High Energy Physics - Phenomenology · Physics 2023-09-08 Huan-Yu Bi , Manfred Kraus , Minos Reinartz , Malgorzata Worek

We study the hadroproduction of a $Wb$ pair in association with a light jet, focusing on the dominant $t$-channel contribution and including exactly at the matrix-element level all non-resonant and off-shell effects induced by the finite…

High Energy Physics - Phenomenology · Physics 2016-06-29 Rikkert Frederix , Stefano Frixione , Andrew S. Papanastasiou , Stefan Prestel , Paolo Torrielli

We report on the first computation of the next-to-next-to-leading order (NNLO) QCD corrections to $W^{\pm}Z$ production in proton collisions. We consider both the inclusive production of on-shell $W^{\pm}Z$ pairs at LHC energies and the…

High Energy Physics - Phenomenology · Physics 2016-08-17 Massimiliano Grazzini , Stefan Kallweit , Dirk Rathlev , Marius Wiesemann

We present details of a calculation of the cross section for hadronic top-antitop production in next-to-leading order (NLO) QCD, including the decays of the top and antitop into bottom quarks and leptons. This calculation is based on matrix…

High Energy Physics - Phenomenology · Physics 2012-11-07 Ansgar Denner , Stefan Dittmaier , Stefan Kallweit , Stefano Pozzorini

Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, we train the NNs to predict the time-dependent dipole and spectra of HHG emission from…

Optics · Physics 2023-03-07 M. Lytova , M. Spanner , I. Tamblyn

Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process that works off-line on pre-recorded simulation data. This…

Neural and Evolutionary Computing · Computer Science 2017-07-12 Joseph Chrol-Cannon , Yaochu Jin , André Grüning

Higher-order tree-level processes in strong laser fields, i.e. cascades, are in general extremely difficult to calculate, but in some regimes the dominant contribution comes from a sequence of first-order processes, i.e. nonlinear Compton…

High Energy Physics - Phenomenology · Physics 2020-08-05 Victor Dinu , Greger Torgrimsson

In these proceedings we discuss the importance of higher-order corrections and off-shell effects for the calculation of signal strength exclusion limits in $t\bar{t}+$DM searches at the Large Hadron Collider. We present limits for the…

High Energy Physics - Phenomenology · Physics 2022-01-10 Jonathan Hermann

With the presence of online collaborative tools for software developers, source code is shared and consulted frequently, from code viewers to merge requests and code snippets. Typically, code highlighting quality in such scenarios is…

Software Engineering · Computer Science 2022-08-05 Marco Edoardo Palma , Pasquale Salza , Harald C. Gall

We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of…

High Energy Physics - Phenomenology · Physics 2022-03-25 Ijaz Ahmed , Anwar Zada , Muhammad Waqas , M. U. Ashraf

The increasing complexity of neural networks and the energy consumption associated with training and inference create a need for alternative neuromorphic approaches, e.g. using optics. Current proposals and implementations rely on physical…

Optics · Physics 2023-08-31 Clara C. Wanjura , Florian Marquardt

We compare fixed order and parton shower matched predictions for the process ${pp\rightarrow \ell^+\nu_{\ell} \ell^-\bar{\nu}_{\ell} \ell^{\pm}{\nu}_{\ell} b\bar{b}+X}$ at NLO in QCD, including the orders $\mathcal{O}(\alpha_s^3\alpha^6)$…

High Energy Physics - Phenomenology · Physics 2022-12-06 Jasmina Nasufi
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