Related papers: Encoding off-shell effects in top pair production …
In this paper, we introduce a new nonlinear optical channel equalizer based on Transformers. By leveraging parallel computation and attending directly to the memory across a sequence of symbols, we show that Transformers can be used…
In this paper we investigate the decoding of parallel turbo codes over the binary erasure channel suited for upper-layer error correction. The proposed algorithm performs on-the-fly decoding, i.e. it starts decoding as soon as the first…
The production of a single top quark in association with a $\mathrm{Z}$ boson ($\mathrm{t}\mathrm{Z}\mathrm{j}$ production) at the LHC is a relevant probe of the electroweak sector of the Standard Model as well as a window to possible…
Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to…
NLO QCD corrections to the process $pp \rightarrow e^+\nu_e \mu^- \bar{\nu}_{\mu} \tau^+\tau^- b \bar{b}+X$ are presented with the full off-shell effects included. The calculation includes all resonant and non-resonant Feynman diagrams,…
Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to deploy them on resource-constrained edge devices. Though…
We compute the next-to-next-to-leading order (NNLO) QCD corrections to event shape distributions and their mean values in deep inelastic lepton-nucleon scattering. The magnitude and shape of the corrections varies considerably between…
We demonstrate that the use of on-shell methods involving calculation of the discontinuity across the t-channel cut associated with the exchange of a pair of massless particles can be used to evaluate loop contributions to the…
High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the structure of the data through large-scale (pre-) training. We…
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular solutions usually suffer two key issues: 1) only focusing…
We present a systematic comparison of different approaches for the modeling of $t\bar{t}\gamma$ final states with leptonic decays at the LHC. On the one hand, we consider a complete calculation at NLO QCD accuracy which includes all…
We discuss top-quark physics at the ILC with a focus on the full off-shell processes for $t\bar{t}$ and $t\bar{t}H$ production, including top-quark decays and also leptonic $W$ decays. A special focus is on the matching of the resummed…
We simulate the hadroproduction of a top-antitop pair in association with a bottom-antibottom pair at 14 TeV LHC using the PowHel package. We use the generated events, stored according to the Les-Houches event format, to make predictions…
We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and…
We present a fully automated framework based on the FeynRules and MadGraph5 aMC@NLO programs that allows for accurate simulations of supersymmetric QCD processes at the LHC. Starting directly from a model Lagrangian that features squark and…
In the field of pattern recognition research, the method of using deep neural networks based on improved computing hardware recently attracted attention because of their superior accuracy compared to conventional methods. Deep neural…
Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed,…
Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition…
Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels…
This paper introduces the Neural Network for Nonlinear Hawkes processes (NNNH), a non-parametric method based on neural networks to fit nonlinear Hawkes processes. Our method is suitable for analyzing large datasets in which events exhibit…