arXiv subjects

Thomas Boettcher

Publications and source records attributed to Thomas Boettcher.

7 recordsLinked to original sources

arXiv2026

A converged architecture for processing 32 Tbps of physics data in real-time at the LHCb experiment

The LHCb detector at the Large Hadron Collider has been upgraded to acquire an unprecedented 32 Tbps of particle-collision data to provide new insights in the High Energy Physics domain. The data produced by the detector is filtered in real-time to select interesting collisions. As part of the upgrade, a pre-filtering stage has been removed leading to a factor 40 increase in data rate. To deal with the high throughput demands of LHCb real-time data processing, we present an off-the-shelf network architecture using zero-copy techniques in conjunction with an efficient, fully-GPU-based filter. Our converged architecture is able to process the full 32 Tbps of particle-collision data in real-time, the highest in any physics experiment to date. Our result extends the reach of the LHCb physics programme and sets a new standard for real-time data processing at particle physics experiments.

hep-ex↗

arXiv2026

Track fitting at the full LHC collision rate

The LHCb experiment at the Large Hadron Collider underwent a major upgrade before the LHC Run 3 data taking period, employing an all-software approach in its trigger system. Here we present a fast implementation of a Kalman filter, used in the first trigger stage since the 2025 data taking period, allowing to determine parameter estimates of charged-particle trajectories at the full LHCb collision frequency of 30 MHz. This approach replaces computationally expensive magnetic field map lookups and numerical integration methods with fast analytical parameterisations while maintaining the mathematical framework of Kalman filtering. Implemented on approximately 500 GPUs within the first-level trigger, the algorithm has replaced the previous partial track fitting algorithm in the real-time trigger environment at the cost of a 2% increase in processing time. Compared to the previous fitter this parameterised Kalman filter shows a significantly improved momentum resolution, resulting in a factor of two improvement in the invariant mass resolutions for reconstructed D0 and J/{\psi} hadrons. It additionally demonstrates greater robustness against detector misalignment effects and substantially sharpens the discrimination between genuine particle trajectories and accidental background, more than doubling the rejection of the latter at no cost to genuine-track efficiency, for a standard selection working point.

hep-ex↗

arXiv2024

QCD challenges from pp to AA collisions -- 4th edition

This paper is a write-up of the ideas that were presented, developed and discussed at the fourth International Workshop on QCD Challenges from pp to AA, which took place in February 2023 in Padua, Italy. The goal of the workshop was to focus on some of the open questions in the field of high-energy heavy-ion physics and to stimulate the formulation of concrete suggestions for making progresses on both the experimental and theoretical sides. The paper gives a brief introduction to each topic and then summarizes the primary results.

hep-ex↗

arXiv2024

Topological heavy-flavor tagging and intrinsic bottom at the Electron-Ion Collider

Heavy-flavor hadron production, in particular bottom hadron production, is difficult to study in deep-inelastic scattering (DIS) experiments due to small production rates and branching fractions. To overcome these limitations, a method for identifying heavy-flavor DIS events based on event topology is proposed. Based on a heavy-flavor jet tagging strategy developed for the LHCb experiment, this algorithm uses displaced vertices to identify decays of heavy-flavor hadrons. The algorithm's performance at the Electron-Ion Collider is demonstrated using simulation, and it is shown to provide discovery potential for non-perturbative intrinsic bottom quarks in the proton.

hep-ex↗

arXiv2023

Low-$x$ physics at LHCb

The LHCb detector's forward geometry provides unprecedented kinematic coverage at low Bjorken-$x$. LHCb's excellent momentum resolution, vertex reconstruction, and particle identification enable precision measurements at low transverse momentum and high rapidity in proton-lead collisions, probing $x$ as small as $10^{-6}$. In this contribution, we present recent studies of low-$x$ physics using the LHCb detector. These studies include charged hadron, neutral pion, and $D^0$ production in proton-lead collisions, as well as charmonium production in ultraperipheral lead-lead collisions. Future prospects and implications for the understanding of low-$x$ nuclear PDFs and parton saturation are also discussed.

nucl-ex↗

arXiv2022

Proceedings of the Low-$x$ 2021 International Workshop

The purpose of the Low-$x$ Workshop series is to stimulate discussions between experimentalists and theorists in diffractive hadronic physics, QCD dynamics at low $x$, parton saturation, and exciting problems in QCD at HERA, Tevatron, LHC, RHIC, and the future EIC. The central topics of the workshop, summarized in the current Proceedings, were: Diffraction in ep and e-ion collisions (including EIC physics); Diffraction and photon-exchange in hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions; Spin Physics; Low-$x$ PDFs, forward physics, and hadronic final states. This Workshop has been the XXVIII edition in the series of the workshop.

hep-ph↗

arXiv2021

Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the "toy Monte Carlo" originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments.

hep-ex↗