arXiv subjects

Christian Veelken

Publications and source records attributed to Christian Veelken.

8 recordsLinked to original sources

arXiv2024

The polarimeter vector for $τ\rightarrow 3 πν_τ$ decays

The polarimeter vector of the $τ$ represents an optimal observable for the measurement of the $τ$ spin. In this paper we present an algorithm for the computation of the $τ$ polarimeter vector for the decay channels $τ^{-} \rightarrow π^{-}π^{+}π^{-}ν_τ$ and $τ^{-} \rightarrow π^{-}π^{0}π^{0}ν_τ$. The algorithm is based on a model for the hadronic current in these decay channels, which was fitted to data recorded by the CLEO experiment.

hep-ex↗

arXiv2024

Probing entanglement and testing Bell inequality violation with $\textrm{e}^{+}\textrm{e}^{-} \rightarrow τ^{+}τ^{-}$ at Belle II

We present a feasibility study to probe quantum entanglement and Belle inequality violation in the process $\textrm{e}^{+}\textrm{e}^{-} \rightarrow τ^{+}τ^{-}$ at a center-of-mass energy of $\sqrt{s} = 10.579$ GeV. The sensitivity of the analysis is enhanced by applying a selection on the scattering angle $\vartheta$ in the $τ^{+}τ^{-}$ center-of-mass frame. We analyze events in which both $τ$ leptons decay to hadrons, using a combination of decay channels $τ^{-} \rightarrow π^{-}ν_τ$, $τ^{-} \rightarrow π^{-}π^{0}ν_τ$, and $τ^{-} \rightarrow π^{-}π^{+}π^{-}ν_τ$. The spin orientation of the $τ$ leptons in these decays is reconstructed using the polarimeter-vector method. Assuming a dataset of $200$ million $τ^{+}τ^{-}$ events and accounting for experimental resolutions, we expect the observation of quantum entanglement and Bell inequality violation by the Belle-II experiment will be possible with a significance well in excess of five standard deviations.

hep-ph↗

arXiv2023

Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms

Identifying and reconstructing hadronic $τ$ decays ($τ_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $τ_{\textrm{h}}$ represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of $τ_{\textrm{h}}$ can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful $τ_{\textrm{h}}$ identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating $τ_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $τ_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider.

hep-ex↗

arXiv2023

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.

physics.data-an↗

arXiv2022

Stitching Monte Carlo samples

Monte Carlo (MC) simulations are extensively used for various purposes in modern high-energy physics (HEP) experiments. Precision measurements of established Standard Model processes or searches for new physics often require the collection of vast amounts of data. It is often difficult to produce MC samples containing an adequate number of events to allow for a meaningful comparison with the data, as substantial computing resources are required to produce and store such samples. One solution often employed when producing MC samples for HEP experiments is to partition the phase space of particle interactions into multiple regions and produce the MC samples separately for each region. This approach allows to adapt the size of the MC samples to the needs of physics analyses that are performed in these regions. In this paper we present a procedure for combining MC samples that overlap in phase space. The procedure is based on applying suitably chosen weights to the simulated events. We refer to the procedure as "stitching". The paper includes different examples for applying the procedure to simulated proton-proton collisions at the CERN Large Hadron Collider.

physics.data-an↗

arXiv2022

Application of the matrix element method to Higgs boson pair production in the channel $\textrm{HH} \to \textrm{b}\bar{\textrm{b}}\textrm{W}\textrm{W}^{*}$ at the LHC

We apply the matrix element method (MEM) to the search for non-resonant Higgs boson pair ($\textrm{HH}$) production in the channel $\textrm{HH} \to \textrm{b}\bar{\textrm{b}}\textrm{W}\textrm{W}^{*}$ at the LHC and study the separation between the $\textrm{HH}$ signal and the large irreducible background, which arises from the production of top quark pairs ($\textrm{t}\bar{\textrm{t}}$). Our study focuses on events containing two leptons (electrons or muons) in the final state. The separation between signal and background is studied for experimental conditions characteristic for the ATLAS and CMS experiments during LHC Run $2$, using the DELPHES fast-simulation package. We find that the $\textrm{t}\bar{\textrm{t}}$ background can be reduced to a level of $0.26\%$ for a signal efficiency of $35\%$.

hep-ph↗

arXiv2021

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

The analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization (PSO) and genetic algorithm (GA), for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.

hep-ex↗

arXiv2018

Reconstruction of the mass of Higgs boson pairs in events with Higgs boson pairs decaying into four $τ$ leptons

Various theories beyond the Standard Model predict the existence of heavy resonances decaying to Higgs (H) boson pairs. In order to maximize the sensitivity of searches for such resonances, it is important that experimental analyses cover a variety of decay modes. The decay of H boson pairs to four $τ$ leptons (HH$ \to ττττ$) has not been discussed in the literature so far. This decay mode provides a small branching fraction, but also comparatively low backgrounds. We present an algorithm for the reconstruction of the mass of the H boson pair in events in which the H boson pair decays via HH$ \to ττττ$ and the $τ$ leptons subsequently decay into electrons, muons, or hadrons. The algorithm achieves a resolution of $7$-$22\%$ relative to the mass of the H boson pair, depending on the mass of the resonance.

hep-ex↗