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Michael Peters

Publications and source records attributed to Michael Peters.

18 recordsLinked to original sources

Analysis note: Long-range near-side correlation in $e^+e^-$ with $W$-boson-pair events at 183-209 GeV with ALEPH archived data

Events characterized by a high multiplicity of charged particles have been a central focus in the study of collective behavior across both large and small collision systems. A previous measurement of two-particle angular correlations in $e^+e^-$ collisions at center-of-mass energies up to $\sqrt{s} = 209$ GeV, using LEP2 data, revealed discrepancies with Monte Carlo (MC) predictions at high multiplicity, suggesting the possible emergence of long-range near-side correlations even in the simplest collision system. Unlike at lower energies, where quark-antiquark production dominates, $W^+W^-$ processes become increasingly important at multiplicities above 30. On the one hand, the observed excess in long-range correlations may reflect the more complex color-string configurations arising from $W^+W^-$ production. On the other hand, it can simply arise from the higher final-state multiplicity made possible by the increased collision energy, independent of the underlying production mechanism. To discriminate between these competing interpretations, we present a measurement of two-particle angular correlations in $e^+e^-$ collisions at $\sqrt{s} = 183-209$ GeV, with a focus on enhancing the contribution from $W^+W^-$ processes. The analysis uses data collected by the ALEPH detector during the LEP2 program. Correlation functions are evaluated across a broad range of pseudorapidities and full azimuth, in bins of charged-particle multiplicity. A ridge-like modulation is seen for multiplicity above 50, deviating from the MC reference. In addition, the correlation functions are further decomposed into a Fourier series, and the resulting harmonic coefficients $v_n$ are compared with predictions from the archived Monte Carlo sample. For multiplicity starting from 30, the signed $v_2$-like proxy goes from negative to positive, also deviating from the MC baseline.

hep-ex

Unbinned measurement of thrust in $e^+e^-$ collisions at $\sqrt{s}$ = 91.2 GeV with ALEPH archived data

The strong coupling constant ($\alpha_{S}$) is a fundamental parameter of quantum chromodynamics (QCD), the theory of the strong force. Some of the earliest precise constraints on $\alpha_{S}$ came from measurements of event shape observables, such as thrust ($T$), using hadronic $Z$ boson decays produced in $e^+e^-$ collisions. However, recent work has revealed discrepancies between event-shape-based extractions of $\alpha_{S}$ and values determined using other experimental methods. This work reexamines archived $e^+e^-$ data collected at a collision energy of $\sqrt{s}=91.2$ GeV by the ALEPH detector at the Large Electron-Positron Collider. Modern machine learning techniques are used to correct for detector effects in an unbinned manner, allowing the $T$ distribution to be measured with higher granularity than previous ALEPH measurements. The new measurement reveals a small but systematic shift towards larger values of $\tau=1-T$, and the potential implications of this shift for $\alpha_{S}$ extractions are illustrated by comparing to state-of-the-art theoretical calculations. In addition, the region of $-6<\log\tau<-2$, where poorly-understood non-perturbative effects are large, is compared to modern parton shower Monte Carlo simulations. This measurement provides unique new inputs for $\alpha_{S}$ extractions and also improves constraints on phenomenological models of QCD dynamics such as parton fragmentation and hadronization.

hep-ex

Analysis note: measurement of thrust in $e^{+}e^{-}$ collisions at $\sqrt{s}$ = 91 GeV with archived ALEPH data

A measurement of the thrust distribution in $e^{+}e^{-}$ collisions at $\sqrt{s} = 91.2$ GeV with archived data from the ALEPH experiment at the Large Electron-Positron Collider is presented. The thrust distribution is reconstructed from charged and neutral particles resulting from hadronic $Z$-boson decays. For the first time with $e^{+}e^{-}$ data, detector effects are corrected using a machine learning based method for unbinned unfolding. The measurement provides new input for resolving current discrepancies between theoretical calculations and experimental determinations of $\alpha_{s}$, constraining non-perturbative effects through logarithmic moments, developing differential hadronization models, and enabling new precision studies using the archived data.

hep-ex

CODEX-b: Opening New Windows to the Long-Lived Particle Frontier at the LHC

This document is written as a contribution to the European Strategy of Particle Physics (ESPP) update. We offer a detailed overview of current developments and future directions for the CODEX-b detector, which aims to detect long-lived particles beyond the Standard Model. We summarize the scientific motivation for this detector, advances in our suite of simulation and detector optimization frameworks, and examine expected challenges, costs, and timelines in realizing the full detector. Additionally, we describe the technical specifications for the smaller-scale demonstrator detector (CODEX-$\beta$) we have installed in the LHCb experimental cavern.

hep-ex

Technical design report for the CODEX-$\beta$ demonstrator

The CODEX-$\beta$ apparatus is a demonstrator for the proposed future CODEX-b experiment, a long-lived-particle detector foreseen for operation at IP8 during HL-LHC data-taking. The demonstrator project, intended to collect data in 2025, is described, with a particular focus on the design, construction, and installation of the new apparatus.

physics.ins-det

Long-range near-side correlation in $e^+e^-$ Collisions at 183-209 GeV with ALEPH Archived Data

The first measurement of two-particle angular correlations for charged particles with LEP-II data is presented. The study is performed using archived hadronic $e^+e^-$ data collected by ALEPH at center-of-mass energies up to 209 GeV, above the $W^+W^-$ production threshold, which provide access to unprecedented charged-particle multiplicities and more complex color-string configurations if compared to previous measurements at LEP-I energies. An intriguing long-range near-side excess is observed in the correlation function measured with respect to the thrust axis in the highest multiplicity interval $N_{\mathrm{trk}}\geq 50$. Such a structure is not predicted by the Monte-Carlo simulation. The harmonic anisotropy coefficients $v_n$, which result from the Fourier expansion of the two-particle correlation functions, were also measured for the first time in $e^+e^-$ data, and compared to PYTHIA6 predictions and to the results obtained in proton-proton collisions. The results presented in the Letter provide novel experimental constraints on the formation of collective phenomena in point-like $e^+e^-$ collisions.

hep-ex

Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC

We are studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived heuristically from the ensemble of charged track parameters and predicted "target histogram" proxies, from which the actual PV positions are extracted. We have recently demonstrated that using a UNet architecture performs indistinguishably from a "flat" convolutional neural network model. We have developed an "end-to-end" tracks-to-hist DNN that predicts target histograms directly from track parameters using simulated LHCb data that provides better performance (a lower false positive rate for the same high efficiency) than the best KDE-to-hists model studied. This DNN also provides better efficiency than the default heuristic algorithm for the same low false positive rate. "Quantization" of this model, using FP16 rather than FP32 arithmetic, degrades its performance minimally. Reducing the number of UNet channels degrades performance more substantially. We have demonstrated that the KDE-to-hists algorithm developed for LHCb data can be adapted to ATLAS and ACTS data using two variations of the UNet architecture. Within ATLAS/ACTS, these algorithms have been validated against the standard vertex finder algorithm. Both variations produce PV-finding efficiencies similar to that of the standard algorithm and vertex-vertex separation resolutions that are significantly better.

hep-ex

Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices

Using deep neural networks to identify and locate proton-proton collision points, or primary vertices, in LHCb has been studied for several years. Preliminary results demonstrated the ability for a hybrid deep learning algorithm to achieve similar or better physics performances compared to standard heuristic approaches. The previously studied architectures relied directly on hand-calculated Kernel Density Estimators (KDEs) as input features. Calculating these KDEs was slow, making use of the DNN inference engines in the experiment's real-time analysis (trigger) system problematic. Here we present recent results from a high-performance hybrid deep learning algorithm that uses track parameters as input features rather than KDEs, opening the path to deployment in the real-time trigger system.

hep-ex

Jet energy spectrum and substructure in $e^+e^-$ collisions at 91.2 GeV with ALEPH Archived Data

The first measurements of energy spectra and substructure of anti-$k_{T}$ jets in hadronic $Z^0$ decays in $e^+e^-$ collisions are presented. The archived $e^+e^-$ annihilation data at a center-of-mass energy of 91.2 GeV were collected with the ALEPH detector at LEP in 1994. In addition to inclusive jet and leading dijet energy spectra, various jet substructure observables are analyzed as a function of jet energy which includes groomed and ungroomed jet mass to jet energy ratios, groomed momentum sharing, and groomed jet radius. The results are compared with perturbative QCD calculations and predictions from the SHERPA, HERWIG v7.1.5, PYTHIA 6, PYTHIA 8, and PYQUEN event generators. The jet energy spectra agree with perturbative QCD calculations which include the treatment of logarithms of the jet radius and threshold logarithms. None of the event generators give a fully satisfactory description of the data.

hep-ex

Implementation of ACTS into sPHENIX track reconstruction

sPHENIX is a high energy nuclear physics experiment under construction at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory (BNL). The primary physics goals of sPHENIX are to study the quark-gluon-plasma, as well as the partonic structure of protons and nuclei, by measuring jets, their substructure, and heavy flavor hadrons in $p$$+$$p$, $p$+Au, and Au+Au collisions. sPHENIX will collect approximately 300 PB of data over three run periods, to be analyzed using available computing resources at BNL; thus, performing track reconstruction in a timely manner is a challenge due to the high occupancy of heavy ion collision events. The sPHENIX experiment has recently implemented the A Common Tracking Software (ACTS) track reconstruction toolkit with the goal of reconstructing tracks with high efficiency and within a computational budget of 5 seconds per minimum bias event. This paper reports the performance status of ACTS as the default track fitting tool within sPHENIX, including discussion of the first implementation of a time projection chamber geometry within ACTS.

physics.ins-det

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

Measurements of two-particle correlations in $e^+e^-$ collisions at 91 GeV with ALEPH archived data

Measurements of two-particle angular correlations of charged particles emitted in hadronic $Z$ decays are presented. The archived $e^+e^-$ annihilation data at a center-of-mass energy of 91 GeV were collected with the ALEPH detector at LEP between 1992 and 1995. The correlation functions are measured over a broad range of pseudorapidity and full azimuth as a function of charged particle multiplicity. No significant long-range correlation is observed in either the lab coordinate analysis or the thrust coordinate analysis, where the latter is sensitive to a medium expanding transverse to the color string between the outgoing $q\bar{q}$ pair from $Z$ boson decays. The associated yield distributions in both analyses are in better agreement with the prediction from the PYTHIA v6.1 event generator than from HERWIG v7.1.5. They provide new insights to showering and hadronization modeling. These results serve as an important reference to the observed long-range correlation in proton-proton, proton-nucleus, and nucleus-nucleus collisions.

hep-ex

Simulating Organogenesis in COMSOL: Comparison Of Methods For Simulating Branching Morphogenesis

During organogenesis tissue grows and deforms. The growth processes are controlled by diffusible proteins, so-called morphogens. Many different patterning mechanisms have been proposed. The stereotypic branching program during lung development can be recapitulated by a receptor-ligand based Turing model. Our group has previously used the Arbitrary Lagrangian-Eulerian (ALE) framework for solving the receptor-ligand Turing model on growing lung domains. However, complex mesh deformations which occur during lung growth severely limit the number of branch generations that can be simulated. A new Phase-Field implementation avoids mesh deformations by considering the surface of the modelling domains as interfaces between phases, and by coupling the reaction-diffusion framework to these surfaces. In this paper, we present a rigorous comparison between the Phase-Field approach and the ALE-based simulation.

q-bio.QM

Uncertainty Quantification for PDEs with Anisotropic Random Diffusion

In this article, we consider elliptic diffusion problems with an anisotropic random diffusion coefficient. We model the notable direction in terms of a random vector field and derive regularity results for the solution's dependence on the random parameter. It turns out that the decay of the vector field's Karhunen-Loeve expansion entirely determines this regularity. The obtained results allow for sophisticated quadrature methods, such as the quasi-Monte Carlo method or the anisotropic sparse grid quadrature, in order to approximate quantities of interest, like the solution's mean or the variance. Numerical examples in three spatial dimensions are provided to supplement the presented theory.

math.NA

Multilevel tensor approximation of PDEs with random data

In this paper, we introduce and analyze a new low-rank multilevel strategy for the solution of random diffusion problems. Using a standard stochastic collocation scheme, we first approximate the infinite dimensional random problem by a deterministic parameter-dependent problem on a high-dimensional parameter domain. Given a hierarchy of finite element discretizations for the spatial approximation, we make use of a multilevel framework in which we consider the differences of the solution on two consecutive finite element levels in the collocation points. We then address the approximation of these high-dimensional differences by adaptive low-rank tensor techniques. This allows to equilibrate the error on all levels by exploiting analytic and algebraic properties of the solution at the same time. We arrive at an explicit representation in a low-rank tensor format of the approximate solution on the entire parameter domain, which can be used for, e.g., the direct and cheap computation of statistics. Numerical results are provided in order to illustrate the approach.

math.NA

Novel results for the anisotropic sparse grid quadrature

This article is dedicated to the anisotropic sparse grid quadrature for functions which are analytically extendable into an anisotropic tensor product domain. Taking into account this anisotropy, we end up with a dimension independent error versus cost estimate of the proposed quadrature. In addition, we provide a novel and improved estimate for the cardinality of the underlying anisotropic index set. To validate the theoretical findings, we present several examples ranging from simple quadrature problems to diffusion problems on random domains. These examples demonstrate the remarkable convergence behaviour of the anisotropic sparse grid quadrature in applications.

math.NA

Materials with low DC magnetic susceptibility for sensitive magnetic measurements

Materials with very low DC magnetic susceptibility have many scientific applications. To our knowledge however, relatively little research has been conducted with the goal to produce a totally nonmagnetic material. This phrase in our case means after spatially averaging over macroscopic volumes, it possesses an average zero DC magnetic susceptibility. We report measurements of the DC magnetic susceptibility of three different types of nonmagnetic materials at room temperature: (I) solutions of paramagnetic salts and diamagnetic liquids, (II) liquid gallium-indium alloys and (III) pressed powder mixtures of tungsten and bismuth. The lowest measured magnetic susceptibility among these candidate materials is in the order of 10^-9 cgs volume susceptibility units, about two orders of magnitude smaller than distilled water. In all cases, the measured concentration dependence of the magnetic susceptibility is consistent with that expected for the weighted sum of the susceptibilities of the separate components within experimental error. These results verify the Wiedemann additivity law and thereby realize the ability to produce materials with small but tunable magnetic susceptibility. For our particular scientific application, we are also looking for materials with the largest possible number of neutrons and protons per unit volume. The gallium-indium alloys fabricated and measured in this work possess to our knowledge the smallest ratio of volume magnetic susceptibility to nucleon number density per unit volume for a room temperature liquid, and the tungsten-bismuth pressed powder mixtures possess to our knowledge the smallest ratio of volume magnetic susceptibility to nucleon number density per unit volume for a room temperature solid. This ratio is a figure of merit for a certain class of precision experiments that search for possible exotic spin-dependent forces of Nature.

physics.ins-det

Spreadsheet Validation and Analysis through Content Visualization

Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-uses the layout and formatting already present in the spreadsheet.

cs.HC