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DOE OSTI2024

Probing New Physics with Tau Leptons using the CMS Detector

The Standard Model (SM) of particle physics is a thoroughly well-tested theory of all electroweak scale phenomena to date—the most recent test being the discovery of the long-predicted Higgs boson. However, there is much evidence to suggest that it is incomplete. On the formal side, the mass of the Higgs particle itself is perplexingly small, while the largest theoretical energy scale of the SM is 17 orders of magnitude larger, a puzzle known as the hierarchy problem. On the experimental side, among other things, the SM lacks a candidate for dark matter, which is now known to play an indispensable role in astrophysical large scale structure formation. Many theories that address the fundamental puzzles of the SM predict the existence of multiple Higgs bosons, including a very light boson that couples preferentially to third-generation fermions. Observation of this new light boson, either directly produced or in the decay of the recently discovered SM Higgs, would provide an unmistakable sign of new dynamics in nature. The research supported by this award used data collected by the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) to explore boosted di-tau signatures of a new light boson as a tool in the quest to understand the shortcomings of the SM. With a proton-proton collision energy of 13 trillion electron-volts (TeV) and peak instantaneous proton luminosity of over 10^34 collisions per square centimeter per second (cm –2 s –1 ), the LHC is the only facility in the world that could enable this line of research. The research sought to address the hierarchy problem and other shortcomings of the SM through a mixture of LHC data analysis, maintenance of the CMS detector to ensure high quality data, and contributions to the Phase 2 upgrade detector for the upcoming High Luminosity phase of the LHC (HL-LHC).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Analysis and Grading of the Test Performance of PS Modules for the CMS Phase-II Outer Tracker Upgrade

The Outer Tracker detector of the Compact Muon Solenoid (CMS) experiment provides information about the trajectory of charged particles produced in proton-proton collisions at the Large Hadron Collider (LHC). During the High Luminosity LHC upgrade, scheduled for the late 2020s, the Outer Tracker will be replaced with new modules capable of transmitting data to the L1 Trigger. These modules are being assembled at several facilities around the world, including Fermilab, necessitating coordinated standards of module quality. Here I discuss the development of POTATO (Phase-II Outer Tracker Analyzer of Test Outputs), a C++ software which provides a standardized procedure for analyzing and grading test results of the Outer Tracker modules. The particular focus of this paper is on the analysis and grading of the PS (pixel-strip) modules in POTATO.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Smart Pixels: In-pixel AI for on-sensor data filtering

We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 µm\textsuperscript{2} in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e\textsuperscript{-} and a total dispersion of $\sim$100e\textsuperscript{-} The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4\% – 75.4\% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm\textsuperscript{2} staying within the experimental constraints.

Parpillon, Benjamin↗

DOE OSTI2024

Celeritas Midterm SciDAC Report

Celeritas is a new Monte Carlo (MC) code that helps satisfy the increasing demand for high energy physics (HEP) detector simulation, using Graphics Processing Unit (GPU) hardware on high performance computing (HPC) systems to model Large Hadron Collider (LHC) experiments and beyond. This report details the project’s progress midway through its SciDAC funding period, highlighting the first complete implementation of standard electromagnetic (EM) physics on GPUs, initial results for performance and scalability on Leadership Computing Facilities (LCFs), and preliminary integration into the CMS and ATLAS experiments. By integrating HEP domain knowledge with expertise in MC transport, Celeritas has catalyzed a shift in the HEP community’s perception of GPU platforms as the future for HPC simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Enabling Innovative Analysis on Heterogeneous Clusters through HTCdaskgateway

High energy particle (HEP) physics research is going through fundamental changes as we move to collect larger amounts of data from the Large Hadron Collider (LHC). Analysis facilities and distributed computing, through HTCs, have come together to create the next pythonic generation of analysis by utilizing HTCdaskgateway, a Dask gateway extension, allowing users to spawn workers compatible with both their analysis and heterogeneous clusters in line with authentication requirements. This is enabling physicists to engage with scientific python in ways they had not before because of domain specific C++ tools. An example of HTCdaskgateway’s use is Fermilab’s Elastic Analysis Facility.

Chavez, Elise [U. Wisconsin, Madison (main)]↗

DOE OSTI2023

The CMS Phase II Outer Tracker Upgrade at Fermilab

The Large Hadron Collider (LHC) will be upgraded to increase the instantaneous luminosity up to 5−7.5×10 34 cm −2 s −1 . To face these challenging conditions the CMS tracking system must be updated, using modules that on the one hand are able to cope with high radiations and on the other hand can provide information to the L-1 trigger selection. This report describes the results of some tests on the new tracking modules at the FNAL test beam facility. In particular half of one module was irradiated by 400 MeV protons (total fluence comparable to the one expected through the lifetime of the experiment) and then some analysis were performed on both the irradiated and not irradiated side of the detector with a 120 GeV proton beam. In this report, the efficiency and noise occupancy analysis are presented, with focus on the main critical issues encountered.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Trigger-System Development for the HGCAL Cassette Testing (Final Report, Summer Internship 2023)

The Large Hadron Collider (LHC) will be upgraded to increase the instantaneous luminosity, which will result in an increase in the number of collisions from the 15 pile-up events of the first run of phase 1 to the 150-200 pile-up events of phase 2. Increasing the number of collisions means increasing the amount of radiation that the experiments will absorb. To meet these challenging conditions, the CMS calorimeters need to be upgraded. The new version of the hadronic HGCAL (High Granularity CALorimeters) in the endcaps will be assembled and tested in Fermilab’s laboratory. This report describes the new trigger system for testing the HGCAL’s cassettes.

42 ENGINEERING↗

DOE OSTI2020

Sensitivity of D meson azimuthal anisotropies to system size and nuclear structure

Recent experimental data at the Large Hadron Collider (LHC) confirmed that the soft sector azimuthal anisotropies in central XeXe collisions are sensitive to deformations in the wave function of the Xenon nucleus. Additionally, the CMS experiment found that D meson flow is slightly suppressed compared to other particle species when considering quark number scaling in small systems of pPb compared to PbPb. In this talk we used the D and B Mesons Modular (DAB-MOD) code coupled to Trento+v-USPhydro to calculate the R AA and v n ’s of D mesons using different energy loss models and Langevin techniques. Comparing D mesons R AA and v n in PbPb to XeXe collisions it is found that in central collisions D meson azimuthal anisotropies are sensitive to details in the nuclear structure wave function of Xenon. Additionally, we find that in mid-central collisions the smaller system size of XeXe suppresses D meson flow.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Design of a Level-1 Long-Lived Particle Trigger in the CMS Hadron Calorimeter

The Hadron Calorimeter (HCAL) in the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) was recently upgraded for Run 3 to introduce depth segmentation and online timing measurements, expanding the physics capabilities. In particular, the augmentation of the calorimeter information at the hardware trigger level enables quick identification and recording of long-lived particle decays using lower thresholds on energy-based event quantities. The depth segmentation and online timing capabilities are utilized in novel HCAL-based hardware-level triggers to identify displaced and delayed long-lived particles (LLPs), either decaying inside the calorimeter volume or arriving at a delayed time. This increases the sensitivity to LLP decays occurring up to almost 6 m from the collision point. This two-pronged calorimeter trigger approach leverages the new capabilities of the CMS HCAL to expand the phase space accessible in ongoing LLP searches. These triggers were deployed for Run 3 of the LHC, beginning data-taking in 2022, and we review the trigger implementation and calibration.

Kopp, Gillian Baron [Princeton University, NJ (Uni↗

DOE OSTI2020

Heterogeneous Reconstruction of Tracks and Primary Vertices With the CMS Pixel Tracker

The High-Luminosity upgrade of the Large Hadron Collider (LHC) will see the accelerator reach an instantaneous luminosity of 7 × 10 34 cm −2 s −1 with an average pileup of 200 proton-proton collisions. These conditions will pose an unprecedented challenge to the online and offline reconstruction software developed by the experiments. The computational complexity will exceed by far the expected increase in processing power for conventional CPUs, demanding an alternative approach. Industry and High-Performance Computing (HPC) centers are successfully using heterogeneous computing platforms to achieve higher throughput and better energy efficiency by matching each job to the most appropriate architecture. In this paper we will describe the results of a heterogeneous implementation of pixel tracks and vertices reconstruction chain on Graphics Processing Units (GPUs). The framework has been designed and developed to be integrated in the CMS reconstruction software, CMSSW. The speed up achieved by leveraging GPUs allows for more complex algorithms to be executed, obtaining better physics output and a higher throughput.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Jets and Jet Substructure at Future Colliders

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗

DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

A Vertex-Aligned Model for Packing 4-Hexagonal Clusters in a Regular Hexagonal Container

This paper deals with a problem the packing polyhex clusters in a regular hexagonal container. It is a common problem in many applications with various cluster shapes used, but symmetric polyhex is the most useful in engineering due to its geometrical properties. Hence, we concentrate on mathematical modeling in such an application, where using the “bee” tetrahex is chosen for the new Compact Muon Solenoid (CMS) design upgrade, which is one of four detectors used in Large Hadron Collider (LHC) experiment at European Laboratory for Particle Physics (CERN). We start from the existing hexagonal containers with hexagonal cells packed inside, and uniform clustering applied. We compare the center-aligned (CA) and vertex-aligned (VA) models, analyzing cluster rotations providing the increased packing efficiency. We formally describe the geometrical properties of clustering approaches and show that cluster sharing is inevitable at the container border with uniform clustering. In addition, we propose a new vertex-aligned model decreasing the number of shared clusters in the uniform scenario, but with a smaller number of clusters contained inside the container. Also, we describe a non-uniform tetrahex cluster packing scheme in the proposed container model. With the proposed cluster packing solution, it is accomplished that all clusters are contained inside the container region. Since cluster-sharing is completely avoided at the container border, the maximal packing efficiency is obtained compared to the existing models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP

In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the Large Hadron Collider (LHC) when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in the search for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Hot QCD White Paper

Hot QCD physics studies the nuclear strong force under extreme temperature and densities. Experimentally these conditions are achieved via high-energy collisions of heavy ions at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). In the past decade, a unique and substantial suite of data was collected at RHIC and the LHC, probing hydrodynamics at the nucleon scale, the temperature dependence of the transport properties of quark-gluon plasma, the phase diagram of nuclear matter, the interaction of quarks and gluons at different scales and much more. This document, as part of the 2023 nuclear science long range planning process, was written to review the progress in hot QCD since the 2015 Long Range Plan for Nuclear Science, as well as highlight the realization of previous recommendations, and present opportunities for the next decade, building on the accomplishments and investments made in theoretical developments and the construction of new detectors. Furthermore, this document provides additional context to support the recommendations voted on at the Joint Hot and Cold QCD Town Hall Meeting, which are reported in a separate document.

FOS: Physical sciences↗

DOE OSTI2025

Enhanced signal of momentum broadening in hard splittings for $γ$-tagged jets in a multistage approach

We investigate medium-induced modifications to jet substructure observables that characterize hard splitting patterns in central Pb-Pb collisions at the top energy of the Large Hadron Collider (LHC). Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $γ$-tagged jets. The results show that quark jets undergo a non-monotonic modification compared to gluon jets in observables such as the Pb-Pb to $p$-$p$ ratio of the Soft Drop prong angle $r_g$, the relative prong transverse momentum $k_{T,g}$ and the groomed mass $m_g$ distributions. Due to this non-monotonic modification, $γ$-tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. This work highlights the potential of hard substructures in $γ$-tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions. All simulations for $γ$-tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis.

FOS: Physical sciences↗

DOE OSTI2025

Upgrades of the ATLAS Zero Degree Calorimeter System for Run 3 at the Large Hadron Collider

Experimental studies of ultra-relativistic heavy ion collisions at the Large Hadron Collider (LHC) depend crucially on Zero Degree Calorimeters (ZDCs) that measure neutrons produced at near-beam rapidity in nucleus-nucleus collisions. In hadronic nuclear collisions these neutrons are mainly spectator neutrons, those that do not scatter from opposing nucleons during the collision. As a result, the ZDCs provide a vital probe of heavy ion collision geometry. The ZDCs are also essential in the study of ultra-peripheral collisions that are initiated by photons associated with the electric fields of one or both nuclei. Coherent photon emission typically leaves the photon emitter intact, making the observation of no ZDC signal, on one or both sides, a tag of such processes. The ATLAS ZDCs, built prior to Run 1 were substantially upgraded for LHC Run 3. The primary upgrades included replacement of the quartz Cherenkov radiator with $\text{H}_2$-doped fused silica rods; installation of fast air-core signal cables between the ZDC and the ATLAS USA15 cavern; new LED-based calibration system; and new electronics implemented for readout and fully-digital triggering. The ZDCs were also augmented with new "Reaction Plane Detectors" (RPDs) designed to measure the transverse centroid of multi-neutron showers to allow event-by-event reconstruction of the directed-flow plane in nuclear collisions. The Run~3 ZDC detectors, including the RPDs, are described in detail with emphasis on aspects that are new for Run~3.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗