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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Search for heavy Majorana neutrinos in pp collisions at √s = 8 TeV with the CMS detector & photodetector and calorimeter R&D for particle colliders

This thesis contains both physics analysis and hardware studies. It consists of two primary sections: the results of a search for heavy Majorana mass neutrinos, using the event signature of same (like) sign charged electron pairs ($e^{\pm} e^{\pm}$ ) and two jets, and the results of studies to upgrade the Hadronic Forward (HF) and Hadronic Endcap (HE) subdetectors in the Compact Muon Solenoid (CMS) detector in response to the high intensity proton-proton collisions generated at the Large Hadron Collider (LHC) at European Organization for Nuclear Research (CERN, Conseil Europ\'{e}en pour la Recherche Nucl\'{e}aire). In this search for Majorana mass neutrinos, same sign dielectron ($e^{\pm} e^{\pm}$) + dijet events in the final state have been considered as a signature for neutrino particles. The analyzed data corresponds to an integrated luminosity of 19.7 fb\textsuperscript{-1} of proton-proton collisions at a center of mass energy of $\sqrt{s} = 8 \text{TeV}$, collected using the CMS detector during the 2012 operation at the LHC. Monte Carlo simulations accounting for the theoretical expectations of the Standard Model (SM) and the detector limitations are used to prototype the experiment and to test proposed analysis steps. No excess of events is observed in the data beyond the expected SM background. Upper limits are set on the mixing element squared, $|{V}_{eN}|^{2}$, of the heavy Majorana neutrino with standard model neutrinos, as a function of Majorana neutrino mass for masses in the range of 40-500 $GeV/c^2$. The detector upgrade search comprises three sections of this thesis. The first section describes the test results of 1785 multianode Hamamatsu R7600U-200-M4 photomultiplier tubes (PMT) in numerous parameters such as gain, dark current, and timing characteristics, which provide insights on the expected performance of the upgraded CMS-HF detector. These PMTs replaced the previous single anode R7525 PMTs because the glass windows of previous PMTs are the source of Cherenkov radiation, which causes a background noise in the experiment. The second section reports characterization results of two types of PMTs in a novel operation mode for Secondary Emission (SE) Ionization Calorimetry, which is a novel technique to measure electromagnetic shower particles in extreme radiation environments. The third section presents the test results of novel scintillating materials for CMS experiment in specific and future particle accelerators in general. These materials are Polyethylene Naphthalate (PEN), Polyethylene Terephthalate (PET), high efficiency mirror (HEM) and quartz plates with various organic and inorganic coating materials such as p-Terphenyl (pTp), Anthracene and Gallium-doped Zinc Oxide (ZnO:Ga). We have investigated them for radiation hardness, light yield, timing characteristics, and scintillation and transmission properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Search for the Higgs and Physics Beyond the Standard Model with the CMS Electromagnetic Calorimeter

The Compact Muon Solenoid (CMS) Experiment is a general purpose particle detector experiment at the Large Hadron Collider (LHC) at the European Laboratory for Nuclear Research (CERN). The LHC is the world’s highest energy particle collider, providing proton-proton collisions up to a center-of-mass energy of 13 TeV. The primary aim of the LHC and the CMS Experiment is to discover new physics at this high energy frontier. In 2012, the discovery of the Higgs boson at the LHC marked a crucial milestone in the Standard Model (SM) of particle physics and ushered in the beginning of a new era of exploration at the high energy frontier. Since this discovery, two key areas of study were the characterization of the new particle to confirm that it is, indeed, the predicted SM Higgs boson and also the examination of possible deviations from the SM that may hint at new physics. As a member of the CMS collaboration, the main interest of PI Orimoto has been the study of electromagnetic objects (electrons and photons) as a critical ingredient in the characterization of the Higgs boson, and as a window into physics beyond-the-SM (BSM). To this end, the Orimoto group has led numerous physics analyses ranging from SM Higgs boson studies, searches for exotic production and decay of the Higgs boson, as well as searches for supersymmetric particles. Moreover, the Orimoto group has made significant contributions to the CMS electromagnetic calorimeter (ECAL), the sub-detector responsible for the precise measurement of the energies of electrons and photons. The Orimoto group’s contributions have been recognized with many leadership roles and high profile conference presentations. This report outlines the group’s products and accomplishments from the award period (June 2013 through July 2018).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Forward Pixel Detector for the Phase 2 Upgrade of CMS And Module Testing at UIC

High-Luminosity Large Hadron Collider (HL-LHC) upgrade aims to increase performance. In order to efficiently handle the larger particle flux and increased radiation, an upgrade is planned for the inner Tracker of the Compact Muon Solenoid (CMS) detector, which is located at the innermost part of the CMS. To efficiently reconstruct and track particles, the existing pixel tracker will be upgraded with novel sensors, readout chips, and front-end electronics designed to handle increased data rates. These improvements will allow the detector to track particles more precisely. In the United States, we are working on assembling new sensor modules to be inserted into disks, referred to as TFPX, for installation in the CMS detector for the HL-LHC era. We are currently testing the prototype of TFPX modules to ensure the upgrade is carried out in the best way possible. The University of Ilinois Chicago has been tasked with testing the final versions of the TFPX sensor modules before they are sent to CERN to be installed into the CMS detector. This work is supported by NSF.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Simulating the CMS High Granularity Calorimeter with ML

Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the existing CMS calorimeters. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation. We study the application of state-of-the-art diffusion models to simulate particle showers in the CMS HGCal. We will discuss methods to overcome the challenges posed by the high-dimensional, irregular geometry of the HGCal. The quality of the showers produced by the diffusion model will be assessed by comparison to the full GEANT4-based simulation. The increase in simulation throughput will be quantified and methods to accelerate the diffusion model inference will also be discussed.

Amram, Oz↗
DOE OSTI2024

Deployment of inference as a service at the US CMS Tier-2 data centers

Coprocessors, especially GPUs, will be a vital ingredient of data production workflows at the HL-LHC. At CMS, the GPU-as-a-service approach for production workflows is implemented by the SONIC project (Services for Optimized Network Inference on Coprocessors). SONIC provides a mechanism for outsourcing computationally demanding algorithms, such as neural network inference, to remote servers, where requests from multiple clients are intelligently distributed across multiple GPUs by a load-balancing service. This talk highlights the recent progress in deploying SONIC at selected U.S. CMS Tier-2 data centers. Using realistic CMS Run3 data processing workflows, such as those containing transformer-based algorithms, we demonstrate how SONIC is integrated into the production-like environment to enable accelerated inference offloading. We will present developments from both the client and server sides, including production job and data center configurations for NVIDIA and AMD GPUs. We will also present performance scaling benchmarks and discuss the challenges of operating SONIC in CMS production, such as server discovery, GPU saturation, fallback server logic, etc.

Holzman, Burt↗
DOE OSTI2019

CRIC and Rucio integration for CMS Data Management

The Large Hadron Collider (LHC) at CERN smashes protons together at close to the speed of light with seven times the energy of the most powerful accelerators built up to now. Some of the collision energy is turned into mass, creating new particles which are observed in the Compact Muon Solenoid (CMS) particle detector. CMS data is analyzed by scientists around the world to build up a picture of what happened at the heart of the collision. This will help us answer questions such as: ”what is the Universe really made of and what forces act within it?” and ”what gives everything substance?”. All the data produced by the experiment are collected and stored in a distributed system, since it must provide an high throughput and performing access to these data to scientists and physicists spread all over the world. In order to answer the previous questions and many others, CMS users must first obtain a copy of a portion of these data, and then to analyze it, therefore the first challenge is to decide in which storage site to save this duplicate so they can access it as quickly as possible. The second issue is each storage site shares a limited memory space with many CMS users, and each one needs to be assigned to a well defined personal amount of storage to pin data he needs. The main goal of my Summer Internship is to make scalable and automatic this user-site mapping which is based on a preexisting policy, proving a structured and organized algorithm to accomplish this task. Hence we are going to define, and solve, the main challenges encountered, and the technical solution used to generalize the algorithm to handle different policies for different countries.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
NASA NTRS2020

NASA’s Carbon Monitoring System (CMS) and Arctic-Boreal Vulnerability Experiment (ABoVE) Social Network and Community of Practice

The NASA Carbon Monitoring System (CMS) and Arctic-Boreal Vulnerability Experiment (ABoVE) have been planned and funded by the NASA Earth Science Division. Both programs have a focus on engaging stakeholders and developing science useful for decision making. The resulting programs have funded significant scientific output and advancements in understanding how satellite remote sensing observations can be used to not just study how the Earth is changing, but also create data products that are of high utility to stakeholders and decisions makers. In this paper we focus on documenting thematic diversity of research themes and methods used, and how the CMS and ABoVE themes are related. We do this through developing a Correlated Topic Model on the 521 papers produced by the two programs and plotting the results in a network diagram. Through analysis of the themes in these papers, we document the relationships between researchers and institutions participating in CMS and ABoVE programs and the benefits from sustained engagement with stakeholders due to recurring funding. We note an absence of policy engagement in the papers and conclude that funded researchers need to be more ambitious and explicit in drawing the connection between their research and carbon policy implications in order to meet the stated goals of the CMS and ABoVE programs.

Molly E Brown↗
DOE OSTI2023

The U.S. CMS HL-LHC R&D Strategic Plan

The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop and operate the software and computing resources necessary to process CMS data expeditiously and to enable U.S. physicists to fully participate in the physics of CMS. We have developed a strategic plan to prioritize R&D efforts to reach this goal for the HL-LHC. This plan includes four grand challenges: modernizing physics software and improving algorithms, building infrastructure for exabyte-scale datasets, transforming the scientific data analysis process and transitioning from R&D to operations. We are involved in a variety of R&D projects that fall within these grand challenges. In this talk, we will introduce our four grand challenges and outline the R&D program of the U.S. CMS Software & Computing Operations Program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Understanding the High Energy Higgs Sector with the CMS Experiment and Artificial Intelligence

This dissertation describes efforts towards understanding the Higgs boson at the highest energies humanly accessible, using the CMS experiment at the Large Hadron Collider and advances in artificial intelligence (AI) and machine learning (ML). We present searches for resonant and nonresonant Higgs-boson (H) pair production in the all-hadronic two beauty-quark and two vector boson (V) final state, using a novel strategy to measure the quartic HHVV coupling and search for new Higgs-like bosons. By targeting highly Lorentz-boosted Higgs pairs, we probe effects of potential new physics in the high energy Higgs sector, which could hold answers to fundamental mysteries of nature such as baryon asymmetry. To enable these and future searches, we introduce as well significant developments in AI/ML, including in the identification of boosted H$\rightarrow$VV decays with deep transformer networks and advances in AI-accelerated fast simulations of the CMS detector. The latter notably includes the development of the first, highly performant generative models for point-cloud data in high energy physics, which have the potential to improve CMS' computational efficiency by up to three orders of magnitude. We also highlight novel solutions to the important and challenging problems of calibrating and validating these ML techniques. Finally, we present new approaches to search for new physics in a model-agnostic manner, using physics-informed ML methods equivariant to Lorentz transformations. The quartic HHVV coupling is observed (expected) to be constrained to $[-0.04, 2.05]$ ($[0.05, 1.98]$) at the 95% confidence level relative to the standard model prediction, representing the second-most sensitive measurement of this coupling by CMS to date. Exclusion limits on the production cross section of new heavy resonances decaying to two Higgs-like bosons are expected to be as low as 0.3 fb for high resonance masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2016

Prototypes and system test stands for the Phase 1 upgrade of the CMS pixel detector

The CMS pixel phase-1 upgrade project replaces the current pixel detector with an upgraded system with faster readout electronics during the extended year-end technical stop of 2016/2017. New electronics prototypes for the system have been developed, and tests in a realistic environment for a comprehensive evaluation are needed. A full readout test stand with either the same hardware as used in the current CMS pixel detector or the latest prototypes of upgrade electronics has been built. The setup enables the observation and investigation of a jitter increase in the data line associated with trigger rate increases. This effect is due to the way in which the clock and trigger distribution is implemented in CMS. A new prototype of the electronics with a PLL based on a voltage controlled quartz crystal oscillator (QPLL), which works as jitter filter, in the clock distribution path was produced. With the test stand, it was confirmed that the jitter increase is not seen with the prototype, and also good performance was confirmed at the expected detector operation temperature ($-$20 °C).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Forward and small-x QCD physics results from CMS experiment at LHC

The Compact Muon Solenoid (CMS) is one of the two large, multi-purpose experiments at the Large Hadron Collider (LHC) at CERN. During the Run I Phase a large pp collision dataset has been collected and the CMS collaboration has explored measurements that shed light on a new era. Forward and small-x quantum chromodynamics (QCD) physics measurements with CMS experiment covers a wide range of physics subjects. Some of highlights in terms of testing the very low-x QCD, underlying event and multiple interaction characteristics, photon-mediated processes, jets with large rapidity separation at high pseudo-rapidities and the inelastic proton-proton cross section dominated by diffractive interactions are presented. Results are compared to Monte Carlo (MC) models with different parameter tunes for the description of the underlying event and to perturbative QCD calculations. The prominent role of multi-parton interactions has been confirmed in the semihard sector but no clear deviation from the standard Dglap parton evolution due to Bfkl has been observed. An outlook to the prospects at 13 TeV is given.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

“Soft” muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Performance of the CMS ME1/1 muon station

ME1/1 is a part of the endcap muon system of the CMS detector at the LHC (CERN). It plays the key role in the muon detection providing muon track matching with the CMS inner tracker. ME1/1 should provide high spatial and time resolution. The station is composed of 72 6-layer cathode strip chambers. The chambers were designed and produced at JINR (Dubna). ME1/1 is the innermost muon detector and operates in the strong axial magnetic field (B = 3 T) in the presence of high background of secondary particles. The paper presents the chamber design features and the ME1/1 station characteristics in the CMS runs with proton-proton collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Quality control of silicon pixel wafers for the CMS Phase-1 pixel upgrade

The CMS detector at the CERN Large Hadron Collider features as its innermost component a silicon pixel detector. The original pixel detector was completely replaced during the 2016-2017 winter technical stop. One of the goals of this Phase-1 Upgrade of the pixel detector was to replace the sensors in the original CMS forward pixel detector with new, unirradiated sensors. The new CMS forward pixel detector must survive an integrated luminosity of 300 fb$^{-1}$ before being replaced again prior to the High-Luminosity LHC era. Just as in the original construction, the Phase-1 forward pixel sensors were made of n$^{+}$-in-n Diffusion Oxygenated Float Zone silicon. This note documents the quality spot-checking of the new sensors, comparing our results with those provided by the vendor. In general there was good agreement between the results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

FACET: a new long-lived particle detector in the very forward region of the CMS experiment

We describe a proposal to add a set of very forward detectors to the CMS experiment for the high-luminosity era of the Large Hadron Collider to search for beyond the standard model long-lived particles, such as dark photons, heavy neutral leptons, axion-like particles, and dark Higgs bosons. The proposed subsystem is called FACET for Forward-Aperture CMS ExTension, and will be sensitive to any particles that can penetrate at least 50 m of magnetized iron and decay in an 18 m long, 1 m diameter vacuum pipe. The decay products will be measured in detectors using identical technology to the planned CMS Phase-2 upgrade.

Particle and Resonance Production↗
DOE OSTI2017

Integration of end-user Cloud storage for CMS analysis

End-user Cloud storage is increasing rapidly in popularity in research communities thanks to the collaboration capabilities it offers, namely synchronisation and sharing. CERN IT has implemented a model of such storage named, CERNBox, integrated with the CERN AuthN and AuthZ services. To exploit the use of the end-user Cloud storage for the distributed data analysis activity, the CMS experiment has started the integration of CERNBox as a Grid resource. This will allow CMS users to make use of their own storage in the Cloud for their analysis activities as well as to benefit from synchronisation and sharing capabilities to achieve results faster and more effectively. It will provide an integration model of Cloud storages in the Grid, which is implemented and commissioned over the world’s largest computing Grid infrastructure, Worldwide LHC Computing Grid (WLCG). In this paper, we present the integration strategy and infrastructure changes needed in order to transparently integrate end-user Cloud storage with the CMS distributed computing model. We describe the new challenges faced in data management between Grid and Cloud and how they were addressed, along with details of the support for Cloud storage recently introduced into the WLCG data movement middleware, FTS3. Finally, the commissioning experience of CERNBox for the distributed data analysis activity is also presented.

97 MATHEMATICS AND COMPUTING↗