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At least 109 records · Page 6

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↗

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↗

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↗

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’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↗

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↗

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↗

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]↗

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 ↗

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↗

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↗

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↗

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 ↗

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↗

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↗

Mass independent sulfur isotope signatures in CMs: Implications for sulfur chemistry in the early solar system

In this study, we have investigated the quadruple sulfur isotopic composition of inorganic sulfur-bearing phases from 13 carbonaceous chondrites of CM type. Our samples include 4 falls and 9 Antarctic finds. We extracted sulfur from sulfides, sulfates, and elemental sulfur (S 0 ) from all samples. On average, we recover a bulk sulfur (S) content of 2.11 ± 0.39 wt.% S (1σ). The recovered sulfate, S 0 and sulfide contents represent 25 ± 12%, 10 ± 7% and 65 ± 15% of the bulk S, respectively (all 1σ). There is no evidence for differences in the bulk S content between falls and finds, and there is no correlation between the S speciation and the extent of aqueous alteration. We report ranges of Δ 33 S and Δ 36 S values in CMs that are significantly larger than previously observed. The largest variations are exhibited by S 0 , with Δ 33 S values ranging between-0.104 ± 0.012‰ and +0.256 ± 0.018‰ (2σ). The Δ 36 S/ 33 S ratios of S 0 are on average -3.1 ± 1.0 (2σ). Two CMs show distinct Δ 36 S/ 33 S ratios, of +1.3 ± 0.1 and +0.9 ± 0.1. We suggest that these mass independent S isotopic compositions record H 2 S photodissociation in the nebula. The varying Δ 36 S/Δ 33 S ratios are interpreted to reflect photodissociation that occurred at different UV wavelengths. The preservation of these isotopic features requires that the S-bearing phases were heterogeneously accreted to the CM parent body. Non-zero Δ 33 S values are also preserved in sulfide and sulfate, and are positively correlated with S 0 values. This indicates a genetic relationship between the S-bearing phases: We argue that sulfates were produced by the direct oxidation of S 0 (not sulfide) in the parent body. We describe two types of models that, although imperfect, can explain the major features of the CM S isotope compositions, and can be tested in future studies. Sulfide and S 0 could both be condensates from the nebula, as the residue and product, respectively, of incomplete H 2 S photodissociation by UV light (wavelength <150 nm). This idea requires that FeS formation and the S 0 condensation co-occur. As an alternative, ice accretion to the CM parent body could allow the delivery of S-MIF in CMs. In that case, sulfides would have been the only S-bearing condensate in CM precursors, and S 0 would have been derived from the oxidation of H 2 S trapped in ices, after its photodissociation at low temperature (<500 K) in the nebula. In our models, the observations of H 2 S UV photodissociation is required to occur at the disk surface, and allowed in nebular environments with canonical C/O ratios. Lastly, vertical motions in the disk would redistribute phases that condensed at high altitude to the midplane, where they accreted in the phases that make up the chondritic matrix.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Results from the CBC3 readout ASIC for CMS 2S-modules

The CBC3 is the latest version of the CMS Binary Chip for readout of the outer radial region of the upgraded CMS Tracker at the High Luminosity LHC. This 254-channel, 130 nm CMOS ASIC is designed to be bump-bonded to a substrate to which sensors will be wire-bonded. It will instrument double-layer 2S-modules, containing two overlaid silicon microstrip sensors, aligned with a parallel orientation. On-chip logic identifies Level-1 trigger primitives from high transverse-momentum tracks by selecting correlated clusters in the two sensors. The CBC3 was delivered in late 2016; wafer probing and performance tests have been carried out. Several prototype modules using the CBC3 have been produced and tested in the lab and in different beams. The results show that the CBC3 satisfies CMS requirements and only small corrections are needed for the final version of the chip for production.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A portrait of the Higgs boson by the CMS experiment ten years after the discovery

In July 2012, the ATLAS and CMS collaborations at the CERN Large Hadron Collider announced the observation of a Higgs boson at a mass of around 125 gigaelectronvolts. Ten years later, and with the data corresponding to the production of a 30-times larger number of Higgs bosons, we have learnt much more about the properties of the Higgs boson. The CMS experiment has observed the Higgs boson in numerous fermionic and bosonic decay channels, established its spin–parity quantum numbers, determined its mass and measured its production cross-sections in various modes. Here the CMS Collaboration reports the most up-to-date combination of results on the properties of the Higgs boson, including the most stringent limit on the cross-section for the production of a pair of Higgs bosons, on the basis of data from proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts. Within the uncertainties, all these observations are compatible with the predictions of the standard model of elementary particle physics. Much evidence points to the fact that the standard model is a low-energy approximation of a more comprehensive theory. Several of the standard model issues originate in the sector of Higgs boson physics. An order of magnitude larger number of Higgs bosons, expected to be examined over the next 15 years, will help deepen our understanding of this crucial sector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗