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DOE OSTI2023

Progress toward Accelogic compression in ROOT

For the last 7 years, Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom-line outcome will be to provide state-of-the-art compression algorithms --and accompanying software libraries-- able to surpass the performance of the compression engines currently available in the ROOT [7] framework. The resulting technology has the capability to enable substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4 [6].As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.In this contribution, we will present our efforts integrating our concepts of “functionally lossless compression” within the ROOT framework implementation, with the purpose of producing a basic solution readily integrated into HENP applications. We will also present our progress applying this compression through realistic examples of analysis from both the STAR and CMS experiments.

Canal, Ph.↗
DOE OSTI2024

New Structures in the J / ψ J / ψ Mass Spectrum in Proton-Proton Collisions at s = 13 TeV

A search is reported for near-threshold structures in the J / ψ J / ψ invariant mass spectrum produced in proton-proton collisions at s = 13 TeV from data collected by the CMS experiment, corresponding to an integrated luminosity of 135 fb − 1 . Three structures are found, and a model with quantum interference among these structures provides a good description of the data. A new structure is observed with a local significance above 5 standard deviations at a mass of 6638 − 38 + 43 ( stat ) − 31 + 16 ( syst ) MeV . Another structure with even higher significance is found at a mass of 6847 − 28 + 44 ( stat ) − 20 + 48 ( syst ) MeV , which is consistent with the X ( 6900 ) resonance reported by the LHCb experiment and confirmed by the ATLAS experiment. Evidence for another new structure, with a local significance of 4.7 standard deviations, is found at a mass of 7134 − 25 + 48 ( stat ) − 15 + 41 ( syst ) MeV . Results are also reported for a model without interference, which does not fit the data as well and shows mass shifts up to 150 MeV relative to the model with interference. © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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↗
DOE OSTI2024

Performance of the CMS high-level trigger during LHC Run 2

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1× 10 34 cm -2 s -1 , twice the initial design value, at √(s)=13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Performance of the CMS high-level trigger during LHC Run 2

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the initial design value, at $\sqrt{s}$ = 13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.

high energy physics↗
DOE OSTI2024

HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs

Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

MiniDAQ-3: Providing concurrent independent subdetector data-taking on CMS production DAQ resources

The data acquisition (DAQ) of the Compact Muon Solenoid (CMS) experiment at CERN, collects data for events accepted by the Level-1 Trigger from the different detector systems and assembles them in an event builder prior to making them available for further selection in the High Level Trigger, and finally storing the selected events for offline analysis. In addition to the central DAQ providing global acquisition functionality, several separate, so-called “MiniDAQ” setups allow operating independent data acquisition runs using an arbitrary subset of the CMS subdetectors. During Run 2 of the LHC, MiniDAQ setups were running their event builder and High Level Trigger applications on dedicated resources, separate from those used for the central DAQ. This cleanly separated MiniDAQ setups from the central DAQ system, but also meant limited throughput and a fixed number of possible MiniDAQ setups. In Run 3, MiniDAQ-3 setups share production resources with the new central DAQ system, allowing each setup to operate at the maximum Level-1 rate thanks to the reuse of the resources and network bandwidth. Configuration management tools had to be significantly extended to support the synchronization of the DAQ configurations needed for the various setups. We report on the new configuration management features and on the first year of operational experience with the new MiniDAQ-3 system.

Amoiridis, Vassileios↗
DOE OSTI2022

A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery

The standard model of particle physics describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles. The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons—the carriers of the strong, electromagnetic and weak forces—are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (τ)) are well measured and indications of interactions with a second-generation particle (muons, μ) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Study of Heavy Flavor Mesons and Flavor-Tagged Jets with the CMS Detector

The goal of this research program is to implement heavy flavor meson triggers in heavy-ion collisions for the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN, including algorithm design, timing studies, offline validation, and online performance monitoring. The physics analyses which can be achieved by data from these new triggers is to address one of the most important questions in the field: parton flavor dependence of jet-quenching for the understanding of the transport properties of the Quark-Gluon Plasma. This program will allow CMS to collect the highest statistics heavy flavor meson and jet data ever recorded in heavy-ion colliders. The program includes two objectives: (1) Build and maintain the heavy flavor meson and jet triggers for heavy-ion collisions and deploy the trigger algorithms for 2015-2018 PbPb and pPb run at the LHC; (2) Perform heavy flavor meson and jet physics analyses, which can be used to study the parton flavor dependence of jet quenching, to extract the elastic energy loss coefficient of the QGP, and to test whether massive quarks also participate in collective expansion dynamics in heavy-ion collisions. With the heavy flavor physics trigger developed in this project, a competitive heavy flavor physics program in heavy-ion collisions has been established in CMS. This program allows studies of the fully reconstructed and flavor identified charm, beauty, and exotic hadrons that cover the widest transverse momentum range. The novel measurements supported by the award provide new constraints on the size of the flavor dependence of parton energy loss, the value of the in-medium charm quark diffusion coefficient, the mechanism of charm and beauty quark hadronization, and provide new insights to the nature of the X(3872) hadron.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Evidence for $CP$ violation and measurement of $CP$-violating parameters in B$^0_\mathrm{s}$ $\to$ J/$\psi\,\phi$(1020) decays in pp collisions at $\sqrt{s} =$ 13 TeV

A pioneering machine-learning-based flavor-tagging algorithm combining same-side and opposite-side tagging is used to obtain the equivalent of 27$\,$000 tagged B$^0_\mathrm{s}$$\to$ J/$\psi\, \phi$(1020) decays from pp collisions at $\sqrt{s} =$ 13 TeV, collected by the CMS experiment and corresponding to an integrated luminosity of 96.5 fb$^{-1}$. A time- and flavor-dependent angular analysis of the $\mu^+\mu^-$K$^+$K$^-$ final state is used to measure parameters of the $\mathrm{B}^0_\mathrm{s}$-$\overline{\mathrm{B}}^0_\mathrm{s}$ system. The weak phase is measured to be $\phi_\mathrm{s}$ = $-$73 $\pm$ 23 (stat) $\pm$ 7 (syst) mrad, which, combined with a $\sqrt{s}$ = 8 TeV CMS result, gives $\phi_\mathrm{s}$ = $-$74 $\pm$ 23 mrad. This value differs from zero by 3.2 standard deviations, providing evidence for $CP$ violation in B$^0_\mathrm{s}$$\to$ J/$\psi\,\phi$(1020) decays. All measured physics parameters are found to agree with standard model predictions where available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2023

Vectorization of CMSSW offline software

The CMS experiment has been utilizing vectorization, or SIMD, in parts of its data processing applications for over a decade. On x86 platforms the vectorization level is still SSE3. In the past attempts to use wider vector instruction sets such as AVX or AVX-512 have, in practice, not resulted in improvements in the overall event processing throughput, because the CPUs scale down their frequency when processing AVX instructions. In addition, a notable part of the global pool of CMS’ resources has been old systems either not supporting AVX, or where the CPU frequency downscaling impacts all cores of the CPU. CMS has nevertheless continued to vectorize more of its application code, and in this work we review profiling methods we have found effective to find out pieces of code that would benefit from vectorization, and techniques to transform those codes such that the GCC compiler is able to auto-vectorize those codes. The build system used for CMSSW, Scram, has also been enha nced to be able to build code for multiple CPU microarchitectures such that the shared libraries of desired microarchitecture level can be loaded based on the CPU of the system. This multi-microarchitecture setup is invisible to the workflow management system, which makes its deployment straightforward. We describe in detail how this multi-microarchitecture build is set up, and measure the impact of using wider vector units than SSE3 on the event processing throughput of CMS applications such as simulation and reconstruction on recent x86 CPUs.

Gartung, Patrick↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2022

Search for long-lived heavy neutral leptons with displaced vertices in proton-proton collisions at $\sqrt{\mathrm{s}}$ =13 TeV

A search for heavy neutral leptons (HNLs), the right-handed Dirac or Majorana neutrinos, is performed in final states with three charged leptons (electrons or muons) using proton-proton collision data collected by the CMS experiment at √s = 13 TeV at the CERN LHC. The data correspond to an integrated luminosity of 138 fb -1 . The HNLs could be produced through mixing with standard model neutrinos ν. For small values of the HNL mass (<20 GeV) and the square of the HNL-ν mixing parameter (10 -7 –10 -2 ), the decay length of these particles can be large enough so that the secondary vertex of the HNL decay can be resolved with the CMS silicon tracker. The selected final state consists of one lepton emerging from the primary proton-proton collision vertex, and two leptons forming a displaced, secondary vertex. No significant deviations from the standard model expectations are observed, and constraints are obtained on the HNL mass and coupling strength parameters, excluding previously unexplored regions of parameter space in the mass range 1–20 GeV and squared mixing parameter values as low as 10 -7 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Evolution of the CMS Global Submission Infrastructure for the HL-LHC Era

Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources provided by traditional Grid sites, being also much more diverse and heterogeneous. Handling their special capabilities or limitations and maintaining global flexibility and efficiency, while also operating at scales much higher than the current capacity, are the major challenges being addressed by the CMS Submission Infrastructure team. These proceedings discuss the risks to the stability and scalability of the CMS HTCondor infrastructure extrapolated to such a scenario, thought to be derived mostly from its growing complexity, with multiple Negotiators and schedulers flocking work to multiple federated pools. New mechanisms for enhanced customization and control over resource allocation and usage, mandatory in this future scenario, are also described.

Yzquierdo, Antonio Pérez-Calero↗
DOE OSTI2025

On-Demand Column Joining for High Energy Physics

As the Large Hadron Collider (LHC) transitions into the High-Luminosity LHC (HL-LHC) era, the volume of data to be processed is expected to increase significantly. The CMS Experiment currently utilizes various data formats, including AOD, MiniAOD, and NanoAOD, each with different levels of detail and storage requirements. This paper addresses the challenges of data duplication and storage inefficiencies in high-energy physics (HEP) analyses by proposing an on-demand column-joining solution. This approach aims to reduce data duplication by enabling the dynamic combination of NanoAOD data with auxiliary information from larger data tiers, such as MiniAOD. The proposed solution leverages Trino, a high-performance distributed SQL query engine, to perform efficient and scalable data joins. Benchmarks using CMS OpenData demonstrate the feasibility of this approach, showing that it can handle large datasets with low latency. Integration with the scikit-hep ecosystem and the coffea analysis framework is also discussed, highlighting the potential for seamless end-to-end data processing and analysis. Ongoing and future work focuses on expanding benchmarks, integrating ServiceX for data transformation, and exploring the use of native object storage solutions.

Manganelli, Nicholas [Northeastern U.]↗
DOE OSTI2020

First measurements with the CMS DAQ and Timing Hub prototype-1

The DAQ and Timing Hub is an ATCA hub board designed for the Phase-2 upgrade of the CMS experiment. In addition to providing high-speed Ethernet connectivity to all back-end boards, it forms the bridge between the sub-detector electronics and the central DAQ, timing, and trigger control systems. One important requirement is the distribution of several high-precision, phase-stable, and LHC synchronous clock signals for use by the timing detectors. The current paper presents first measurements performed on the initial prototype, with a focus on clock quality. It is demonstrated that the current design provides adequate clock quality to satisfy the requirements of the Phase-2 CMS timing detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Measurement of Energy Correlators inside Jets and Determination of the Strong Coupling α S ( m Z )

Energy correlators that describe energy-weighted distances between two or three particles in a hadronic jet are measured using an event sample of s = 13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an integrated luminosity of 36.3 fb − 1 . The measured distributions are consistent with the trends in the simulation that reveal two key features of the strong interaction: confinement and asymptotic freedom. By comparing the ratio of the measured three- and two-particle energy correlator distributions with theoretical calculations that resum collinear emissions at approximate next-to-next-to-leading-logarithmic accuracy matched to a next-to-leading-order calculation, the strong coupling is determined at the Z boson mass: α S ( m Z ) = 0.122 9 − 0.0050 + 0.0040 , the most precise α S ( m Z ) value obtained using jet substructure observables. © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗