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DOE OSTI2023

Measurements of the suppression and correlations of dijets in Xe+Xe collisions at $\sqrt{s{NN}}$ = 5.44 TeV

Measurements of the suppression and correlations of dijets is performed using 3 µb –1 of Xe+Xe data at $\sqrt{s{NN}}$ = 5.44 TeV collected with the ATLAS detector at the CERN Large Hadron Collider. Dijets with jets reconstructed using the R = 0.4 anti- k t algorithm are measured differentially in jet p T over the range of 32 to 398 GeV and the centrality of the collisions. Significant dijet momentum imbalance is found in the most central Xe+Xe collisions, which decreases in more peripheral collisions. Results from the measurement of per-pair normalized and absolutely normalized dijet p T balance are compared with previous Pb + Pb measurements at $\sqrt{s{NN}}$ = 5.02 TeV. The differences between the dijet suppression in Xe+Xe and Pb + Pb are further quantified by the ratio of pair nuclear-modification factors. The results are found to be consistent with those measured in Pb + Pb data when compared in classes of the same event activity and when taking into account the difference between the center-of-mass energies of the initial parton scattering process in Xe+Xe and Pb + Pb collisions. These results should provide input for a better understanding of the role of energy density, system size, path length, and fluctuations in the parton energy loss.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Azimuthal correlations of prompt D mesons with charged particles in pp and p–Pb collisions at ${\sqrt{s_\mathrm{NN}}} = 5.02\ \hbox {TeV}$

The measurement of the azimuthal-correlation function of prompt D mesons with charged particles in pp collisions at $\sqrt{s} =5.02\ \hbox {TeV}$ and p–Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02\ \hbox {TeV}$ with the ALICE detector at the LHC is reported. The $\mathrm{D}^{0}$ , $\mathrm{D}^{+} $ , and $\mathrm{D}^{*+} $ mesons, together with their charge conjugates, were reconstructed at midrapidity in the transverse momentum interval $3< p_\mathrm{T} < 24\ \hbox {GeV}/c$ and correlated with charged particles having $p_\mathrm{T} > 0.3\ \hbox {GeV}/c$ and pseudorapidity $|\eta | < 0.8$ . The properties of the correlation peaks appearing in the near- and away-side regions (for $\Delta \varphi \approx 0$ and $\Delta \varphi \approx \pi $ , respectively) were extracted via a fit to the azimuthal correlation functions. The shape of the correlation functions and the near- and away-side peak features are found to be consistent in pp and p–Pb collisions, showing no modifications due to nuclear effects within uncertainties. The results are compared with predictions from Monte Carlo simulations performed with the PYTHIA, POWHEG+PYTHIA, HERWIG, and EPOS 3 event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Studying charm hadronisation into baryons with azimuthal correlations of Λ$^+_c$ with charged particles in pp collisions at $\sqrt{𝑠}$ = 13 TeV

The distribution of angular correlations between prompt charm hadrons and primary charged particles in pp collisions is sensitive to the charm-quark hadronisation process. In this letter, charm-baryon correlations are measured for the first time by studying the azimuthal-angle difference between charged particles and prompt Λ$^+_c$ baryons produced in pp collisions at a centre-of-mass energy of $\sqrt{𝑠}$ = 13 TeV, with the ALICE detector. Λ$^+_c$ baryons are reconstructed at midrapidity (|𝑦| < 0.5) in the transverse-momentum interval 3 < 𝑝 T < 16 GeV/𝑐, and correlated with charged particles with 𝑝 T > 0.3 GeV/𝑐 and pseudorapidity |𝜂| < 0.8. For 3 < 𝑝$^{Λ{^{+}_{c} ,D}}_{T}$ < 5 GeV/𝑐, the comparison with published measurements of D-meson and charged-particle correlations in the same collision system hints at a larger number of low-momentum particles associated with Λ$^+_c$-baryon triggers than with D-meson triggers, both in the collinear and opposite directions with respect to the trigger particle. These differences can be quantified by the comparison of the properties of the near- and away-side correlation peaks, and are not reproduced by predictions of various Monte Carlo event generators, generally underpredicting the associated particle yields at 𝑝$^{assoc}_{T}$ < 1 GeV/𝑐. This tension between Λ$^+_c$-baryon and D-meson associated peak yields could suggest a modified fragmentation of the charm quark, or a different hadronisation process, when a charm baryon is produced in the final state.

Acharya, S. (ORCID:0000000292135329)↗

DOE OSTI2025

Measurement of inclusive jet cross section and substructure in 𝑝 + 𝑝 collisions at $\sqrt{s}$ = 200 GeV

The jet cross section and jet-substructure observables in 𝑝 + 𝑝 collisions at $\sqrt{s}$ =200 GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-𝑘 𝑡 algorithm with a jet radius of 𝑅 = 0.3 for jets with transverse momentum within 8.0 < 𝑝 𝑇 < 40.0 GeV/𝑐 and pseudorapidity |𝜂| < 0.15. Measurements include the jet cross section, as well as distributions of SoftDrop-groomed momentum fraction (𝑧 𝑔 ), charged-particle transverse momentum with respect to jet axis (𝑗 𝑇 ), and radial distributions of charged particles within jets (𝑟). Also measured was the distribution of 𝜉 = −ln⁡(𝑧), where 𝑧 is the fraction of the jet momentum carried by the charged particle. The measurements are compared to theoretical next-to and next-to-next-to-leading-order calculations, the PYTHIA and H erwig event generators, and to other existing experimental results. Indicated from these measurements is a lower particle multiplicity in jets at RHIC energies when compared to models. Also noted are implications for future jet measurements with sPHENIX at RHIC as well as at the future Electron-Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Efficient Data Management in Neutron Scattering Data Reduction Workflows at ORNL

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2 TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11% to 30% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase.

Godoy, William↗

DOE OSTI2025

Metagenome-assembled genomes measured at 3 depths during snowmelt period in East River, CO (March, May, and June, September 2017)

Snowmelt is a critical biogeochemical period that accounts for large nitrogen (N) export events from high-elevation watersheds. Soil microbial populations bloom and immobilize N during snowmelt, yet the population size crashes in spring, which releases a pulse of soil N. We sought to discover the N sources fueling this microbial bloom and determine the fate of N following microbial die-off. Here, focusing on the snowmelt period within a headwater catchment of the Upper Colorado River Basin (East River, CO), we deployed strain-resolved metagenomics to identify the metabolic pathways and processes that mobilize soil N during and after snowmelt. Soil metagenome samples were taken from 6 snowpits from 3 depths (0-5cm, 5-15cm, >15cm) at 4 time points during snowmelt period (March 2017, May 2017, and June 2017, September 2017) generating 48 metagenomes. We reconstructed 474 metagenome-assembled genomes (MAGs) across all metagenomes.All 48 metagenomes were sequenced at JGI and raw data can be found under JGI (Joint Genome Institute) GOLD Study Gs0135149. Metagenome assemblies from IMG under the same study were used for genome binning. This dataset (1) a zip file of 474 MAGs (as fasta files, Gs0135149_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0135149.kml), (4) metagenome metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (metagenomes.csv), (5) location metadata file (locations.csv), (6) file-level metadata file (flmd.csv) and (7) data dictionary (dd.csv) file.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Accelerating the Inference of the Exa.TrkX Pipeline

Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Configuration and performance of the ATLAS b -jet triggers in Run 2

Several improvements to the ATLAS triggers used to identify jets containing b-hadrons (b-jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b-jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b-jet identification efficiency compared to the performance in Run 1 (2011–2012). The efficiency to identify b-jets in the trigger, and the conditional efficiency for b-jets that satisfy offline b-tagging requirements to pass the trigger are also measured. Correction factors are derived to calibrate the b-tagging efficiency in simulation to match that observed in data. The associated systematic uncertainties are substantially smaller than in previous measurements. In addition, b-jet triggers were operated for the first time during heavy-ion data-taking, using dedicated triggers that were developed to identify semileptonic b-hadron decays by selecting events with geometrically overlapping muons and jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

The Global Structure of the Milky Way’s Stellar Halo Based on the Orbits of Local Metal-poor Stars

We analyze the global structure of the Milky Way (MW)'s stellar halo, including its dominant subcomponent, Gaia-Sausage-Enceladus (GSE). The method for reconstructing the global distribution of this old stellar component is to employ the superposition of the orbits covering the large MW's space, where each of the orbit-weighting factors is assigned following the probability that the star is located at its currently observed position. The selected local, metal-poor sample with [Fe/H] <–1, using Gaia Early Data Release 3 and Sloan Digital Sky Survey Data Release 16, shows that the global shape of the halo is systematically rounder at all radii in more metal-poor ranges, such that an axial ratio, q, is nearly 1 for [Fe/H] <–2.2 and ~0.7 for –1.4 < [Fe/H] < –1.0. It is also found that a halo in the relatively metal-rich range of [Fe/H] >–1.8 actually shows a boxy/peanut-like shape, suggesting a major merger event. The distribution of azimuthal velocities shows a disk-like flattened structure at –1.4 < [Fe/H] < –1.0, which is thought to be the metal-weak thick disk. For the subsample of stars showing GSE-like kinematics, at [Fe/H] >–1.8, its global density distribution has an axis ratio of 0.9, which is more spherical than the general halo sample, and an outer ridge at r ~ 20 kpc. This spherical shape is consistent with the features of accreted halo components, and the ridge suggests that the orbit of GSE's progenitor had an apocenter of ~20 kpc. Implications for the formation of the stellar halo are also presented.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Search for Higgsinos in final states with low-momentum lepton-track pairs at 13 TeV

We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2025

Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation

ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.

Glubokovskikh, Stanislav↗

DOE OSTI2025

Reconstruction framework advancements to support streaming for the ePIC detector at the EIC

The ePIC collaboration adopted the JANA2 framework to manage its reconstruction algorithms. This framework has since evolved substantially in response to ePIC’s needs. There have been three main design drivers: integrating cleanly with the Podio-based data models and other layers of the key4hep stack, enabling external configuration of existing components, and supporting timeframe splitting for streaming readout. The result is a unified component model featuring a new declarative interface for specifying inputs, outputs, parameters, services, and resources. This interface enables the user to instantiate, configure, and wire components via an external file. One critical new addition to the component model is a hierarchical decomposition of data boundaries into levels such as Run, Timeframe, PhysicsEvent, and Subevent. Two new component abstractions, Folder and Unfolder, are introduced in order to traverse this hierarchy, e.g. by splitting or merging. The pre-existing components can now operate at different event levels, and JANA2 will automatically construct the corresponding parallel processing topology. This means that a user may write an algorithm once, and configure it at runtime to operate on timeframes or on physics events. Overall, these changes mean that the user requires less knowledge about the framework internals, obtains greater flexibility with configuration, and gains the ability to reuse the existing abstractions in new streaming contexts.

Brei, Nathan [Thomas Jefferson National Accelerato↗

DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗

DOE OSTI2024

Behavior and mechanisms of Doppler wind lidar error in complex terrain: stable flow case study at Perdigão

A numerical experiment is carried out investigating the magnitude of biases in ground-based lidar measurements in complex flow conditions. Biases assessed include those arising from flow curvature and from the interaction of turbulence with the wind field reconstruction (WFR) algorithms used by a WindCube lidars and anemometers. RANS-CFD and WRF-LES simulations were performed for the Perdig˜ao Field Experiment site for a range of atmospheric conditions. Virtual anemometer and lidar data were generated for four locations: two near exposed ridge tops and two in low-speed regions in the valley. The LES data at these four locations show that the scalar inflation terms (the relation between scalar and vector averaged wind speed) for virtual lidar and virtual cups agree very well with predictions using perturbation theory. While the lidar errors vary greatly with location and height, the contribution from the flow curvature tends to be larger than the differences arising from scalar inflation. For one lidar/mast pair near the ridge top, comparisons between simulations and measurements are carried out for a resonant mountain wave event on June 14th, 2017, and for the whole duration of the Perdigão campaign for winds perpendicular to the ridges. The lidar error during the mountain wave, a period of strong stability and low inversion height, is significantly larger than the campaign average. The sensitivity of the lidar error to atmospheric stability is confirmed by the RANS simulations, which suggests strong sensitivity of flow curvature error to stability conditions and to the shape of the wind speed profile near the top of the boundary layer.

17 WIND ENERGY↗

DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗

DOE OSTI2024

Observation of Enhanced Long-Range Elliptic Anisotropies Inside High-Multiplicity Jets in p p Collisions at s = 13 TeV

A search for collective effects inside jets produced in proton-proton collisions is performed via correlation measurements of charged particles using the CMS detector at the CERN LHC. The analysis uses data collected at a center-of-mass energy of s = 13 TeV , corresponding to an integrated luminosity of 138 fb − 1 . Jets are reconstructed with the anti- k T algorithm with a distance parameter of 0.8 and are required to have transverse momentum greater than 550 GeV and pseudorapidity | η jet | < 1.6 . Two-particle correlations among the charged particles within the jets are studied as functions of the particles’ azimuthal angle and pseudorapidity separations ( Δ ϕ * and Δ η * ) in a jet coordinate basis, where particles’ η * , ϕ * are defined relative to the direction of the jet. The correlation functions are studied in classes of in-jet charged-particle multiplicity up to N ch j ≈ 100 . Fourier harmonics are extracted from long-range azimuthal correlation functions to characterize azimuthal anisotropy for | Δ η * | > 2 . For low- N ch j jets, the long-range elliptic anisotropic harmonic, v 2 * , is observed to decrease with N ch j . This trend is well described by Monte Carlo event generators. However, a rising trend for v 2 * emerges at N ch j ≳ 80 , hinting at a possible onset of collective behavior, which is not reproduced by the models tested. This observation yields new insights into the dynamics of jet evolution in the vacuum. © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Measurement of K*(892) ± production in inelastic pp collisions at the LHC

The first results on K*(892) ± resonance production in inelastic pp collisions at LHC energies of √s = 5.02, 8, and 13 TeV are presented. The K*(892) ± has been reconstructed via its hadronic decay channel K*(892)→±K$^0_S$ +π ± with the ALICE detector. Measurements of transverse momentum distributions, p T -integrated yields, and mean transverse momenta for charged K*(892) are found to be consistent with previous ALICE measurements for neutral K*(892) within uncertainties. For p T >1 GeV/c the K*(892) ± transverse momentum spectra become harder with increasing centre-of-mass energy from 5.02 to 13 TeV, similar to what previously observed for charged kaons and pions. For p T < GeV/c the K*(892) ± yield does not evolve significantly and the abundance of K*(892) ± relative to K is rather independent of the collision energy. The transverse momentum spectra, measured for K*(892) ± at midrapidity in the interval 0 < p T <15 GeV/c, are not well described by predictions of different versions of PYTHIA 6, PYTHIA 8 and EPOS-LHC event generators. These generators reproduce the measured p T -integrated K *± /K ratios and describe well the momentum dependence for p T <2 GeV/c.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗