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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Search for New Physics in Jet Multiplicity Patterns of Multilepton Events at $\sqrt{s}$ = 13 TeV

A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of 138 fb −1 of 13 TeV proton-proton collisions recorded by the CMS detector at the LHC. The search uses observed jet multiplicity distributions in one-, two-, and four-lepton events to explore possible enhancements in jet production rate in three-lepton events with and without bottom quarks. The data are found to be consistent with the standard model expectation. The results are interpreted in terms of supersymmetric production of electroweak chargino-neutralino superpartners with cascade decays terminating in prompt hadronic 𝑅-parity violating interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Foundation model framework for all tasks involving jet physics

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the omnilearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the omnilearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over 1 × 10 9 jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate omnilearned with three representative tasks: top-quark jet tagging with the community delphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, omnilearned is the state of the art, further expanding the discovery potential of past, current, and future collider experiments.

Bhimji, Wahid [Lawrence Berkeley National Laborato↗
DOE OSTI2024

Jet modification via 𝜋 0 -hadron correlations in Au + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4–12 GeV/𝑐 and 0.5–7 GeV/𝑐, respectively, have been measured by the PHENIX experiment in 2014 for Au + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au + Au collisions and 𝑝 + 𝑝 collisions, 𝐼 𝐴⁢𝐴 and Δ 𝐴⁢𝐴 , as a function of the trigger-hadron azimuthal separation, Δ⁢𝜙, are measured for the first time at the BNL Relativistic Heavy Ion Collider. Finally, these results better quantify how the yield of low-𝑝 𝑇 associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Differentiable vertex fitting for jet flavor tagging

This work explores the use of differentiable programming to integrate domain knowledge, in the form of domain specific software, into neural networks to develop scientific machine learning systems. We propose a differentiable vertex fitting algorithm that estimates the crossing point of multiple curves. In the high energy physics setting, these curves are defined by particle equations of motion and the crossing point represents the origin of particle production. This differentiable vertex fitting algorithm can be seamlessly integrated into neural networks, and we show its utility and efficacy in the high energy physics application of the classification of jets, i.e., collimated streams of particles in particle detectors whose originating parent particle we aim to classify. We demonstrate how differentiable vertex fitting can be integrated into larger transformer-based models for jet flavor tagging and show improvements in heavy flavor jet classification when compared to baseline models. Published by the American Physical Society 2024

Smith, Rachel E. C. (ORCID:0000000335851262)↗
DOE OSTI2025

Enhancing charge ratio sensitivity to hadronization effects via jet selections on resolved SoftDrop splitting

The study of quantum chromodynamics (QCD) at ultrarelativistic energies can be performed in a controlled environment through lepton-hadron deep inelastic scatterings. In such collisions, the high-energy partonic emissions that follow from the ejected hard partons are accurately described by perturbative QCD. However, the lower energy scales at which quarks and gluons experience color confinement, i.e., hadronization mechanism, fall outside the validity regions for perturbative calculations, requiring phenomenological models tuned to data to describe it. As such, hadronization physics cannot be currently derived from first principles alone. Monte Carlo event generators are useful tools to describe these processes as they simulate both the perturbative and the nonperturbative interactions, with model-dependent energy scales that control parton dynamics. This work employs jets—experimental reconstructions of final-state particles likely to have a common partonic origin—to inspect this transition further. Although originally proposed to circumvent hadronization effects, we show that jets can be utilized as probes of nonperturbative phenomena via their substructure. The charge correlation ratio was recently shown to be sensitive to hadronization effects. Our work further improves this sensitivity to nonperturbative scales by introducing a new selection based on the relative placement of the within the clustering tree, defined as the unclustering that resolves the jet’s leading charged particles. Published by the American Physical Society 2025

Apolinário, Liliana (ORCID:0000000335009681)↗
DOE OSTI2025

Impact of LHC precision measurements of inclusive jet and dijet production on the CTEQ-TEA global PDF fit

In this study, we investigate the impact of new Large Hadron Collider (LHC) inclusive jet and dijet measurements on parton distribution functions (PDFs) that describe the proton structure, with a particular focus on the gluon distribution at large momentum fraction, x , and the corresponding partonic luminosities. We assess constraints from these datasets using next-to-next-to-leading-order (NNLO) theoretical predictions, accounting for a range of uncertainties from scale dependence and numerical integration. From the scale choices available for the calculations, our analysis shows that the central predictions for inclusive jet production show a smaller scale dependence than dijet production. We examine the relative constraints on the gluon distribution provided by the inclusive jet and dijet distributions, and also explore the phenomenological implications for inclusive H , t t ¯ , and t t ¯ H production at the LHC at 14 TeV. Published by the American Physical Society 2025

Ablat, Alim (ORCID:0009000519877755)↗
DOE OSTI2025

Measurement of photonuclear jet production in ultraperipheral Pb + Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ATLAS detector

In ultrarelativistic heavy ion collisions at the LHC, each nucleus acts a sources of high-energy real photons that can scatter off the opposing nucleus in ultraperipheral photonuclear (𝛾 + 𝐴) collisions. Hard scattering processes initiated by the photons in such collisions provide a novel method for probing nuclear parton distributions in a kinematic region not easily accessible to other measurements. ATLAS has measured production of dijet and multijet final states in ultraperipheral Pb + Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV using a dataset recorded in 2018 with an integrated luminosity of 1.72 nb −1 . Photonuclear final states are selected by requiring a rapidity gap in the photon direction; this selects events where one of the outgoing nuclei remains intact. Jets are reconstructed using the anti-𝑘 t algorithm with radius parameter, 𝑅 = 0.4. Triple-differential cross sections, unfolded for detector response, are measured and presented using two sets of kinematic variables. The first set consists of the total transverse momentum (𝐻 T ), rapidity, and mass of the jet system. The second set uses 𝐻 T and particle-level nuclear and photon parton momentum fractions, 𝑥 A and 𝑧 𝛾 , respectively. The results are compared with leading-order perturbative QCD calculations of photonuclear jet production cross sections, where all leading order predictions using existing fits fall below the data in the shadowing region. More detailed theoretical comparisons will allow these results to strongly constrain nuclear parton distributions, and these data provide results from the LHC directly comparable to early physics results at the planned Electron-Ion Collider.

Parton distribution functions↗
DOE OSTI2024

Estimating Scaled Cloud Height in Pulse Jet Mixed Vessels

To suspend and mix radioactive slurries without pumps or impellers, the performance of pulsed jet mixed vessels has been investigated. The slurry in the pulse tubes is expelled impinging upon the vessel floor and lifting solids to a “cloud height” in a cyclic process. Experiments measuring cloud height for a variety of waste simulants have been conducted for vessel configurations that varied vessel diameter, fill height and pulse tube number, volume, and nozzle diameter. The elevation to which suspended particles rise is termed the cloud height and derives from a balance between the fluid forces of the jet and the gravitational forces acting on the particle ensemble. Here we use scaling to estimate the cloud height based on conservation of mass and momentum and jet similarity. We find that the cloud height varies with the nozzle exit velocity to the 2/3 power in agreement with observations.

cloud height, effective cleaning radius, effective↗
DOE OSTI2025

Estimation of backgrounds from jets misidentified as τ-leptons using the Universal Fake Factor method with the ATLAS detector

Processes with τ$$\tau $$-leptons in the final state are important for Standard Model measurements and searches for physics beyond the Standard Model. The ATLAS experiment at the Large Hadron Collider observes τ$$\tau $$-leptons produced in proton–proton collisions only through their decay products. Data analyses involving hadronically decaying τ$$\tau $$-leptons face challenges due to backgrounds from jets misidentified as τ$$\tau $$-leptons that are not modelled reliably by Monte Carlo simulations. Data-driven methods such as the fake-factor method allow such misidentified backgrounds to be predicted by measuring transfer factors, known as fake factors, in data from dedicated regions. This paper describes a refined technique for determining the fake factors, the Universal Fake Factor method. It evaluates the fake factors for a signal region by using fake factors from samples enriched in different sources of jets misidentified as τ$$\tau $$-leptons (light-quark, gluon, b-quark, and pile-up jets). Each fake factor is calculated as a linear combination of fake factors measured in these different enriched samples. For the full Run 2 data set, the systematic uncertainty of the calculated fake factors, evaluated using W(μν)$$W(\mu u )$$ enriched event sample, ranges from 15 to 35% depending on the τ$$\tau $$-lepton’s transverse momentum and charged-particle decay multiplicity.

Aad, G↗
DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Binder jet additive manufacturing of silicon carbide solar reactor

Achieving high powder packing density is critical in binder jet additive manufacturing (BJAM), as it directly influences the final part density, mechanical properties, and sintering behavior. Multi-modal powder blends, which combine particles of different sizes, have been explored as a strategy to optimize packing efficiency and minimize defects. In this study, bimodal and trimodal powder blends were obtained by mixing silicon carbide powder in three different sizes. These results show that increased powder density is achievable with bimodal powder blends but is reduced in trimodal blends, and it was found that a 13% increase in the powder tap density was achieved using a bimodal blend of powder. The powder size distribution of the bimodal blend was measured at various stages during binder jet additive manufacturing and, no measurable powder separation occurred even after eight prints. Overall, this study shows limited advantage to trimodal powder blends but good promise for trimodal blends for increasing printed density while maintaining reusability in the binder jet process.

36 MATERIALS SCIENCE↗
DOE OSTI2025

sPHENIX heavy flavor jet tagging studies in p+p at $\sqrt{s_{NN}}=200~GeV$

Heavy-flavor jets, which are initiated from heavy quarks, are ideal probes for studying flavor dependent parton energy loss. We report on the performance of jet flavor tagging using two Neural Network Machine Learning (ML) models: the Long Short-Term Memory (LSTM) model and an Attention-based Neural Network, in simulations of 200 GeV p + p collisions. The tagging performance of bottom quark initiated jets with both ML models surpasses that of the traditional cut-based method. Technical details, including sample and kinematic variable selections, the machine learning training and testing setup with parameter tuning, and outcome comparisons, will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

The Liquid Jet Endstation for Hard X-ray Scattering and Spectroscopy at the Linac Coherent Light Source

The ability to study chemical dynamics on ultrafast timescales has greatly advanced with the introduction of X-ray free electron lasers (XFELs) providing short pulses of intense X-rays tailored to probe atomic structure and electronic configuration. Fully exploiting the full potential of XFELs requires specialized experimental endstations along with the development of techniques and methods to successfully carry out experiments. The liquid jet endstation (LJE) at the Linac Coherent Light Source (LCLS) has been developed to study photochemistry and biochemistry in solution systems using a combination of X-ray solution scattering (XSS), X-ray absorption spectroscopy (XAS), and X-ray emission spectroscopy (XES). The pump–probe setup utilizes an optical laser to excite the sample, which is subsequently probed by a hard X-ray pulse to resolve structural and electronic dynamics at their intrinsic femtosecond timescales. The LJE ensures reliable sample delivery to the X-ray interaction point via various liquid jets, enabling rapid replenishment of thin samples with millimolar concentrations and low sample volumes at the 120 Hz repetition rate of the LCLS beam. This paper provides a detailed description of the LJE design and of the techniques it enables, with an emphasis on the diagnostics required for real-time monitoring of the liquid jet and on the spatiotemporal overlap methods used to optimize the signal. Additionally, various scientific examples are discussed, highlighting the versatility of the LJE.

EXAFS↗
DOE OSTI2025

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

17 WIND ENERGY↗
DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficiency of ML Anomaly Detection Triggers for Emerging Jets

Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by traditional triggers relying on basic kinematic variable selections. Consequently, they have the potential to enhance signals indicative of physics beyond the Standard Model, such as those associated with emerging jets. Such signals are predicted by models featuring a composite dark sector where long-lived particles decay into Standard Model jets with displaced tracks and numerous vertices. This study evaluates the efficiency of these anomaly detection triggers in selecting events with emerging jets produced via the s-channel production of two dark quarks.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Search for resonant pair production of Higgs bosons in the $\textrm{b}\overline{\textrm{b}}\textrm{b}\overline{\textrm{b}}$ final state using large-area jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search is presented for the resonant production of a pair of standard model-like Higgs bosons using data from proton-proton collisions at a centre-of-mass energy of 13 TeV, collected by the CMS experiment at the CERN LHC in 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The final state consists of two b quark-antiquark pairs. The search is conducted in the region of phase space where at least one of the pairs is highly Lorentz-boosted and is reconstructed as a single large-area jet. The other pair may be either similarly merged or resolved, the latter reconstructed using two b-tagged jets. The data are found to be consistent with standard model processes and are interpreted as 95% confidence level upper limits on the product of the cross sections and the branching fractions of the spin-0 radion and the spin-2 bulk graviton that arise in warped extradimensional models. The limits set are in the range 9.74–0.29 fb and 4.94–0.19 fb for a narrow radion and a graviton, respectively, with masses between 1 and 3 TeV. For a radion and for a bulk graviton with widths 10% of their masses, the limits are in the range 12.5–0.35 fb and 8.23–0.23 fb, respectively, for the same masses. These limits result in the exclusion of a narrow-width graviton with a mass below 1.2 TeV, and of narrow and 10%-width radions with masses below 2.6, and 2.9 TeV, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Heavy flavour jet substructure

We present a comprehensive study of energy correlation functions and jet angularities for heavy-flavour QCD jets. In particular, we discuss the possibility of using these observables to expose the dead cone effect, i.e. the suppression of collinear QCD radiation around massive quarks, and to investigate the sensitivity of different observable definitions to the presence of quark masses. Our calculations are presented as all-order resummed predictions at next-to-leading-logarithmic accuracy, matched to (partial) fixed-order results to obtain a better description of the transition around the dead cone threshold. We also compare our analytic results with Pythia, Herwig and Sherpa Monte Carlo predictions to estimate the impact of non-perturbative contributions such as hadronisation, underlying events and B-hadron decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗