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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Search for the doubly heavy $ {\Xi}_{bc}^0 $ baryon via decays to D0pK-

A search for the doubly heavy \( {\Xi}_{bc}^0 \) baryon using its decay to the D 0 pK – final state is performed using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment between 2016 and 2018, corresponding to an integrated luminosity of 5.4 fb - 1 . No significant signal is found in the invariant mass range from 6.7 to 7.2 GeV/ c 2 . Upper limits are set at 95% credibility level on the ratio of the \( {\Xi}_{bc}^0 \) production cross-section times its branching fraction to D 0 pK - relative to that of the \( {\Lambda}_b^0\to {D}^0{pK}^{-} \) decay. The limits are set as a function of the \( {\Xi}_{bc}^0 \) mass and lifetime hypotheses, in the rapidity range from 2.0 to 4.5 and in the transverse momentum region from 5 to 25 GeV/ c . Upper limits range from 1 . 7 × 10 - 2 to 3 . 0 × 10 - 1 for the considered \( {\Xi}_{bc}^0 \) mass and lifetime hypotheses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗
DOE OSTI2020

Measurement of away-side broadening with self-subtraction of flow in Au+Au collisions at ${{\sqrt{s_{NN}} = 200}}$ GeV

High transverse momentum ($p_T$) particle production is suppressed due to parton (jet) energy loss in the hot dense medium created in relativistic heavy-ion collisions. Redistribution of energy at low-to-modest $p_T$ has been elusive to measure because of large anisotropic backgrounds. Here, we report a novel data-driven method for background evaluation and subtraction, exploiting the away-side pseudorapidity gaps, to measure the jetlike correlation shape in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV with the STAR experiment. The correlation shapes, for trigger particle $p_T$ > 3 GeV/$c$ and various associated particle $p_T$ ranges within 0.5 < $p_T$ < 10 GeV/$c$, are consistent with Gaussians and their widths are found to increase with centrality. The results indicate jet broadening in the medium created in central heavy-ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2026

Transverse single-spin asymmetry of forward 𝜂 mesons in 𝑝 ↑ +𝑝 collisions at √𝑠 =200 GeV

Utilizing the 2012 transversely polarized proton data from the Relativistic Heavy Ion Collider at Brookhaven National Laboratory, the forward 𝜂-meson transverse single-spin asymmetry (𝐴 𝑁 ) was measured for 𝑝 ↑ +𝑝 collisions at √𝑠 =200 GeV as a function of Feynman-x (𝑥 𝐹 ) for 0.2 <|𝑥 𝐹 | <0.8 and transverse momentum (𝑝 𝑇 ) for 1.0 <𝑝 𝑇 <5.0 GeV/𝑐. Large asymmetries at positive 𝑥 𝐹 are observed (⟨𝐴 𝑁 ⟩=0.086±0.019), agreeing well with previous measurements of 𝜋 0 and 𝜂𝐴 𝑁 , but with reach to higher 𝑥 𝐹 and 𝑝 𝑇 . The contribution of initial-state spin-momentum correlations to the asymmetry, as calculated in the collinear twist-3 framework, appears insufficient to describe the data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Custodial symmetry breaking and Higgs boson signatures at the LHC

We discuss the simplest model that predicts a tree-level modification of the ρ parameter from a shift in the W -mass without changing the prediction for the Z mass. This model predicts a new neutral Higgs and two charged Higgses, with very similar masses and suppressed couplings to the Standard Model fermions. We discuss the decay properties of these new scalar bosons, and the main signatures at the Large Hadron Collider. Comparing these signatures for the first time to the latest measurements, we show that while masses around 200 GeV are excluded for some scenarios, over a wide range of model parameter space the new bosons can have a mass close to the electroweak scale without violating existing limits from experimental searches or destroying the agreement with measurements. We investigate the scenario where the new neutral Higgs is fermiophobic and has a large branching ratio into W gauge bosons and/or photons, and show that this could lead to a signal in the diphoton mass spectrum at low Higgs masses. We discuss the different signatures that can motivate new measurements and searches at the Large Hadron Collider. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Quantum simulations of hadron dynamics in the Schwinger model using 112 qubits

Hadron wave packets are prepared and time evolved in the Schwinger model using 112 qubits of IBM’s 133-qubit Heron quantum computer ibm_torino. The initialization of the hadron wave packet is performed in two steps. First, the vacuum is prepared across the whole lattice using the recently developed SC-ADAPT-VQE algorithm and workflow. SC-ADAPT-VQE is then extended to the preparation of localized states, and used to establish a hadron wave packet on top of the vacuum. This is done by adaptively constructing low-depth circuits that maximize the overlap with an adiabatically prepared hadron wave packet. Due to the localized nature of the wavepacket, these circuits can be determined on a sequence of small lattices using classical computers, and then robustly scaled to prepare wave packets on large lattices for simulations using quantum computers. Time evolution is implemented with a second-order Trotterization. To reduce both the required qubit connectivity and circuit depth, an approximate quasilocal interaction is introduced. This approximation is made possible by the emergence of confinement at long distances, and converges exponentially with increasing distance of the interactions. Using multiple error-mitigation strategies, up to 14 Trotter steps of time evolution are performed, employing 13,858 two-qubit gates (with a CNOT depth of 370). The propagation of hadrons is clearly identified, with results that compare favorably with Matrix Product State simulations. Finally, prospects for a near-term quantum advantage in simulations of hadron scattering are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Transverse momentum moments

We establish robust relations between Transverse Momentum Dependent distributions (TMDs) and collinear distributions. We define weighted integrals of TMDs that we call Transverse Momentum Moments (TMMs) and prove that TMMs are equal to collinear distributions evaluated in some minimal subtraction scheme. The conversion to the modified minimal subtraction ( MS ¯ ) scheme can be done by a calculable factor, which we derive up to three loops for some cases. We discuss in detail the zeroth, the first, and the second TMMs and provide phenomenological results for them based on the current extractions of TMDs. The results of this paper open new avenues for theoretical and phenomenological investigation of the three-dimensional and collinear hadron structures. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Final state radiation from high and ultrahigh energy neutrino interactions

Charged leptons produced by high-energy and ultrahigh-energy neutrinos have a substantial probability of emitting prompt internal bremsstrahlung ν ℓ + N → ℓ + X + γ . This can have important consequences for neutrino detection. We discuss observable consequences at high- and ultrahigh-energy neutrino telescopes and the Large Hadron Collider’s (LHC’s) Forward Physics Facility. Logarithmic enhancements can be substantial (e.g., ∼ 20 % ) when either the charged lepton’s energy or the rest of the cascade is measured. We comment on final state radiation’s impacts on measuring the inelasticity distribution, ν / ν ¯ flux ratio, throughgoing muons, and double-bang signatures for high-energy neutrino observation. Furthermore, for ultrahigh-energy neutrino observation, we find that final state radiation increases the overall detectable energy by as much as 20%, affects flavor measurements, and decreases the energy of both Earth-emergent tau leptons and regenerated tau neutrinos. Many of these have significant impacts on measuring neutrino fluxes and spectra. Finally, for the LHC’s Forward Physics Facility, we find that final state radiation will impact future extractions of strange quark parton distribution functions. Final state radiation should be included in future analyses at neutrino telescopes and the Forward Physics Facility. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗