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779 records · Page 44

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DOE OSTI2025

Constraining the Synthesis of the Lightest 𝑝 Nucleus 74 Se

We provide the first experimental cross section of the 73 As(p, γ) 74 Se reaction to constrain one of the main destruction mechanisms of the p nucleus 74 Se in explosive stellar environments. The measurement was done using a radioactive 73 As beam at incident energies of 3.1 and 3.7 MeV/nucleon. Along with the total cross section measurement, statistical properties of the 74 Se compound nucleus were extracted, constraining the reaction cross section across the entire Gamow window of the γ process. The impact of the experimentally constrained reaction rate on 74 Se production in Type II supernovae is investigated through Monte Carlo one-zone network simulations. The results indicate that the overproduction of 74 Se by Type II supernova models cannot be resolved by nuclear physics and point towards the description of the astrophysical conditions of the γ process.

59 ≤ A ≤ 89↗

DOE OSTI2025

Identification of tau leptons using a convolutional neural network with domain adaptation

A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ h ) from quark or gluon jets and electrons and muons that are misreconstructed as τ h candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ h candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb -1 , respectively. Techniques to calibrate the performance of the τ h identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.

Large detector-systems performance↗

DOE OSTI2026

Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations

The IceCube Upgrade is an extension of the existing IceCube Neutrino Observatory and will be deployed in the 2025–2026 austral summer. It will significantly improve the sensitivity of the detector to atmospheric neutrino oscillations. The existing 86-string IceCube array contains a dense in-fill known as DeepCore which is optimized to measure neutrinos with energies down to a few GeV. The IceCube Upgrade will consist of seven new densely instrumented strings placed within the DeepCore volume to further enhance the performance in the GeV energy range. The additional strings will feature new optical modules, each containing multiple photomultiplier tubes (PMTs), in contrast to the existing modules that each contain a single PMT. This will more than triple the number of PMT channels with respect to the current IceCube configuration, allowing for improved detection efficiency and reconstruction performance at GeV energies. We describe necessary updates to simulation, event selection, and reconstruction to accommodate the higher data rates observed by the upgraded detector and the addition of multi-PMT modules. We determine the expected sensitivity of the IceCube Upgrade to the atmospheric neutrino oscillation parameters sin 2 ⁡𝜃 23 and Δ⁢𝑚$^{2}_{32}$, the appearance of tau neutrinos and the neutrino mass ordering. The IceCube Upgrade will provide neutrino oscillation measurements that are of similar precision to those from accelerator experiments, while providing complementarity by probing higher energies and longer baselines, and with different sources of systematic uncertainties.

artificial neural networks↗

DOE OSTI2025

Measurement of neutron production in atmospheric neutrino interactions at Super-Kamiokande

We present measurements of total neutron production from atmospheric neutrino interactions in water, analyzed as a function of electron-equivalent visible energy over a range of 30 MeV to 10 GeV. These results are based on 4,270 days of data collected by Super-Kamiokande, including 564 days with 0.011 wt% gadolinium added to enhance neutron detection. Neutron signal selection is based on a neural network trained on simulation, with its performance validated using an Am/Be neutron point source. The measurements are compared to predictions from neutrino event generators combined with various hadron-nucleus interaction models, which include an intranuclear cascade model and a nuclear deexcitation model. We observe significant variations in the predictions depending on the choice of hadron-nucleus interaction model. We discuss key factors that contribute to describing our data, such as in-medium effects in the intranuclear cascade and the accuracy of statistical evaporation modeling.

Cherenkov detectors↗

DOE OSTI2025

Search for triple Higgs boson production in the 6⁢𝑏 final state using 𝑝⁢𝑝 collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for the production of three Higgs bosons (𝐻⁡𝐻⁡𝐻) in the $b\bar{b}b\bar{b}b\bar{b}$ final state is presented. The search uses 126 fb −1 of proton-proton collision data at $\sqrt{s}$ =13 TeV collected with the ATLAS detector at the Large Hadron Collider. The analysis targets both nonresonant and resonant production of 𝐻⁡𝐻⁡𝐻. The resonant interpretations primarily consider a cascade decay topology of 𝑋 →𝑆⁢𝐻 → 𝐻⁡𝐻⁡𝐻 with masses of the new scalars 𝑋 and 𝑆 up to 1.5 and 1 TeV, respectively. In addition to scenarios where 𝑆 is off-shell, the nonresonant interpretation includes a search for Standard Model 𝐻⁡𝐻⁡𝐻 production, with limits on the trilinear and quartic Higgs self-coupling set. No evidence for 𝐻⁡𝐻⁡𝐻 production is observed. An upper limit of 59 fb is set, at the 95% confidence level, on the cross section for Standard Model 𝐻⁡𝐻⁡𝐻 production.

Artificial neural networks↗