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We report the results of the investigation of low-frequency electronic noise in ZrS3 van der Waals semiconductor nanoribbons. The test structures were of the back-gated field-effect-transistor type with a normally off n-channel and an on-to-off ratio of up to four orders of magnitude. The current–voltage transfer characteristics revealed significant hysteresis owing to the presence of deep levels. The noise in ZrS3 nanoribbons had spectral density SI ∼ 1/fγ (f is the frequency) with γ = 1.3–1.4 within the whole range of the drain and gate bias voltages. We used light illumination to establish that the noise is due to generation–recombination, owing to the presence of deep levels, and determined the energies of the defects that act as the carrier trapping centers in ZrS3 nanoribbons. 

Abstract Quantum Chromodynamics predicts a phase transition from hadronic matter to quark–gluon plasma (QGP) at high temperatures and energy densities, where quarks and gluons (partons) are no longer confined within hadrons. The QGP forms in ultrarelativistic heavy-ion collisions. Anisotropic flow coefficients, quantifying the azimuthal expansion of produced matter, probe QGP properties. Flow measurements in high-energy heavy-ion collisions show a distinctive grouping of anisotropic flow for baryons and mesons at intermediate transverse momentum – a feature associated with flow imparted at the quark level, confirming QGP existence. The observation of QGP-like features in proton–proton and proton–ion collisions has sparked debate about QGP formation in smaller systems. For the first time, we demonstrate the distinctive grouping of anisotropic flow for baryons and mesons in high-multiplicity proton–lead and proton–proton collisions at the Large Hadron Collider (LHC). These results are described by a model including hydrodynamic flow followed by hadron formation via quark coalescence, consistent with the formation of partonic flowing systems in these collisions. 

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses. 

A<sc>bstract</sc> The yields of prompt and non-prompt J/ψand the fraction of non-prompt J/ψare measured at midrapidity (|y| < 0.9) via the dielectron decay channel as a function of the midrapidity charged-particle multiplicity (|η| < 0.9) in pp collisions at$$\sqrt{s}=13$$TeV. The J/ψyields and the multiplicity are normalized by their average value in inelastic collisions. The multiplicity-dependent yield ratio between prompt J/ψand D⁰is reported. The multiplicity is further divided into three azimuthal regions with respect to the J/ψmomentum: toward the J/ψemission direction, transverse, or opposite to it. A stronger-than-linear increase of the self-normalized yields is observed for both prompt and non-prompt J/ψproduction, with similar trends. This behaviour is also observed in the toward region, while a weaker increase is observed in the transverse and away regions. 

Abstract Two-particle angular correlations probe particle production mechanisms and the underlying event-wide phenomena present in hadronic collisions. The correlations are examined as a function of rapidity and azimuthal-angle differences ($$\Delta y, \Delta \varphi $$ $\Delta y,\Delta \phi$) for pairs of like- and unlike-sign pions, kaons, and (anti-)protons produced in pp collisions at$$\sqrt{s}$$ $\sqrt{s}$= 13 TeV, measured by the ALICE experiment. Two-particle correlation functions are provided together with$$\Delta y$$ $\Delta y$and$$\Delta \varphi $$ $\Delta \phi$projections and compared to Monte Carlo (MC) model predictions. For the first time, the measurement is performed as a function of the event’s charged-particle density. Previous studies conducted for pp collisions at$$\sqrt{s}$$ $\sqrt{s}$= 7 TeV at ALICE revealed a near-side anticorrelation for baryon–baryon and antibaryon–antibaryon pairs, whose origin remains unresolved. Here, an additional approach is introduced to study the multiplicity dependence and the expected inverse multiplicity scaling of the correlation function. This method highlights qualitative differences in the underlying sources of correlations, such as quantum-statistics effects, final-state interactions, and resonance decays. The puzzling near-side anticorrelation in baryon baryon measurements is observed across all multiplicity classes and continues to challenge current particle-production models. Furthermore, the multiplicity dependence of the correlations between mesons provides an independent probe of the sensitivity of current MC models to soft-QCD effects and hadronization dynamics. The presented measurements, together with the baryon results, enrich the experimental picture of two-particle correlations in pp collisions and serve as valuable input for ongoing theoretical developments. 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification.