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At least 109 records · Page 6

Observation of quantum entanglement in top quark pair production in proton–proton collisions at $\sqrt{s}$ = 13 TeV

Entanglement is an intrinsic property of quantum mechanics and is predicted to be exhibited in the particles produced at the Large Hadron Collider. A measurement of the extent of entanglement in top quark-antiquark ($t\bar{t}$) events produced in proton–proton collisions at a center-of-mass energy of 13 TeV is performed with the data recorded by the CMS experiment at the CERN LHC in 2016, and corresponding to an integrated luminosity of 36.3 fb −1 . The events are selected based on the presence of two leptons with opposite charges and high transverse momentum. An entanglement-sensitive observable D is derived from the top quark spin-dependent parts of the $t\bar{t}$ production density matrix and measured in the region of the $t\bar{t}$ production threshold. Values of $D \lt -1/3$ are evidence of entanglement and D is observed (expected) to be $-0.480^{+0.026}_{-0.029}$ ($-0.467^{+0.026}_{-0.029}$) at the parton level. With an observed significance of 5.1 standard deviations with respect to the non-entangled hypothesis, this provides observation of quantum mechanical entanglement within $t\bar{t}$ pairs in this phase space. This measurement provides a new probe of quantum mechanics at the highest energies ever produced.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Observation of a cross-section enhancement near the production threshold in $\sqrt{s} = 13$ TeV pp collisions with the ATLAS detector

A measurement of $t\bar{t}$ production is presented in the invariant-mass region near the pair production threshold, $m_{t\bar{t}} \sim 345$ GeV, in final states with two charged leptons and multiple jets. The measurement is based on $140\,\mathrm{fb}^{-1}$ of proton–proton collision data collected at $\sqrt{s} = 13$ TeV with the ATLAS detector at the Large Hadron Collider. The data are compared to two models of $t\bar{t}$ production: a baseline model including only perturbative quantum chromodynamics (pQCD) predictions for the hard process at approximate next-to-next-to leading order accuracy in the strong coupling, and an extended model that, in addition, incorporates non-relativistic QCD simulations that also include the formation of colour-singlet quasi-bound-states near the $t\bar{t}$ threshold. The agreement between the data and the models is quantified via a profile-likelihood fit to the reconstructed $m_{t\bar{t}}$ distributions, in bins of two angular observables sensitive to spin-correlations in the $t\bar{t}$ system. An excess of events is observed over the baseline pQCD prediction, with an observed significance over 8 standard deviations. This excess is consistent with the formation of colour-singlet and spin-singlet S-wave quasi-bound $t\bar{t}$ states, as predicted by non-relativistic QCD, and corresponds to an observed cross-section of $9.3^{+1.4}_{-1.3}$ pb.

Large Hadron Collider

Optimizing infrasound observations for sample return capsule re-entry: Insights from OSIRIS-REx and Hayabusa2

The atmospheric entry of meteoroids presents a rare and unpredictable phenomenon, posing challenges for systematic observation and detailed characterization. Such events are nonetheless critical for advancing understanding of acoustic wave propagation, atmospheric structure, and entry dynamics. In contrast, sample return capsules (SRCs) from space missions follow well constrained re-entry trajectories, enabling planned observations of shock wave generation and propagation under controlled conditions. This study compares two SRC atmospheric entries, Hayabusa2 in 2020 and OSIRIS-REx in 2023, to assess how different infrasound array configurations influence shock wave detection and trajectory validation. Hayabusa2’s re-entry was monitored using a distributed network of 28 portable infrasound sensors across seven arrays in Woomera, Australia, permitting three-dimensional reconstruction of the trajectory and analysis of wave characteristics. For OSIRIS-REx, a compact four-sensor array deployed near Eureka Airport provided trajectory confirmation through arrival-time differences and back-azimuth estimates. Spectral and waveform analyses revealed differences in signal properties associated with variations in entry angle and velocity. The results illustrate both the strengths and the limitations of the deployed array configurations. The dense and distributed arrays during Hayabusa2’s re-entry enabled detailed trajectory reconstruction, whereas the compact array at Eureka primarily provided confirmation of signal coherence and back-azimuth consistency. These case studies highlight that even relatively small arrays, if located close to the predicted ground track, can still capture useful information on arrival direction and timing. Such insights provide practical guidance for planning future observational campaigns of SRC returns and other controlled atmospheric entries, and may also inform approaches to opportunistic observations of meteoroid events and other atmospheric acoustic phenomena.

celestial mechanics: orbit determination

Bayesian quantification of observability and equation of state of twin stars

The possibility of discovering twin stars, two neutron stars (NSs) with the same mass but different radii, is usually studied in forward modelings by using a restricted number of NS matter equations of state (EOSs) encapsulating a first-order phase transition from hadronic to quark matter (QM). Informing our likelihood function with the NS radius data from GW170817 and using a metamodel with nine parameters capable of mimicking most NS EOSs available in the literature, we conduct a Bayesian quantification of the observability and underlying EOSs of twin stars. Of the accepted EOSs, between 12 and 18% yield twin stars, depending on the restrictions we place on the second branch. The possibility of twin stars remains robust even under recent observational constraints. Here, we show that many of these twin star scenarios are observable with currently available levels of accuracy in measuring NS radii. We also present the marginalized posterior probability density functions (PDFs) of every EOS parameter for each of four mass-radius correlation topologies. We find that the inferred EOS depends sensitively on not only whether twin stars are present, but also the category of twin stars, indicating that the observation of twin stars would provide a strong constraint on the underlying EOS. In particular, for two coexisting hybrid stars having QM cores at different densities, the PDF for QM speed of sound squared 𝑐$^2_{qm}$ has two peaks, one below and another above the conformal limit 𝑐$^2_{qm}$ = 1/3 predicted by perturbative QCD.

QCD in nuclear reactions

Optimal observables for the chiral magnetic effect from machine learning

The detection of the chiral magnetic effect (CME) in relativistic heavy-ion collisions remains challenging due to substantial background contributions that obscure the expected signal. In this Letter, we present a novel machine learning approach for constructing optimized observables that significantly enhance CME detection capabilities. By parametrizing generic observables constructed from flow harmonics and optimizing them to maximize the signal-to-background ratio, we systematically develop CME-sensitive measures that outperform conventional methods. Using simulated data from the anomalous viscous fluid dynamics framework, our machine learning observables demonstrate up to 90% higher sensitivity to CME signals compared to traditional 𝛾 and 𝛿 correlators, while maintaining minimal background contamination. The constructed observables provide physical insight into optimal CME detection strategies and offer a promising path forward for experimental searches of the CME at the BNL Relativistic Heavy Ion Collider and the CERN Large Hadron Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four Z+jets Kinematic Observables with the ATLAS Detector

Z boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from standard model predictions. All previous measurements of Z boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins. In this analysis, a machine learning method called omnifold is used to produce a simultaneous measurement of twenty-four Z +jets observables using 139 fb -1 of proton-proton collisions at √s =13 TeV collected with the ATLAS detector. Unlike any previous fiducial differential cross-section measurement, this result is presented unbinned as a dataset of particle-level events, allowing for flexible reuse in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Complementarity-based complementarity: The choice of mutually unbiased observables shapes quantum uncertainty relations

Quantum uncertainty relations impose fundamental limits on the joint knowledge that can be acquired from complementary observables: Perfect knowledge of a quantum state in one basis implies maximal indetermination in all other mutually unbiased bases (MUBs). Uncertainty relations derived from joint properties of the MUBs are generally assumed to be uniform, irrespective of the specific observables chosen within a set. In this work, we demonstrate instead that the uncertainty relations can depend on the choice of observables. Through both experimental observation and numerical methods, we show that selecting different sets of three MUBs in a five-dimensional quantum system results in distinct uncertainty bounds, i.e., in varying degrees of complementarity, in terms of both entropy and variance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC

Predicting Adaptively Chosen Observables in Quantum Systems

Recent advances have demonstrated that 𝒪⁡(log 𝑀) measurements suffice to predict 𝑀 properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properties after looking at previous predictions. This work investigates the adaptive setting for three classes of observables: local, Pauli, and bounded-Frobenius-norm observables. We prove that Ω⁡(√𝑀) samples of an arbitrarily large unknown quantum state are necessary to predict expectation values of 𝑀 adaptively chosen local and Pauli observables, where the system size scales exponentially and polynomially in 𝑀, respectively. We also present computationally efficient algorithms that achieve this information-theoretic lower bound. In contrast, for bounded-Frobenius-norm observables, we devise an algorithm requiring only 𝒪⁡(log 𝑀) samples, independent of system size. These results highlight the potential pitfalls of adaptivity in analyzing data from quantum experiments and provide algorithmic tools to safeguard against erroneous predictions in quantum experiments.

Machine learning

Confronting Earth System Model trends with observations

Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations.

58 GEOSCIENCES

Measurement of the 1-jettiness event shape observable in deep-inelastic electron-proton scattering at HERA

The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable $τ^b_1$ in neutral-current deep-inelastic electron-proton scattering (DIS). The observable $τ^b_1$ is equivalent to a thrust observable defined in the Breit frame. The data sample was collected at the HERA ep collider in the years 2003–2007 with center-of-mass energy of $\sqrt{s}$ = 319 GeV, corresponding to an integrated luminosity of 351.1 pb -1 . Triple differential cross sections are provided as a function of $τ^b_1$, event virtuality $Q^2$, and inelasticity y, in the kinematic region $Q^2$ > 150 GeV 2 . Single differential cross section are provided as a function of $τ^b_1$ in a limited kinematic range. Double differential cross sections are measured, in contrast, integrated over $τ^b_1$ and represent the inclusive neutral-current DIS cross section measured as a function of $Q^2$ and y. The data are compared to a variety of predictions and include classical and modern Monte Carlo event generators, predictions in fixed-order perturbative QCD where calculations up to $\mathcal{O}$($α^3_s$) are available for $τ^b_1$ or inclusive DIS, and resummed predictions at next-to-leading logarithmic accuracy matched to fixed order predictions at $\mathcal{O}$($α^2_s$). These comparisons reveal sensitivity of the 1-jettiness observable to QCD parton shower and resummation effects, as well as the modeling of hadronization and fragmentation. Within their range of validity, the fixed-order predictions provide a good description of the data. Monte Carlo event generators are predictive over the full measured range and hence their underlying models and parameters can be constrained by comparing to the presented data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes

Seismic DAS Observations of a large underground chemical explosion in dry tuff

On 18 October 2023 a 16.3-ton TNT equivalent chemical explosion was detonated underground at the Nevada National Security Site, generating a seismic event (Meyers et al., 2024). The associated seismic wavefield was measured on a Distributed Acoustic Sensing (DAS) array with slant range distances from 27 m – 1123 m. The first arriving phase traveled at an apparent velocity of about 2640 m s -1 from 27 m to 420 m slant range and about 2470 m s -1 from 505 m to 1123 m slant range according to the first arrival moveouts on the DAS data. The first arrival from the explosion temporarily saturated the cable from a slant range of 27 m – 186 m and 0.009 s to 0.084 s post detonation. From 186 m slant range to 420 m slant range, peak strain rates of 5.6 x 10 6 nm m -1 s -1 were observed for the first arrival phase. For the first arrival from 505 m slant range to 1123 m slant range, peak strain rates reduced to 8.0 x 10 4 nm m -1 s -1 . A comparison of the scaled accelerations computed from DAS, the geophone pairs, and the measurements of co-located accelerometer pairs show common agreement at the scaled ranges of the single point sensors. This study adds to the body of work reporting near-source DAS observations of the seismic wavefields generated by underground chemical explosions. These results indicate that near-source DAS observations can refine interpretations of phase identification from single-point sensor observations. Phase identification could be one mechanism that contributes scatter to single point seismic measurements which would confound the performance of empirical relationships for small explosions. Removing that mechanism may therefore reduce interstation variability and increase empirical relationship performance for small explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF

Seismic DAS observations of a large underground chemical explosion in dry tuff

On 18 October 2023 a 16.3-ton TNT equivalent chemical explosion was detonated underground at the Nevada National Security Site, generating a seismic event with a magnitude of 1.7 (Meyers et al., 2024). The associated seismic wavefield was measured on a Distributed Acoustic Sensing (DAS) array with slant range distances from 27 m – 1123 m. The first arriving phase traveled at an apparent velocity of about 2640 m s -1 from 27 m to 420 m slant range and about 2470 m s -1 from 505 m to 1123 m slant range according to the first arrival moveouts on the DAS data. The first arrival from the explosion temporarily saturated the cable from a slant range of 27 m – 186 m and 0.009 s to 0.084 s post detonation. From 186 m slant range to 420 m slant range, peak strain rates of 5.6 x 10 6 nm m -1 s -1 were observed for the first arrival phase. For the first arrival from 505 m slant range to 1123 m slant range, peak strain rates reduced to 8.0 x 104 nm m -1 s -1 . A comparison of the scaled accelerations computed from DAS, the geophone pairs, and the measurements of co-located accelerometer pairs show common agreement at the scaled ranges of the single point sensors. This study adds to the body of work reporting near-source DAS observations of the seismic wavefields generated by underground chemical explosions. These results indicate that near-source DAS observations can refine interpretations of phase identification from single-point sensor observations. Phase identification could be one mechanism that contributes scatter to single point seismic measurements which would confound the performance of empirical relationships for small explosions. Removing that mechanism may therefore reduce interstation variability and increase empirical relationship performance for small explosions.

58 GEOSCIENCES

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES

Observable-projected ensembles

Measurements in many-body quantum systems can generate non-trivial phenomena, such as preparation of long-range entangled states, dynamical phase transitions, or measurement-altered criticality. Here, we introduce a new measurement scheme that produces an ensemble of mixed states in a subsystem, obtained by measuring a local Hermitian observable on part of its complement. We refer to this as the observable-projected ensemble . Unlike standard projected ensembles-where pure states are generated by projective measurements on the complement-our approach involves projective partial measurements of specific observables. This setup has two main advantages: theoretically, it is amenable to analytical computations, especially within conformal field theories. Experimentally, it requires only a linear number of measurements, rather than an exponential one, to probe the properties of the ensemble. As a first step in exploring the observable-projected ensemble, we investigate its entanglement properties in conformal field theory and perform a detailed analysis of the free compact boson.

Milekhin, Alexey [California Institute of Technolo

A Curated Dataset of Regional Meteor Events with Simultaneous Optical and Infrasound Observations (2006–2011)

We present a curated, openly accessible dataset of 71 regional meteor events simultaneously recorded by optical and infrasound instrumentation between 2006 and 2011. These events were captured during an observational campaign using the all-sky cameras of the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. Each entry provides optical trajectory measurements, infrasound waveforms, and atmospheric specification profiles. The integration of optical and acoustic data enables robust linkage between observed acoustic signals and specific points along meteor trajectories, offering new opportunities to examine shock wave generation, propagation, and energy deposition processes. This release fills a critical observational gap by providing the first validated, openly accessible archive of simultaneous optical–infrasound meteor observations that supports trajectory reconstruction, acoustic propagation modeling, and energy deposition analyses. By making these data openly available in a structured format, this work establishes a durable reference resource that advances reproducibility, fosters cross-disciplinary research, and underpins future developments in meteor physics, atmospheric acoustics, and planetary defense.

astrometry

Applying the Velocity Gradient Technique in NGC 1333: Comparison with Dust Polarization Observations

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physical scales. In this work, we seek to use a recently proposed approach called the velocity gradient technique (VGT) to study B-fields in star-forming regions and compare the results with dust polarization observations in different wavelengths. The VGT is based on the anisotropic properties of eddies in magnetized turbulence to derive B-field properties in the ISM. We investigate that this technique is synergistic with dust polarimetry when applied to a turbulent diffused medium for the purpose of measuring its magnetization. Specifically, we use the VGT on molecular line data toward the NGC 1333 star-forming region ( 12 CO, 13 CO, C 18 O, and N 2 H + ), and we compare the derived B-field properties with those inferred from 214 and 850 μm dust polarization observations of the region using Stratospheric Observatory for Infrared Astronomy/High-Resolution Airborne Wide-band Camera Plus and James Clerk Maxwell Telescope/POL-2, respectively. We estimate both the inclination angle and the 3D Alfvénic Mach number M A from the molecular line gradients. Crucially, testing this technique on gravitationally bound, dynamic, and turbulent regions, and comparing the results with those obtained from polarization observations at different wavelengths, such as the plane-of-sky field orientation, is an important test on the applicability of the VGT in various density regimes of the ISM. We in general do not find a close correlation between the velocity gradient inferred orientations and the dust inferred magnetic field orientations.

79 ASTRONOMY AND ASTROPHYSICS

A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations

The structure of the magnetic reconnection boundary, particularly the presence of slow-mode shocks in the near-Earth magnetotail was studied by using magnetospheric multiscale (MMS) observations and 2.5D hybrid simulations. A total of 51 crossings of MMS from 2017 to 2021 were analyzed. We found that the detection percentage of slow-mode shocks in the near-Earth magnetotail is 41%–55%. Previous studies have only reported one slow-mode shock event in the near-Earth magnetotail and a slow-mode shock detection percentage of 10% or lower in the mid-to-distant magnetotail. It was observed that if the high-energy beam region data is removed from the slow-mode shock downstream observations then the detection of slow-mode shocks reduces, implying that the kinetic effects play an important role in the detection of slow-mode shocks. For the crossings where the interface was not identified as a slow-mode shock, it was found that the turbulence in those crossings can change the mass flux values and disrupt the detection of slow-mode shock. However, the macroscopic slow-mode shock-like structure stably exists around the magnetic reconnection interface, as most of the conditions for slow-mode shocks were satisfied. This result suggests that slow-mode shocks are a general feature of magnetic reconnection geometry. We find that the lack of detection of slow-mode shocks in previous observations and simulations can be explained by taking into account the kinetic structure of slow-mode shocks and the presence of turbulence.

79 ASTRONOMY AND ASTROPHYSICS