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Assessing equation of state-independent relations for neutron stars with nonparametric models
Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this study we revisit commonly used relations and reassess them with a very flexible set of phenomenological nonparametric equation of state models that are based on Gaussian processes. Our models correspond to two sets: equations of state which mimic hadronic models, and equations of state with rapidly changing behavior that resemble phase transitions. Here we quantify the accuracy of relations under both sets and discuss their applicability with respect to expected upcoming statistical uncertainties of astrophysical observations. We further propose a goodness-of-fit metric which provides an estimate for the systematic error introduced by using the relation to model a certain equation-of-state set. Overall, the nonparametric distribution is more poorly fit with existing relations, with the I–Love–Q relations retaining the highest degree of universality. Fits degrade for relations involving the tidal deformability, such as the binary-Love and compactness-Love relations, and when introducing phase transition phenomenology. For most relations, systematic errors are comparable to current statistical uncertainties under the nonparametric equation of state distributions.
Many-Body Majorana Braiding without an Exponential Hilbert Space
Qubits built out of Majorana zero modes constitute the primary path toward topologically protected quantum computing. Simulating the braiding process of multiple Majorana zero modes corresponds to the quantum dynamics of a superconducting many-body system. It is crucial to study the Majorana dynamics both in the presence of all other quasiparticles and for reasonably large system sizes. We present a method to calculate arbitrary many-body wave functions as well as their expectation values, correlators, and overlaps from time evolved single-particle states of a superconductor, allowing for significantly larger system sizes. We calculate the fidelity, transition probabilities, and joint parities of Majorana pairs to track the quality of the braiding process. Here, we show how the braiding success depends on the speed of the braid. Moreover, we demonstrate the topological CNOT two-qubit gate as an example of two-qubit entanglement. Our Letter opens the path to test and analyze the many theoretical implementations of Majorana qubits. Moreover, this method can be used to study the dynamics of any noninteracting superconductor.
Impact of increased smoothing by spectral dispersion bandwidth on stimulated Brillouin scattering in laser driven Hohlraums
Experiments were conducted at the National Ignition Facility (NIF) to investigate the impact of increased smoothing by spectral dispersion (SSD) bandwidth on the production of stimulated Brillouin scattering (SBS) within an indirect-drive inertial confinement fusion (ICF) Hohlraum. This was done in a subscale gold Hohlraum driven by 192 laser beams depositing 1.1 MJ of energy. The laser bandwidth was increased from 45 to 118 GHz (before frequency tripling) on the 30° and 50° cones, where backscatter could be measured using the Full Aperture Backscatter Station (FABS). It was expected that this 2.6-fold increase in bandwidth would suppress SBS generated within the Hohlraum plasma and lower the backscattered SBS energy on the 50° cones by over a factor of four. Experimental results, however, show that this SSD change only reduced the 50° cone SBS during the main capsule drive by -18±31% and -4.5±7.8% over the entire pulse. This is small compared to expected shot-to-shot SBS reproducibility (∼30%), such that the result can be considered within normal performance fluctuations. New 3D parallel paraxial code (pF3D) simulations, accounting for beam refractive intensification reproduce this result, suggesting that closer to 300 GHz of bandwidth would have been required to mitigate SBS to the expected level. Delivering such a high bandwidth is not feasible at NIF when operating at high peak power and would potentially prevent NIF’s ability to use cross-beam energy transfer (CBET) for implosion symmetry tuning.
Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030
The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.
Enhancing detection of topological order by local error correction
The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states—locally error-corrected decoration (LED)—by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order.
Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in pp collisions at s=13TeV with the ATLAS detector
A model-agnostic search for Beyond the Standard Model physics is presented, targeting final states with at least four light leptons (electrons or muons). The search regions are separated by event topology and unsupervised machine learning is used to identify anomalous events in the full 140 fb-1$$^{-1}$$ of proton–proton collision data collected with the ATLAS detector during Run 2. No significant excess above the Standard Model background expectation is observed. Model-agnostic limits are presented in each topology, along with limits on several benchmark models including vector-like leptons, wino-like charginos and neutralinos, or smuons. Limits are set on the flavourful vector-like lepton model for the first time.
Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids
Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.
Future Physics Perspectives on the Equation of State from Heavy Ion Collisions to Neutron Stars
With the computational power and algorithmic improvements available today, the ongoing STAR/RHIC and HADES/GSI experiments, the future FAIR and NICA facilities becoming operational, and the new precise measurements from NICER and LIGO/VIRGO, the high-energy nuclear physics and astrophysics communities are in the unique position to set very stringent constraints on the equation of state of strongly interacting matter. Here, we review the state-of-the-art of different approaches used in the description of hot and ultradense baryonic matter in and out of equilibrium, and discuss the regions in the phase diagram where heavy-ion collisions and neutron star mergers can overlap. Future perspectives are discussed to help define a comprehensive, multi-disciplinary strategy to map out the phase diagram of strongly interacting matter from heavy ion collisions to neutron stars.
The Psyche Gamma-Ray and Neutron Spectrometer
A Gamma-Ray and Neutron Spectrometer (GRNS) instrument has been developed as part of the science payload for NASA’s Discovery Program Psyche mission to the M-class asteroid (16) Psyche. The GRNS instrument is designed to measure the elemental composition of Psyche with the goal to understand the origin of this mysterious, potentially metal-rich planetary body. The GRNS will measure the near-surface abundances for the elements Ni, Fe, Si, K, S, Al, and Ca, as well as the spatial distribution of Psyche’s metal-to-silicate fraction (or metal fraction). These measurements address three of the five Psyche mission science objectives: determine if Psyche is a core; determine whether small metal bodies incorporate light elements into the metal phase; and determine whether Psyche was formed under reducing conditions. The Gamma-Ray Spectrometer (GRS) uses a cryocooled, high-purity Ge (HPGe) sensor to detect cosmic-ray generated gamma rays in the 60 to 9000-keV energy range. The HPGe sensor is surrounded by a borated plastic anticoincidence shield that provides three functions: active background rejection from charged particle interactions in the HPGe sensor; fast neutron measurements; and direct measurements of the incident galactic cosmic ray flux. The Neutron Spectrometer (NS) uses three 3 He gas proportional sensors, each with different material wraps to measure thermal (<0.4 eV), low-energy epithermal (0.4 eV to 1 keV), and high-energy epithermal (up to 100 keV) neutrons. This paper provides an overview of the Psyche GRNS, including: its science and measurement objectives; the design of the instrument hardware, software, and operation; pre-launch performance measurements and its initial performance in space; and an overview of its data products and expected operation for different Psyche mission phases.
Evidence against the Efimov effect in 12 C from spectroscopy and astrophysics
Background: The Efimov effect is a universal phenomenon in physics whereby three-body systems are stabilized via the interaction of an unbound two-body subsystems. A hypothetical state in 12 C at 7.458-MeV excitation energy, comprising a loose structure of three α particles in mutual two-body resonance, has been suggested in the literature to correspond to an Efimov state in nuclear physics. The existence of such a state has not been demonstrated experimentally. Purpose: Using a combination of γ spectroscopy, charged-particle spectroscopy, and astrophysical rate calculations allowing for strict limits on the existence of such a state to been established here. Method: Using the combined data sets from two recent experiments, one with the TexAT (Texas Active Target) TPC (Time Projection Chamber) to measure α decay and the other with Gammasphere to measure γ decay of states in 12 C populated by 12 N and 12 B β decay, respectively, we achieve high sensitivity to states in close proximity to the α threshold in 12 C. Results: No evidence of a state at 7.458 MeV is seen in either data set. Using a likelihood method, the 95% confidence limit γ -decay branching ratio is determined as a function of the β-decay feeding strength relative to the Hoyle state. In parallel, calculations of the 3α reaction rate show the inclusion of the Efimov corresponds to a large increase in the reaction rate around 5 × 10 7 K. Conclusion: From decay spectroscopy—at the 95% confidence limit, the Efimov state cannot exist at 7.458 MeV with any γ-decay branching ratio unless the β strength is less than 0.7% of the Hoyle state. This limit is evaluated for a range of different excitation energies and the results are not favorable for existence of the hypothetical Efimov state in 12 C . Furthermore, the 3α reaction rate with the inclusion of a state between 7.43 and 7.53 MeV exceeds the rate required for stars to undergo the red giant phase.
Quantum simulations of excited states with active-space downfolded Hamiltonians
Many-body techniques based on the double unitary coupled cluster ansatz (DUCC) can be used to downfold electronic Hamiltonians into low-dimensional active spaces. It can be shown that the resulting dimensionality reduced Hamiltonians are amenable for quantum computing. Recent studies performed for several benchmark systems using quantum phase estimation (QPE) algorithms demonstrated that these formulations can recover a significant portion of ground-state dynamical correlation effects that stem from the electron excitations outside of the active space. These results have also been confirmed in studies of ground-state potential energy surfaces using quantum simulators. In this letter, we study the effectiveness of the DUCC formalism in describing excited states. We also emphasize the role of the QPE formalism and its stochastic nature in discovering/identifying excited states or excited-state processes in situations when the knowledge about the true configurational structure of a sought after excited state is limited or postulated (due to the specific physics driving excited-state processes of interest). In this context, we can view the QPE algorithm as an engine for verifying various hypotheses for excited-state processes and providing statistically meaningful results that correspond to the electronic state(s) with the largest overlap with a postulated configurational structure. We illustrate these ideas on examples of strongly cor- related molecular systems, characterized by small energy gaps and high density of quasi-degenerate states around the Fermi level.
Neutron Fluence and Kerma Responses to Bare, Steel-Reflected, and Lucite-Reflected Steady-State Operations of the ORNL Health Physics Research Reactor
The experiments evaluated in this report were conducted at the Health Physics Research Reactor (HPRR), also known as the Fast Burst Reactor. The reactor was designed and built at Oak Ridge National Laboratory (ORNL) in 1961. In this evaluation, a total of 12 experiments were evaluated (6 shield configurations and two responses). Six of them are considered acceptable as benchmark experiments and are thoroughly described: the measurands are bare, steel-shielded and Lucite-shielded neutron fluences and neutron KERMA responses to steady-state operations of the HPRR. It is important to mention that there is a general lack of information in the experimental methodology and neutron spectrum unfolding, and in some cases, unexpected and unexplained discrepancies between calculation and experiment. The HPRR’s unique characteristics were that it is a fast reactor, the use of a Lucite shield as the shielding material, and the use of the neutron fluence as the main benchmark measurand. There are no known, significant correlations between benchmarks in this evaluation and benchmarks in other evaluations.
Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level
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Physics-informed graphical neural network for power system state estimation
State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. Here, this paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods.
Dimorphos’s Material Properties and Estimates of Crater Size from the DART Impact
On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft intentionally collided with Dimorphos, the moon of the binary asteroid system 65803 Didymos. This collision provided the first full-scale test of a kinetic impactor for planetary defense. Images from DART’s DRACO camera revealed Dimorphos to be an oblate spheroid covered in boulders of varying sizes and shapes. Very little was known about Dimorphos prior to DART’s impact, including its shape, structure, and material properties. Approach observations and those following the DART impact have provided crucial knowledge that narrows the parameter space relevant to modeling the impact into Dimorphos. Here we present the results of a suite of hydrocode simulations of the DART impact on Dimorphos. Despite remaining uncertainties, initial models of DART’s kinetic impact provide important information about the results of DART (e.g., potential crater size and morphology, ejecta mass) and the properties of Dimorphos. Simulations here suggest that Dimorphos has near-surface strength ranging from a few Pascals to tens of kPa, which corresponds to crater sizes of ~40–60 m. Simulated crater sizes provide a crucial comparison metric for the European Space Agency Hera mission when it arrives at the Didymos system. Hera’s measurement of crater size in combination with measurement of Dimorphos’s mass will allow us to assess our simulations and provide the information needed to make the DART impact experiment both the first test of a planetary defense mitigation mission and the first full-scale planetary defense simulation validation exercise.
The Muon Collider
Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R\&D project that can make the muon collider a reality.
Validation of a Global Geospace Model With a Systems Science Approach Based on Canonical Correlation Analysis
A systems science approach based on canonical correlation analysis (CCA) is applied as a new, behavioral way to validate global geospace models. The biggest novelty of the technique is that it validates models at a system level, whereby a side‐by‐side comparison is performed of CCA applied to a 30‐day observational and the corresponding simulation data sets comprising quiet, moderate and active times. The simulation used the Multiscale Atmosphere‐Geospace Environment (MAGE) model. It is shown that (a) CCA must be combined with sensitivity analysis to be effective, (b) the MAGE model generally reproduces the observed behavior (more so for quieter time intervals), quantified by the intercorrelations between different variables and (c) the technique identifies the SuperMAG SML index as a quantity for which refinements of the model are needed.