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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Aerosol Thermodynamics (Aerosol Liquid Water Concentration and Aerosol pH) at S2 during CoURAGE

These data report aerosol liquid water content (ALWC) and aerosol pH for the S2 (Mt. Airy) site during CoURAGE for April - June 2025. The ISORROPIA-II aerosol thermodynamic equilibrium model was used to compute ALWC and pH. The model was run in forward mode, with solids formation disabled (metastable mode), according to Pye et al. (Atmospheric Chemistry and Physics, 2020). Information about the model can be found at https://www.epfl.ch/labs/lapi/models-and-software/isorropia/, and in Fountoukis and Nenes (Atmospheric Chemistry and Physics, 2007). Inputs to the model were: (1) measured Temperature and relative humidity, both from the DoE ARM “metwxt” data products at S2, (2) aerosol chemical composition from the ARM ACSM, and (3) gas-phase ammonia concentrations. For ISORROPIA-II, ambient RH values above 0.995 were input to the model as 0.995; therefore, caution should be exercised interpreting ALWC when RH was above 0.995. Aerosol composition or NH3(g) concentrations below the LOD were input into the model as 0.5*LOD. Model runs were only conducted for data points at 30-min resolution with all three inputs (meteorology, ACSM, and NH3). If any of the three model inputs were missing, aerosol thermodynamic outputs are flagged as -999. Similarly, aerosol thermodynamic outputs are flagged as -999 if measured NH3(g) and aerosol ammonium (NH4+) were simultaneously below the respective LODs. The ALWC includes liquid water from inorganic aerosol components, calculated directly by ISORROPIA-II, as well as liquid water from organics, estimated from the ACSM organics assuming a kappa value of 0.12 according to Guo et al. (Atmospheric Chemistry and Physics, 2015).

Aerosol Liquid Water Content↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An analysis of controlled detachment by seeding various impurity species in high performance scenarios on DIII-D and EAST

Experiments at DIII-D and EAST have demonstrated simultaneous high confinement, divertor detachment, and active control of detachment level, all of which are required for ITER. Comparing detachment control via T e and J sat , it appears that is the most straightforward sensor to use for accessing detachment onset, while J sat offers more precise control of degree of detachment. Based on these results, control using nitrogen seeding has so far shown the best ability to follow a target value with the low disruptivity and little to no degradation of performance when an Internal Transport Barrier (ITB) is present, but not all facilities allow its use. Neon seeding also can be paired with feedback control with low impact on core performance as long as there is no disruption, however shots with neon seeding commonly disrupted during these experiments. Argon is effective in EAST, but tends to degrade performance (by ≈ 10%β p ) when detachment is achieved. With ideal conditions and strike point position control, data from a single Langmuir probe are an acceptable input to the control algorithm, but this simple system is easily defeated by strike point displacement comparable to the T e or J sat scale lengths. The presence of an ITB seems to be critical to retaining core performance in detachment in these parameter ranges, as the pedestal pressure tends to decrease as a result of impurity seeding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning

Abstract X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Determinants of residential mobility: an adaptive retrospective survey method

This study introduces a survey instrument to collect retrospective life-course events, focusing on residential relocation, and it utilizes the survey to evaluate determinants of residential mobility. Here, the survey consists of seven modules collecting information about household structure and household demographics, latest residential relocation, current and previous home, employment, education, vehicle ownership, and travel behavior. The time window of the life-course calendar in the survey is customized according to the latest residential relocation as an anchor point to assist with memory recollection and balance the required input from participants. The survey is used to collect data from a sample of 514 respondents in Sydney, Australia, and another sample of 404 respondents in Chicago, Illinois, in the US. The Cox proportional hazard model is used to analyze residential mobility. The results show primary school commencement is a salient determinant of residential relocation, but its impact is significantly higher in Chicago compared to Sydney.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Heinrich, Lukas (ORCID:0000000240487584)↗
DOE OSTI2022

When One Cannot Bypass the Byproducts: Plastic Packaging Waste Embedded in Production and Export

While packaging is praised for reducing waste of perishable food products, packaging waste is a growing concern. Its least recyclable type, plastics waste, is increasingly polluting the marine environment and many developing countries are now refusing to take the waste from developed countries. Before responsibility can be established, one has to identify the key producers and consumers. This paper adjusts the packaging waste satellite account in EXIOBASE 3 by combining the official waste data published by the World Bank. Based on input-output methods, we find that the world's top suppliers of plastic packaging waste are the United States, China, and Brazil. Food and beverage products constitute the bulk of the problem. Yet, 21% of these countries' plastic packaging waste is induced export. At the global level, it is 25.8%. About 76.0% of export-induced plastic packaging waste corresponds to packaging of the exported goods that will ultimately be disposed of in the destination country; while the others correspond to the packaging used in their supply-chain and wasted domestically. We conclude that international efforts need to assign shared responsibility and promote improvement in plastic packaging recyclability.

environment responsibility↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2025

SOHIP Abel Transform and Onion Peeling Model Module

This software provides tools for analyzing and modeling physical systems using mathematical transforms and layered models. It includes (1) functions for performing the Abel transform, which is used to relate measurements of bending angles to properties such as refractive index and radius in a medium. The code can compute bending angles from input profiles and also reconstruct these profiles from observed data; (2) the functions for modeling systems with multiple layers using an onion-peeling approach, allowing users to simulate and analyze the behavior of layered materials or structures. These capabilities are useful for researchers and engineers working in fields such as optics, atmospheric science, and materials analysis, enabling them to interpret and model data from experiments or simulations relates to refraction in spherical symmetric medium.

Xu, Shuang [Lawrence Livermore National Laboratory↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

FY21 Status Report on the Molten Salt Thermal Properties Database (MSTDB) Development

This report describes the thermodynamic assessments for the ZrF 4 -BeF 2 , AlCl 3 -NaCl, AlCl3-KCl and the PuCl 3 -CsCl pseudo-binary subsystems. Calculation results are compared to the inputs used to optimize the adjustable model parameters. The data came from experimental studies reported in the open literature, from the MSR research community, and/or computational results generated within the NEAMS program and from collaborators. The models from the thermodynamic assessment of these salt systems are integrated into the MSTDB-TC (Molten Salt Thermal Properties Database-Thermochemical). A brief description of the companion MSTDB-TP (Molten Salt Thermal Properties Database-Thermophysical) is given along with an overview of the approach for predicting both thermodynamic and thermophysical property behavior of multicomponent systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Process with Decoupled Absorber Kinetics and Solvent Regeneration through Membrane Dewatering and In-Column Heat Transfer (Final Report)

This report summarizes the work conducted on project DE-FE0031604 where University of Kentucky Center for Applied Energy (UK CAER) has validated its intensified CO 2 capture process through substantial enhancements to the kinetics of the absorption process and energy reductions by absorber temperature profile modification, dewatering and heat integration technologies for achieving significant capital and operating cost reductions. To address DOE’s objective of improving post-combustion CO 2 capture technology and reducing associated cost, UK CAER employed an intensified process which combined three key aspects targeted at overcoming inherent limitations or barriers in the conventional CO 2 capture and desorption process. The process designed to be independent of the type of solvent used, included (1) the use of 3-D printed two-channel structured packing material to control the temperature profile and increase the CO 2 absorption rate in the absorber, (2) a zeolite membrane dewatering unit for dewatering of the carbon-rich solvent to decouple solvent concentration needs for CO 2 absorption and desorption, and (3) a rich-split feed with two-phase flow heat transfer prior to the stripper that provided a secondary point of vapor generation to provide energy savings in steam extraction and solvent regeneration. The project was executed over two budget periods. This involved testing of individual process components which included the advanced heat transfer packing and the dewatering membrane on UK CAER’s 30 liter per minute (L/min) CO 2 (3” Column) capture bench unit with simulated flue gas in the first budget period. Subsequent scaled-up testing of these components together with the split-feed configuration were also tested in UK CAER’s 0.1 MWth CO 2 capture unit with coal-derived flue gas in the second-budget period. Long term studies were done during this period to assess process and solvent performance over extended duration. Project partners Lawrence Livermore National Laboratory (LLNL) and Media & Process Technology (MPT) led the development efforts for the advanced packing material and dewatering membrane respectively. Data from the long term testing was used as input for an environmental, health and safety (EH&S) assessment for the process and scaled technology performed by ALL4 LLC. Trimeric Corporation also completed a techno-economic analysis (TEA) for the UK CAER technology which was compared to the DOE reference Case B12B. Tests on the 3” column capture unit showed that the advanced heat transfer packing could be used to lower the bulge temperature in the absorber, and this was also proven in the scaled testing in the 0.1 MWth CO 2 capture unit. The bulge temperature could be lowered by >10 °C, changing the temperature profile in the absorber, and showed potential to enhance absorption with the ability to tailor the profile to provide conditions suitable for a solvent’s properties and kinetics. Conditions for short term evaluation of a 19” zeolite dewatering membrane on the 3” column capture unit yielded desirable fluxes and sustained rejection rates of >80%. However, for the scaled testing of six membrane modules consisting of 21 parts of 31-inch-long membrane tubes in each bundle (surface area 0.3 m 2 ), over a more extended duration, similar rejection rates could not be achieved. With the split-feed of the rich stream to the stripper, improved heat recovery minimized waste heat exiting the top of the stripper. The stripper exhaust temperatures could be reduced by >10 °C; reducing the amount of water vaporization contributed to lowering the regeneration energy by ~ 15%. The energy benefit could be sustained from the long term monitoring of the solvent performance. The solvent properties were not significantly impacted over the long-term operations. The benefits of the UK CAER process demonstrated experimentally were mostly validated from the TEA comparing a commercial scale application of the technology to DOE reference Case B12B. The cost of CO 2 capture for the UK CAER technology was estimated to be ~$34.97/tonne of CO 2 captured; a reduction of 23% compared to Case B12B. The increase in cost of electricity was also shown to be 16% lower than that of Case B12B. The total parasitic demand was also shown to be 11% lower. The key drivers for the benefits are a result of the process intensification approaches employed in the UK CAER technology for enhanced solvent performance, effective heat recovery and improved energy performance. The EH&S assessment did not find any major environmental concerns or barriers to the full scale implementation of the technology.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Evaluation of Hypotheses for the Fate of Historical Discharges from Hanford Site BC Cribs and Trenches

A preliminary review of declassified reports and historical documents from the 1950s and 1960s indicated they may contain valuable information regarding potential impacts to groundwater from liquid waste discharged to the 216-BC Cribs and Trenches during the late 1950s. Transport of contaminants in the vadose zone beneath the BC Cribs area is simulated by a vadose zone model developed for the Composite Analysis (DOE/RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau ). Results from this model indicate there has been no substantial breakthrough of contaminants (particularly technetium-99) to groundwater from the vadose zone up to the present day. However, there is uncertainty in the anisotropy and other model input parameters as well as acknowledged sparseness of sampling data for this area. In this study, historical documents and other information were reviewed to evaluate the possibility that contaminant breakthrough to groundwater occurred during or shortly after operation of the BC Cribs sites, and whether refinements of the vadose zone model parametrization should be considered.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2020

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Core States of Neutron Stars from Anatomizing Their Scaled Structure Equations

Given an Equation of State (EOS) for neutron star (NS) matter, there is a unique mass–radius sequence characterized by a maximum mass ${M}_{\mathrm{NS}}^{\max}$ at radius $R$ max . We first show analytically that the ${M}_{\mathrm{NS}}^{\max}$ and $R$ max scale linearly with two different combinations of the NS central pressure $P$ $c$ and energy density $ε$ $c$ , by dissecting perturbatively the dimensionless Tolman–Oppenheimer–Volkoff (TOV) equations governing NS internal variables. The scaling relations are then verified via 87 widely used and rather diverse phenomenological as well as 17 microscopic NS EOSs with/without considering hadron–quark phase transitions and hyperons, by solving numerically the original TOV equations. The EOS of the densest NS matter allowed before it collapses into a black hole is then obtained. Using the universal ${M}_{\mathrm{NS}}^{\max}$ and $R$ max scalings and Neutron Star Interior Composition Explorer and XMM-Newton mass–radius observational data for PSR J0740+6620, a very narrow constraining band on the NS central EOS is extracted directly from the data for the first time, without using any specific input EOS model.

79 ASTRONOMY AND ASTROPHYSICS↗