Search NASA⌕ Search

SEARCH · Search NASA

Results for “Clustering algorithms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Multistage Stochastic Transmission Expansion Algorithm for Wide-Area Planning under Uncertainty

The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Model Based Validation of Intelligent Powertrain Strategies for Connected and Automated Vehicles

Systems incorporating Vehicle to Everything (V2X) and conventional cellular based communication in vehicles can significantly help improve energy consumption via a combination of intelligent powertrain control strategies, smarter routing algorithms and driving in such a way as to minimize fuel economy and the emission of carbon dioxide, known as "eco-driving." In projects led by the Southwest Research Institute (SwRI), large-scale traffic simulations are created to model real-world scenarios with dynamic behavior that is reactive to imposed changes. Coupled with high fidelity powertrain models, the closed loop framework enables research and development of such Connected and Automated Vehicle (CAV) enabled technologies at scale. This paper will discuss a traffic system simulation environment that was built based on the High Street urban corridor in Columbus, Ohio. Eco-driving strategies were tested at scale on a variety of powertrain platforms – internal combustion engines, hybrid electric and fully electric vehicles. Furthermore, the paper will focus on hybrid electric powertrain modeling along with details on how the powertrain model was leveraged to develop a sophisticated clustering scheme to help down-select speed traces from large scale simulation studies for validation on vehicle dynamometer. Nominal energy consumption improvement around 12% was observed with good match between simulation studies and vehicle testing.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

funcX: Federated Function as a Service for Science

Here, funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. funcX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. funcX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints-enabling users to route functions to endpoints based on specific needs. funcX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. funcX implements various container management strategies to execute functions with high performance and efficiency on diverse funcX endpoints. funcX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for funcX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than 130000 concurrent workers. We show that funcX's container warming-aware routing algorithm can reduce the completion time for 3,000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
DOE OSTI2024

$\mathrm{COBRA}$ improves the completeness and contiguity of viral genomes assembled from metagenomes

Viruses are often studied using metagenome-assembled sequences, but genome incompleteness hampers comprehensive and accurate analyses. Contig Overlap Based Re-Assembly (COBRA) resolves assembly breakpoints based on the de Bruijn graph and joins contigs. Here we benchmarked COBRA using ocean and soil viral datasets. COBRA accurately joined the assembled sequences and achieved notably higher genome accuracy than binning tools. From 231 published freshwater metagenomes, we obtained 7,334 bacteriophage clusters, ~83% of which represent new phage species. Notably, ~70% of these were circular, compared with 34% before COBRA analyses. We expanded sampling of huge phages (≥200 kbp), the largest of which was curated to completion (717 kbp). Improved phage genomes from Rotsee Lake provided context for metatranscriptomic data and indicated the in situ activity of huge phages, whiB-encoding phages and cysC- and cysH-encoding phages. COBRA improves viral genome assembly contiguity and completeness, thus the accuracy and reliability of analyses of gene content, diversity and evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Continuous-variable quantum computation of the O(3) model in 1+1 dimensions

We formulate the $O(3)$ non-linear sigma model in $1+1$ dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. Here we construct the ground state and excited states using the coupled cluster ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates, estimate the discretization error, and present numerical results obtained from a photonic quantum simulator. We expect that the methods developed in this work will be useful for exploring interesting dynamics for a wide class of sigma models and gauge theories, as well as for simulating scattering events on quantum hardware in the coming decade.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

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

Performance Results for Sensor Assignment Problem as Solved on a Multi-Node Cluster

An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Evidence of a Dwarf Galaxy Stream Populating the Inner Milky Way Halo

Stellar streams produced from dwarf galaxies provide direct evidence of the hierarchical formation of the Milky Way. Here, we present the first comprehensive study of the LMS-1 stellar stream, that we detect by searching for wide streams in the Gaia EDR3 data set using the STREAMFINDER algorithm. This stream was recently discovered by Yuan et al. We detect LMS-1 as a 60° long stream to the north of the galactic bulge, at a distance of ~20 kpc from the Sun, together with additional components that suggest that the overall stream is completely wrapped around the inner Galaxy. Using spectroscopic measurements from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Sloan Digital Sky Survey, and the Apache Point Observatory Galactic Evolution Experiment, we infer that the stream is very metal-poor (<[Fe/H]> = –2.1) with a significant metallicity dispersion (σ [Fe/H] = 0.4), and it possesses a large radial velocity dispersion (σ v = 20 ± 4 km s–1 ). These estimates together imply that LMS-1 is a dwarf galaxy stream. The orbit of LMS-1 is close to polar, with an inclination of 75° to the galactic plane. Both the orbit and metallicity of LMS-1 are remarkably similar to the globular clusters NGC 5053, NGC 5024, and the stellar stream Indus. As a result, these findings make LMS-1 an important contributor to the stellar population of the inner Milky Way halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
DOE OSTI2020

Development of the Intelligent, Preventive Infrared (IR) Inspection System Housed in Hybrid Robotic Platforms

Robots and robotic systems that are designed for inspection, environmental study, and health and safety aid are becoming an increasing necessity. However, there is a number of challenges that accompany robots that are designed for these specific applications. These challenges include: navigating compact, enclosed spaces, travelling over multiple terrains and large obstacles, using the proper sensing and detection methods to assess an environment, and the use of lightweight and durable materials. The robotic platforms currently in development look at all of these challenges and attempt to overcome them. These designs specific use of hybrid robotic platforms, or platform that utilizes soft and rigid materials, allows for a more flexible platform and makes environments more navigable. To further improve the navigation of the platforms and environmental assessment, a novel infrared detection system is housed in the platforms to create a robot that can be used for the applications listed above and more. Objectives: Further develop two types of robotic platforms that utilize additive manufacturing, soft materials, and rigid materials. Continue the development of an intelligent and inhibitory infrared detection system based on an artificial intelligence (AI) algorithm. Continued study and fabrication of active soft materials designed for both sensing and actuation in hybrid robotic systems. Improve additive manufacturing fabrication to design rigid and semi-rigid components for hybrid robotic platforms. Transformable Wheel Robotic Platform: The chassis, wheels, tires and inspection system housing use different additive manufacturing techniques for fabrication. Continued work with additive manufacturing has lead to studies in metal-based printing and modular design and manufacturing. The new platform design with integrated electrical component printed. This will allow integration of the sensor housing onto the platform. Electrical components are being tested for battery life and performance. To improve this performance, such as integration of Lithium Polymer (LiPo) batteries. Snake Robotic Platform: The main focus of the development has centered around liquid-based soft actuators. That act on the principles of electrostatic and hydraulic actuation. A liquid dielectric sits between two compliant electrodes, contained by a flexible polymer shell. The electrodes and film gradually collapse toward each other from one corner of the electrode to the other. When the electrodes and film close together, a majority of the fluid is pushed into the area not covered by an electrode. A thin layer of the liquid dielectric remains between the electrode. The actuators will be stacked to cause large displacement, and move the linkages. The chassis of this platform uses purely additively manufactured linkages. Intelligent, Preventive IR Inspection System: Development of the AI for the system has lead to using a Scikit-Learn which assists in creating predictive models based on Regression, clustering, classification etc. To improve the infrared thermometry for low emissivity sources, work on the fabrication of a tandem photoconductive infrared thermometer was a main focus. Distance-Voltage-Temperature response data has been collected in the range of 7 cm - 100 cm and 200-400 deg. C. Modifications were made to the existing test bench to have a better control over the data. Automated data collection was realized using a Python code and an Arduino controlled stepper motor to increase the sample rate. A protective enclosure has been built around the setup to minimize the effect of the environment on the measurements. To better predict temperature, different AI models are being optimized. The regression model, LARS showed a high accuracy but had convergence issues and only works for the current test set-up. Results: The Transformable Wheel Robot has developed into a more flexible and modular platform. With the improvements to the current work, effort on the tire or soft gripper design been a large focus. The soft grippers will be interchange able to allow for increased performance in identified terrain types. The development of the liquid-based actuators allows the snake robotic platform to achieve the goals of being flexible and able to navigate confined spaces. However, there is room for improvement. Optimization work is currently being done in COMSOL Multiphysics. With the current IR system set-up the LARS model perfectly predicts the data; however, considering the mobility aspect of the project other models will allow for an optimized system. Testing of other model types is currently being done.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Predictions for the Detectability of Milky Way Satellite Galaxies and Outer-Halo Star Clusters with the Vera C. Rubin Observatory

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$ = 0 mag and $r_{1/2}$ = 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~7%-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Hijacking a rapid and scalable metagenomic method reveals subgenome dynamics and evolution in polyploid plants

Premise: The genomes of polyploid plants archive the evolutionary events leading to their present forms. However, plant polyploid genomes present numerous hurdles to the genome comparison algorithms for classification of polyploid types and exploring genome dynamics. Methods: Here, the problem of intra- and inter-genome comparison for examining polyploid genomes is reframed as a metagenomic problem, enabling the use of the rapid and scalable MinHashing approach. To determine how types of polyploidy are described by this metagenomic approach, plant genomes were examined from across the polyploid spectrum for both k-mer composition and frequency with a range of k-mer sizes. In this approach, no subgenome-specific k-mers are identified; rather, whole-chromosome k-mer subspaces were utilized. Results: Given chromosome-scale genome assemblies with sufficient subgenome-specific repetitive element content, literature-verified subgenomic and genomic evolutionary relationships were revealed, including distinguishing auto- from allopolyploidy and putative progenitor genome assignment. The sequences responsible were the rapidly evolving landscape of transposable elements. An investigation into the MinHashing parameters revealed that the downsampled k-mer space (genomic signatures) produced excellent approximations of sequence similarity. Furthermore, the clustering approach used for comparison of the genomic signatures is scrutinized to ensure applicability of the metagenomics-based method. Discussion: The easily implementable and highly computationally efficient MinHashing-based sequence comparison strategy enables comparative subgenomics and genomics for large and complex polyploid plant genomes. Such comparisons provide evidence for polyploidy-type subgenomic assignments. In cases where subgenome-specific repeat signal may not be adequate given a chromosomes' global k-mer profile, alternative methods that are more specific but more computationally complex outperform this approach.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantum Simulations of Fermionic Hamiltonians with Efficient Encoding and Ansatz Schemes

Here, we propose a computational protocol for quantum simulations of fermionic Hamiltonians on a quantum computer, enabling calculations on spin defect systems which were previously not feasible using conventional encodings and a unitary coupled-cluster ansatz of variational quantum eigensolvers. We combine a qubit-efficient encoding scheme mapping Slater determinants onto qubits with a modified qubitcoupled cluster ansatz and noise-mitigation techniques. Our strategy leads to a substantial improvement in the scaling of circuit gate counts and in the number of required qubits, and to a decrease in the number of required variational parameters, thus increasing the resilience to noise. We present results for spin defects of interest for quantum technologies, going beyond minimum models for the negatively charged nitrogen vacancy center in diamonds and the double vacancy in 4H silicon carbide (4H-SiC) and tackling a defect as complex as negatively charged silicon vacancy in 4H-SiC for the first time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fiducial-cosmology-dependent systematics for the DESI 2024 BAO analysis

When measuring the Baryon Acoustic Oscillations (BAO) scale from galaxy surveys, one typically assumes a fiducial cosmology when converting redshift measurements into comoving distances and also when defining input parameters for the reconstruction algorithm. A parameterised template for the model to be fitted is also created based on a (possibly different) fiducial cosmology. This model reliance can be considered a form of data compression, and the data is then analysed allowing that the true answer is different from the fiducial cosmology assumed. In this study, we evaluate the impact of the fiducial cosmology assumed in the BAO analysis of the Dark Energy Spectroscopic Instrument (DESI) survey Data Release 1 (DR1) on the final measurements in DESI 2024 III. We utilise a suite of mock galaxy catalogues with survey realism that mirrors the DESI DR1 tracers: the bright galaxy sample (BGS), the luminous red galaxies (LRG), the emission line galaxies (ELG) and the quasars (QSO), spanning a redshift range from 0.1 to 2.1. We compare the four secondary AbacusSummit cosmologies against DESI's fiducial cosmology (Planck 2018). The secondary cosmologies explored include a lower cold dark matter density, a thawing dark energy universe, a higher number of effective species, and a lower amplitude of matter clustering. The mocks are processed through the BAO pipeline by consistently iterating the grid, template, and reconstruction reference cosmologies. We determine a conservative systematic contribution to the error of 0.1% for both the isotropic and anisotropic dilation parameters αiso and αAP. We then directly test the impact of the fiducial cosmology on DESI DR1 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Survey of Surveys: I. The largest compilation of radial velocities for the Galaxy

In the present-day panorama of large spectroscopic surveys, the amount, diversity, and complexity of the available data continuously increase. The overarching goal of studying the formation and evolution of our Galaxy is hampered by the heterogeneity of instruments, selection functions, analysis methods, and measured quantities. Here, we present a comprehensive catalogue, the Survey of Surveys (SoS), built by homogeneously merging the radial velocity (RV) determinations of the largest ground-based spectroscopic surveys to date, such as APOGEE, GALAH, Gaia-ESO, RAVE, and LAMOST, using Gaia as a reference. This pilot study serves to prove the concept and to test the methodology that we plan to apply in the future to the stellar parameters and abundance ratios as well. We have devised a multi-staged procedure that includes: (i) the cross match between Gaia and the spectroscopic surveys using the official Gaia cross-match algorithm, (ii) the normalisation of uncertainties using repeated measurements or the three-cornered hat method, (iii) the cross calibration of the RVs as a function of the main parameters on which depend (magnitude, effective temperature, surface gravity, metallicity, and signal-to-noise ratio) to remove trends and zero point offsets, and (iv) the comparison with external high-resolution samples, such as the Gaia RV standards and the Geneva-Copenhagen survey, to validate the homogenisation procedure and to calibrate the RV zero-point of the SoS catalogue. We provide the largest homogenised RV catalogue to date, containing almost 11 million stars, of which about half come exclusively from Gaia and half in combination with the ground-based surveys. We estimate the accuracy of the RV zero-point to be about 0.16–0.31 km s –1 and the RV precision to be in the range 0.05–1.50 km s –1 depending on the type of star and on its survey provenance. We validate the SoS RVs with open clusters from a high resolution homogeneous samples and provide the systemic velocity of 55 individual open clusters. Additionally, we provide median RVs for 532 clusters recently discovered by Gaia data. The SoS is publicly available and ready to be applied to various research projects, such as the study of star clusters, Galactic archaeology, stellar streams, or the characterisation of planet-hosting stars, to name a few. We also plan to include survey updates and more data sources in future versions of the SoS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Rapid Initial-State Preparation for the Quantum Simulation of Strongly Correlated Molecules

Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003 . Finally, we estimate the total resources to perform ground-state energy estimation of Fe - S cluster systems, including the Fe Mo cofactor by estimating the overlap of different MPS initial states with potential ground states of the Fe Mo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000 , our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the Fe Mo cofactor, producing a total resource estimate of 7.3 × 10 10 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system. Published by the American Physical Society 2025

Berry, Dominic W. (ORCID:0000000334461449)↗
DOE OSTI2020

Accurate prediction of core-level spectra of radicals at density functional theory cost via square gradient minimization and recoupling of mixed configurations

State-specific orbital optimized approaches are more accurate at predicting core-level spectra than traditional linear-response protocols, but their utility had been restricted due to the risk of "variational collapse" down to the ground state. We employ the recently developed square gradient minimization algorithm to reliably avoid variational collapse and study the effectiveness of orbital optimized density functional theory (DFT) at predicting second period element 1s core-level spectra of open-shell systems. Several density functionals (including SCAN, B3LYP, and ωB97X-D3) are found to predict excitation energies from the core to singly occupied levels with high accuracy (≤0.3 eV RMS error) against available experimental data. Higher excited states are, however, more challenging by virtue of being intrinsically multiconfigurational. We thus present a configuration interaction inspired route to self-consistently recouple single determinant mixed configurations obtained from DFT, in order to obtain approximate doublet states. This recoupling scheme is used to predict the C K-edge spectra of the allyl radical, the O K-edge spectra of CO + , and the N K-edge of NO 2 with high accuracy relative to experiment, indicating substantial promise in using this approach for the computation of core-level spectra for doublet species [vs more traditional time dependent DFT, equation of motion coupled cluster singles and doubles (EOM-CCSD), or using unrecoupled mixed configurations]. Finally, we also present general guidelines for computing core-excited states from orbital optimized DFT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗