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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wave packet dynamics and control in excited states of molecular nitrogen

Wave packet interferometry with vacuum ultraviolet light has been used to probe a complex region of the electronic spectrum of molecular nitrogen, N 2 . Wave packets of Rydberg and valence states were excited by using double pulses of vacuum ultraviolet (VUV), free-electron-laser (FEL) light. These wave packets were composed of contributions from multiple electronic states with a moderate principal quantum number (n ~ 4-9) and a range of vibrational and rotational quantum numbers. The phase relationship of the two FEL pulses varied in time, but as demonstrated previously, a shot-by-shot analysis allows the spectra to be sorted according to the phase between the two pulses. The wave packets were probed by angle-resolved photoionization using an infrared pulse with a variable delay after the pair of excitation pulses. The photoelectron branching fractions and angular distributions display oscillations that depend on both the time delays and the relative phases of the VUV pulses. The combination of frequency, time delay, and phase selection provides significant control over the ionization process and ultimately improves the ability to analyze and assign complex molecular spectra.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2024

Network of epistatic interactions in an enzyme active site revealed by large-scale deep mutational scanning

Cooperative interactions between amino acids are critical for protein function. A genetic reflection of cooperativity is epistasis, which is when a change in the amino acid at one position changes the sequence requirements at another position. To assess epistasis within an enzyme active site, we utilized CTX-M β-lactamase as a model system. CTX-M hydrolyzes β-lactam antibiotics to provide antibiotic resistance, allowing a simple functional selection for rapid sorting of modified enzymes. We created all pairwise mutations across 17 active site positions in the β-lactamase enzyme and quantitated the function of variants against two β-lactam antibiotics using next-generation sequencing. Context-dependent sequence requirements were determined by comparing the antibiotic resistance function of double mutations across the CTX-M active site to their predicted function based on the constituent single mutations, revealing both positive epistasis (synergistic interactions) and negative epistasis (antagonistic interactions) between amino acid substitutions. The resulting trends demonstrate that positive epistasis is present throughout the active site, that epistasis between residues is mediated through substrate interactions, and that residues more tolerant to substitutions serve as generic compensators which are responsible for many cases of positive epistasis. Additionally, we show that a key catalytic residue (Glu166) is amenable to compensatory mutations, and we characterize one such double mutant (E166Y/N170G) that acts by an altered catalytic mechanism. These findings shed light on the unique biochemical factors that drive epistasis within an enzyme active site and will inform enzyme engineering efforts by bridging the gap between amino acid sequence and catalytic function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Transition Core Analysis for HEU to LEU Fuel Conversion at the University of Missouri Research Reactor

A new type of low-enriched uranium (LEU) fuel based on an alloy of uranium and molybdenum is expected to allow the conversion of U.S. domestic high-performance research and test reactors requiring high density fuel from highly enriched uranium (HEU) to LEU. The University of Missouri Research Reactor (MURR (R)) has undergone design and performance calculations for conversion to this LEU fuel. Presented in this paper is the analysis of a crucial step in the conversion process: the sequence of MURR transition cores from all fresh to equilibrium burnup LEU operations. During the initial conversion from HEU to LEU fuel, MURR will operate atypically due to the lack of burned LEU elements. Given the constraints of MURR operation and experiments, a proposed transition scheme minimizes the time MURR operates atypically compared to the prototypic cycles currently run with HEU fuel and moves quickly to the same sort of equilibrium cycles for the LEU fuel.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Towards an Agent-Based Blackboard System for Reactor Design Optimization

The field of reactor design is rich with opportunities for applications of computational optimization algorithms; these applications can range from preliminary core design to reactor shuffling patterns. Many of these schemes rely on sets of previously generated solutions (sometimes referred to as “generations”) to inform future decisions. While it is important to build upon prior knowledge, this process requires a full generation of solutions to be formed before future solutions can be examined. Rather than relying on a generational scheme to perform an optimization, we propose using an agent-based approach in conjunction with a blackboard framework for performing reactor design optimizations. Utilizing an agent-based approach allows agents to perform tasks independently, while retaining the ability to build off of previous solutions. We develop an agent-based blackboard system (ABBS) for determining the Pareto front (PF) in sodium fast reactor design optimization problems and compared this with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Our goal is to evaluate the viability of the ABBS in producing a PF that is comparable with the NSGA-II algorithm. The design space consists of the fuel height, fuel smear, and plutonium fraction in the core, and we seek to minimize the reactivity swing and plutonium mass, while maximizing the burnup. The diversity, coverage, and spread of the PFs generated by the two methods are examined, and the ABBS is able to converge to the same PF as the NSGA-II algorithm. These results show that the ABBS is able to find optimal designs that are similar to those found by the NSGA-II algorithm. We conclude our study by applying the ABBS to the design of a sodium-cooled fast reactor to dispose of weapons-grade plutonium. The ABBS finds a core design that can burn upwards of 17.5 kg of weapons-grade plutonium per year and degrade an additional 195 kg of weapons-grade plutonium per year into non-weapons-grade material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Protonic ceramic materials for clean and sustainable energy: advantages and challenges

In recent years, the hydrogen economy has been strongly favoured by governmental and industrial bodies worldwide. A tremendous number of papers are published every year on different aspects of protonic ceramic electrochemical cells (PCECs) due to their lower operation temperature, easier reversible operation, and brighter prospects for further development. While new progress is being made continuously, many critical challenges remain. The effort on PCEC investigation could be more aligned for greater collective impact, e.g. the academic community could devote more effort to overdue critical problems but less to incremental improvements. This review aims to provide some insightful perspectives on critical challenges facing the development of PCECs, to sort out priorities in future effort, and to suggest promising directions to pursue. In this way, it is hoped that the technical readiness level of PCECs might advance more quickly, toward field demonstrations and commercialization for a clean and sustainable energy era.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine Learning Algorithms for Automated NIF Capsule Mandrel Selection

Small shells, approximately 2 mm in diameter, made from Poly(α-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation. As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., “Automation in Target Fabrication” [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. Finally, as an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Quantification of Changes in Fill Tube Curvature During Target Assembly

National Ignition Facility (NIF) targets used for inertial confinement fusion (ICF) experiments are precision-engineered assemblies composed of more than 100 components carefully and precisely assembled with accuracies no larger than a few micrometers in most cases. When individual components deviate from these strict specifications during the assembly process, they can induce target failure. An integral component essential to ICF targets is the capsule-fill-tube assembly. This assembly involves bonding the capsule to a fused silica tube less than 5 μm in thickness, through which the tritium and deuterium (T 2 + D 2 ) fuel mixture is injected into the capsule prior to the NIF shot. The filling tube is bonded to a larger fused silica capillary, about 130 μm in outer diameter, which is coated with a polymeric layer on the order of a 10-μm thickness or less. Only tubes exhibiting a deflection equivalent to less than 1% of their total length from a perfectly straight line are suitable for assembly. Slight curvatures on the order of 1 mm over 10 cm can induce unwanted stresses, potentially causing capsule misalignment and resulting in clogging or leaks during the filling process. Despite manually qualitatively sorting each tube for straightness prior assembly, it has been found that the treatments the tubes undergo once attached to the capsule can alter their curvature. Filling tubes that initially satisfy straightness tolerances can undergo geometric deformation, resulting in curvature deviations exceeding 1% of their total length and thus failing to meet target assembly requirements. Here, in this study, we propose a metric to assess the degree of curvature of the filling tubes and to gauge their changes in curvature caused by the standard thermal processing methods employed in target assembly. In addition, we suggest alternatives to mitigate tube curvature, ensuring they conform to specifications following assembly in the final targets.

Materials science↗
DOE OSTI2023

Visualizing the NIOSH Pocket Guide: Open-source web application for accessing and exploring the NIOSH Pocket Guide to Chemical Hazards

The NIOSH Pocket Guide to Chemical Hazards is a trusted resource that displays key information for a collection of chemicals commonly encountered in the workplace. Entries contain chemical structures—occupational exposure limit information ranging from limits based on full-shift time-weighted averages to acute limits such as short-term exposure limits and immediately dangerous to life or health values, as well as a variety of other data such as chemical-physical properties and symptoms of exposure. The NIOSH Pocket Guide (NPG) is available as a printed, hardcopy book, a PDF version, an electronic database, and a downloadable application for mobile phones. All formats of the NIOSH Pocket Guide allow users to access the data for each chemical separately, however, the guide does not support data analytics or visualization across chemicals. This project reformatted existing data in the NPG to make it searchable and compatible with exploration and analysis using a web application. The resulting application allows users to investigate the relationships between occupational exposure limits, the range and distribution of occupational exposure limits, and the specialized sorting of chemicals by health endpoint or to summarize information of particular interest. These tasks would have previously required manual extraction of the data and analysis. The usability of this application was evaluated among industrial hygienists and researchers and while the existing application seems most relevant to researchers, the open-source code and data are amenable to modification by users to increase customization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The 2022 magneto-optics roadmap

Abstract Magneto-optical (MO) effects, viz. magnetically induced changes in light intensity or polarization upon reflection from or transmission through a magnetic sample, were discovered over a century and a half ago. Initially they played a crucially relevant role in unveiling the fundamentals of electromagnetism and quantum mechanics. A more broad-based relevance and wide-spread use of MO methods, however, remained quite limited until the 1960s due to a lack of suitable, reliable and easy-to-operate light sources. The advent of Laser technology and the availability of other novel light sources led to an enormous expansion of MO measurement techniques and applications that continues to this day (see section 1). The here-assembled roadmap article is intended to provide a meaningful survey over many of the most relevant recent developments, advances, and emerging research directions in a rather condensed form, so that readers can easily access a significant overview about this very dynamic research field. While light source technology and other experimental developments were crucial in the establishment of today’s magneto-optics, progress also relies on an ever-increasing theoretical understanding of MO effects from a quantum mechanical perspective (see section 2), as well as using electromagnetic theory and modelling approaches (see section 3) to enable quantitatively reliable predictions for ever more complex materials, metamaterials, and device geometries. The latest advances in established MO methodologies and especially the utilization of the MO Kerr effect (MOKE) are presented in sections 4 (MOKE spectroscopy), 5 (higher order MOKE effects), 6 (MOKE microscopy), 8 (high sensitivity MOKE), 9 (generalized MO ellipsometry), and 20 (Cotton–Mouton effect in two-dimensional materials). In addition, MO effects are now being investigated and utilized in spectral ranges, to which they originally seemed completely foreign, as those of synchrotron radiation x-rays (see section 14 on three-dimensional magnetic characterization and section 16 on light beams carrying orbital angular momentum) and, very recently, the terahertz (THz) regime (see section 18 on THz MOKE and section 19 on THz ellipsometry for electron paramagnetic resonance detection). Magneto-optics also demonstrates its strength in a unique way when combined with femtosecond laser pulses (see section 10 on ultrafast MOKE and section 15 on magneto-optics using x-ray free electron lasers), facilitating the very active field of time-resolved MO spectroscopy that enables investigations of phenomena like spin relaxation of non-equilibrium photoexcited carriers, transient modifications of ferromagnetic order, and photo-induced dynamic phase transitions, to name a few. Recent progress in nanoscience and nanotechnology, which is intimately linked to the achieved impressive ability to reliably fabricate materials and functional structures at the nanoscale, now enables the exploitation of strongly enhanced MO effects induced by light–matter interaction at the nanoscale (see section 12 on magnetoplasmonics and section 13 on MO metasurfaces). MO effects are also at the very heart of powerful magnetic characterization techniques like Brillouin light scattering and time-resolved pump-probe measurements for the study of spin waves (see section 7), their interactions with acoustic waves (see section 11), and ultra-sensitive magnetic field sensing applications based on nitrogen-vacancy centres in diamond (see section 17). Despite our best attempt to represent the field of magneto-optics accurately and do justice to all its novel developments and its diversity, the research area is so extensive and active that there remains great latitude in deciding what to include in an article of this sort, which in turn means that some areas might not be adequately represented here. However, we feel that the 20 sections that form this 2022 magneto-optics roadmap article, each written by experts in the field and addressing a specific subject on only two pages, provide an accurate snapshot of where this research field stands today. Correspondingly, it should act as a valuable reference point and guideline for emerging research directions in modern magneto-optics, as well as illustrate the directions this research field might take in the foreseeable future.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Developing new pathways for energy and environmental decision-making in India: a review

Abstract India faces a dual challenge of economic development and responding to climate change. Although India’s per capita emissions are well below global average, the country is one of the world’s largest greenhouse gas emitters. Indian policymakers and stakeholders require high-quality data and research to assess low-emissions, sustainable development strategies. Peer-reviewed literature is a key source of this information and also a key venue for conversation amongst research leaders. This paper examines the recent peer-reviewed literature on India’s 2030 and 2050 pathways. We conducted a systematic literature review to identify key quantitative national modeling studies. From the 34 studies identified, we synthesized scenario data to draw common conclusions and identify critical research gaps. The main focus was on examining the coverage and the state of information available on low-carbon pathways. Overall, we find a few scenarios that are potentially consistent with a 2070 net-zero goal, but more limited assessment of pathways to reach net-zero emissions before this date. Mitigation pathways with greater ambition are required across all energy sectors to ensure a smooth transition to net-zero emissions by or before 2070. The scenarios confirm that reducing emissions to below 2 GtCO 2 yr −1 by mid-century would necessitate significant transformations of the Indian energy sector, such as, a decrease in unabated coal power capacity, transportation modal shift, and industrial process switching. The assessment also finds substantial differences in final energy estimates reported across studies, particularly in transportation. The lack of consistency in, and transparency about underlying drivers, assumptions, and even outputs across studies points to the critical need for the sorts of coordinated, multi-model studies that have proven exceptionally valuable for decision makers in other major emitting countries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation: Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. Results: We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. Availability and implementation MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Quartet-based inference is statistically consistent under the unified duplication-loss-coalescence model

Abstract Motivation The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. This has motivated an extensive body of work on phylogenetic methods that are statistically consistent under MSC. One such particularly popular method is ASTRAL, a quartet-based species tree inference method. Novel studies suggest that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). GDL is prevalent in evolutionary histories and is the first core process in the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. Results In this work, we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that the quartet-based inference approach is statistically consistent under DLCoal. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Laminarin stimulates single cell rates of sulfate reduction whereas oxygen inhibits transcriptomic activity in coastal marine sediment

Abstract The chemical cycles carried out by bacteria and archaea living in coastal sediments are vital aspects of benthic ecology. These ecosystems are subject to physical disruption, which may allow for increased respiration and complex carbon consumption—impacting chemical cycling in this environment often thought to be a terminal place of deposition. We use the redox-enzyme sensitive probe RedoxSensor Green to measure rates of electron transfer physiology in individual sulfate reducer cells residing in anoxic sediment, subjected to transient exposure of oxygen and laminarin. We use index fluorescence activated cell sorting and single cell genomics sequencing to link those measurements to genomes of respiring cells. We measure per-cell sulfate reduction rates in marine sediments (0.01–4.7 fmol SO42− cell−1 h−1) and determine that cells within the Chloroflexota phylum are the most active in respiration. Chloroflexota respiration activity is also stimulated with the addition of laminarin, even in marine sediments already rich in organic matter. Evaluating metatranscriptomic data alongside this respiration-based technique, Chloroflexota genomes encode laminarinases indicating a likely ability to degrade laminarin. We also provide evidence that abundant Patescibacteria cells do not use electron transport pathways for energy, and instead likely carry out fermentation of polysaccharides. There is a decoupling of respiration-related activity rates from transcription, as respiration rates increase while transcription decreases with oxygen exposure. Overall, we reveal an active community of respiring Chloroflexota that cycles sulfate at potential rates of 23–40 nmol h−1 per cm3 sediment in incubation settings, and non-respiratory Patescibacteria that can cycle complex polysaccharides.

Lindsay, Melody R.↗
DOE OSTI2025

Climate warming enhances biodiversity and stability of grassland soil phosphorus-cycling microbial communities

Abstract Climate warming poses significant challenges to global phosphorus sustainability, an essential component of Earth biogeochemistry cycling and water-food-energy nexus. Despite the crucial role of polyphosphate-accumulating organism as key functional microbial agents in phosphorus cycling, the impacts of global climate warming on polyphosphate accumulating organism communities remain largely enigmatic. This study investigates the effects of climate warming on the taxonomic, network, and functional profiles of soil bacterial polyphosphate-accumulating organisms, leveraging fluorescence-activated cell sorting and single-cell Raman spectroscopy. Climate warming enhances both taxonomic and functional biodiversity of polyphosphate-accumulating organisms via biotic interactions and environmental filtering, with observed functionality-biodiversity relationships supporting the functional redundancy theory. Furthermore, polyphosphate-accumulating organism network complexity and stability rise under warming with strengthened positive relationships, supporting stress gradient hypothesis and the belief that complexity begets stability. Finally, polyphosphate-accumulating organisms are significantly correlated to key ecosystem functioning in carbon and phosphorus cycling under warming. Our study suggests that preserving polyphosphate-accumulating organism communities is crucial for maintaining soil ecosystem functioning and sustainable phosphorus management in a warming world and opens avenues for predicting the responses of other functional microbial groups to climate change, beneficially or maliciously.

Environmental Sciences & Ecology↗
DOE OSTI2022

Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations

ABSTRACT Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present hyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. In hyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we compare hyperrank to the common mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

XRISM high-resolution X-ray spectroscopy of Cygnus X-1: Orbital and short-term variability of iron absorption

We present the first high-resolution spectroscopy of the black hole high-mass X-ray binary Cygnus X-1 with XRISM, including orbital-phase-resolved analyses and tentative evidence of short-term variability in the Fe K band on second timescales. Using data from the Performance Verification phase in 2024 April, we analyzed spectral variability across orbital phases with the Resolve microcalorimeter and the Xtend CCD imager. The unprecedented resolution of Resolve reveals variability in highly ionized Fe absorption lines. The absorption features show orbital-phase-dependent variability in column density, ionization state, and blueshifted velocity, suggesting structural variations in the focused stellar wind along the line of sight. We also find indications of subtle broadening of the neutral Fe emission profile. In addition, intensity-sorted spectroscopy during dip phases suggests possible variability on timescales of a few seconds in the absorption features, consistent with cooler, denser, and lower-ionized gas clumps. Although the statistical significance is limited, these results hint that the stellar wind and the X-rays from the accretion disk around the black hole may interact on timescales as short as a few seconds. These XRISM results constrain wind-fed accretion in Cyg X-1 and highlight Resolve’s capability to probe plasma environments in high-mass X-ray binaries.

Astronomy and AstroPhysics↗