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DOE OSTI2024

Ratio-preserving approach to cosmological concordance

Cosmological observables are particularly sensitive to key ratios of energy densities and rates, both today and at earlier epochs of the Universe. Well-known examples include the photon-to-baryon and the matter-to-radiation ratios. Equally important, though less publicized, are the ratios of pressure-supported to pressureless matter and the Thomson scattering rate to the Hubble rate around recombination, both of which observations tightly constrain. Preserving these key ratios in theories beyond the Λ Cold-Dark-Matter ( Λ CDM ) model ensures broad concordance with a large swath of datasets when addressing cosmological tensions. We demonstrate that a mirror dark sector, reflecting a partial Z 2 symmetry with the Standard Model, in conjunction with percentage level changes to the visible fine-structure constant and electron mass which represent a phenomenological change to the Thomson scattering rate, maintains essential cosmological ratios. Incorporating this ratio-preserving approach into a cosmological framework significantly improves agreement to observational data ( Δ χ 2 = - 35.72 ) and completely eliminates the Hubble tension with a cosmologically inferred H 0 = 73.80 ± 1.02 km / s / Mpc when including the S H 0 ES calibration in our analysis. While our approach is certainly nonminimal, it emphasizes the importance of keeping key ratios constant when exploring models beyond Λ CDM .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

RESIN: Responsible Innovation for Highly Recyclable Plastics - TASK 4: Risk Assessment Framework

This report covers the entirety of Task 4, but its main purpose is to deliver milestones ML4.4 and ML4.5, the last two SOPO milestones under Task 4. ML4.4 reports on the compatibility of polymer properties that affect both environmental performance and functional performance and the tradeoffs involved in turning these properties to the benefit of each. ML4.5 presents a complete set of information on the critical properties of benign target products, where benign products are defined as those with the shortest environmental lifetime which meet performance requirements. To support and provide context to the discussions of ML4.4 and ML4.5 and to provide a complete picture of Task 4, milestones 4.1-4.3 are briefly summarized at the beginning of the report. The discussion of ML4.4 introduces the notion of polymer persistence as a proxy for environmental risk. It then discusses the development and comparison of two machine leaning models explored for estimating polymer degradation rates, a random forest (RF) classifier and an RF regressor. Given the advantage of continuous outputs rather than simple classes, the RF regressor was incorporated in the Excel risk calculator, which to this point could implement the objectives of subtasks 4.1-4.3, estimating polymer release and redistribution. The ML4.4 discussion then addresses the effect of each of the three polymer features used by the RF regressor on polymer functional performance. These features are number molecular weight (Mn), glass transition temperature (T g ) and heat of fusion (H fus ). The discussion of ML4.5 reviews the conceptual framework of the risk model, which served as the foundation for developing the Excel risk calculator and describes the use of, and assumptions within, the calculator. Appendix A is further provided as a user’s guide for the calculator. To demonstrate how the calculator is intended to be used by developers in the design of low-risk polymers, an analysis of 27 hypothetical polymers defined by varying values for the three polymer features used by the RF regressor is presented. The range of parameter values selected produces estimates of polymer degradation rates and lifetimes that may be typical of various consumer products made from polyurethane polymers and shows how changes in polymer features affect lifetimes. The demonstration also predicts the final distribution of released polymer materials in environmental compartments as a function of consumer product mix and assumed leakage rates of end-of-life processes. Lastly, this report summarizes the achievement of Task 4 goals from original conception to final delivery and discusses how and to what degree to which each subtask goal was achieved.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Measurement of the muon neutrino charged-current mesonless cross section in the NOvA near detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab. Its physics goals include precision neutrino oscillation measurements, neutrino interaction cross-section measurements and beyond Standard Model explorations. We present a measurement of muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is performed as a function of the kinematics of the final state muon. Our chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and it provides handles for constraining the cross section systematic uncertainties in oscillation analyses in present and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA Near detector. This allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. We study strategies for constraining the remaining charged-pion background via Michel electron information in a template fitting approach. The main experimental result is a two-dimensional differential cross section as a function of final-state muon kinetic energy and polar angle. The parameters of this measurement, including binning and unfolding, were optimized to reduce the expected systematic uncertainty in the total cross section. The final result shows good agreement with the main GENIE-based simulation framework that was independently fine-tuned in NOvA. We finally propose improvements and subsequent steps that build on this analysis and further dissect the final states of neutrino interactions. This work has been supported by US DOE grant DE-SC0015684.

Sánchez Falero, Sebastián Jesús [Iowa State U.]↗

DOE OSTI2020

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.

Broughton, Michael↗

DOE OSTI2023

On the Impact of Mechanics on Electrochemistry of Lithium-Ion Battery Anodes

Abstract Models exploring electrochemistry-mechanics coupling in liquid electrolyte lithium-ion battery anodes have traditionally incorporated stress impact on thermodynamics, bulk diffusive transport, and fracture, while stress-kinetics coupling is more explored in the context of all solid-state batteries. Here, we showcase the existence of strong link between active particle surface pressure and reaction kinetics affecting performance even in liquid electrolyte systems. Traction-free and immobile particle surface mechanical boundary conditions are used to delineate the varying pressure magnitudes in graphite host during cycling. Both tensile and compressive stresses are generated in traction-free case, while a fixed surface subjects the entire particle to a compression state. Pressure magnitudes are nearly two to three orders of magnitude higher for the latter resulting in significant depression of open circuit potential and improvement of exchange current densities compared to stress-free state. The results demonstrate the need for incorporating stress-kinetics linkage in models and provide a rationale for putting battery electrodes under compression to improve kinetics.

25 ENERGY STORAGE↗

DOE OSTI2023

Search for direct production of electroweakinos in final states with one lepton, jets and missing transverse momentum in pp collisions at $\sqrt{s} $ = 13 TeV with the ATLAS detector

Searches for electroweak production of wino-like chargino pairs, ${\overset{\sim }{\chi}}_1^{+}{\overset{\sim }{\chi}}_1^{-}$, and of wino-like chargino and next-to-lightest neutralino, ${\overset{\sim }{\chi}}_1^{\pm }{\overset{\sim }{\chi}}_2^0$, are presented. The models explored assume that the charginos decay into a W boson and the lightest neutralino, ${\overset{\sim }{\chi}}_1^{\pm}\to {W}^{\pm }{\overset{\sim }{\chi}}_1^0$. The next-to-lightest neutralinos are degenerate in mass with the chargino and decay to ${\overset{\sim }{\chi}}_1^0$ and either a Z or a Higgs boson, ${\overset{\sim }{\chi}}_2^0\to Z{\overset{\sim }{\chi}}_1^0$ or $h{\overset{\sim }{\chi}}_1^0$. The searches exploit the presence of a single isolated lepton and missing transverse momentum from the W boson decay products and the lightest neutralinos, and the presence of jets from hadronically decaying Z or W bosons or from the Higgs boson decaying into a pair of b-quarks. The searches use 139 fb –1 of $\sqrt{s}$ = 13 TeV proton-proton collisions data collected by the ATLAS detector at the Large Hadron Collider between 2015 and 2018. No deviations from the Standard Model expectations are found, and 95% confidence level exclusion limits are set. Chargino masses ranging from 260 to 520 GeV are excluded for a massless ${\overset{\sim }{\chi}}_1^0$ in chargino pair production models. Degenerate chargino and next-to-lightest neutralino masses ranging from 260 to 420 GeV are excluded for a massless ${\overset{\sim }{\chi}}_1^0$ for ${\overset{\sim }{\chi}}_2^0\to Z{\overset{\sim }{\chi}}_1^0$. For decays through an on-shell Higgs boson and for mass-splitting between ${\overset{\sim }{\chi}}_1^{\pm }/{\overset{\sim }{\chi}}_2^0$ and ${\overset{\sim }{\chi}}_1^0$ as small as the Higgs boson mass, mass limits are improved by up to 40 GeV in the range of 200–260 GeV and 280–470 GeV compared to previous ATLAS constraints.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2008

Science Goals in Radiation Protection for Exploration

Space radiation presents major challenges to future missions to the Earth s moon or Mars. Health risks of concern include cancer, degenerative and performance risks to the central nervous system, heart and lens, and the acute radiation syndromes. The galactic cosmic rays (GCR) contain high energy and charge (HZE) nuclei, which have been shown to cause qualitatively distinct biological damage compared to terresterial radiation, such as X-rays or gamma-rays, causing risk estimates to be highly uncertain. The biological effects of solar particle events (SPE) are similar to terresterial radiation except for their biological dose-rate modifiers; however the onset and size of SPEs are difficult to predict. The high energies of GCR reduce the effectiveness of shielding, while SPE s can be shielded however the current gap in radiobiological knowledge hinders optimization. Methods used to project risks on Earth must be modified because of the large uncertainties in projecting health risks from space radiation, and thus impact mission requirements and costs. We describe NASA s unique approach to radiation safety that applies probabilistic risk assessments and uncertainty based criteria within the occupational health program for astronauts and to mission design. The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in radiation risk projection models. Exploration science goals in radiation protection are centered on ground-based research to achieve the necessary biological knowledge, and in the development of new technologies to improve SPE monitoring and optimize shielding. Radiobiology research is centered on a ground based program investigating the radiobiology of high-energy protons and HZE nuclei at the NASA Space Radiation Laboratory (NSRL) located at DoE s Brookhaven National Laboratory in Upton, NY. We describe recent NSRL results that are closing the knowledge gap in HZE radiobiology and improving exploration risk estimates. Linking probabilistic risk assessment to research goals makes it possible to express risk management objectives in terms of quantitative metrics, which include the number of days in space without exceeding a given risk level within well defined confidence limits, and probabilistic assessments of the effectiveness of design trade spaces such as material type, mass, solar cycle, crew selection criteria, and biological countermeasures. New research in SPE alert and risk assessment, individual radiation sensitivity, and biological countermeasure development are described.

Cucinotta, Francs A.↗

NASA NTRS2024

NASA’s Top Human System Research and Technology Needs for Mars

NASA is working with industry and international partners to return humans to the Moon and to eventually enable humans to explore Mars. Within NASA, several organizations work together to identify, prioritize, fund, execute, and operationalize the research and technology development (R&TD) that will be necessary to enable crew health and performance (CHP) during these future missions. These organizations include flight programs, the Health and Medical Technical Authority (HMTA), the Human Research Program, the Space Technology Mission Directorate, System Capability Leadership Teams, and other organizations, many of which existed for several years prior to the creation of the Moon-to-Mars (M2M) Program Office in 2023. A variety of constructs, vocabularies, and processes exist for managing risks and supporting strategic planning across these organizations. For example, M2M objectives, program risks, human system risks, human research gaps, capability gaps, and envisioned futures are all constructs currently used within NASA to identify and prioritize R&TD needs. These strategic planning constructs are evolving to allow M2M objectives and R&TD investments to be aligned and traced at a detailed level. A recognized need exists among stakeholder organizations to identify and communicate the highest CHP R&TD priorities in a unified and digestible way that addresses the perspectives of NASA’s CHP community. To achieve this, the HMTA arranged a series of discussions with representatives of NASA’s CHP community, during which the 8 highest priority CHP capabilities that will enable human missions to Mars, referred to as the “top human system capability needs for Mars”, were identified. The list includes Earth-independent human operations; Mars-duration food system; Mars-duration effects on human physiology; risk mitigations for vehicle atmospheres; computational injury and anthropometric models; exploration exercise countermeasures; individual variability in responses to spaceflight; and sensorimotor countermeasures. Existing tools and processes for strategic planning and risk management were evaluated, as well as the technical practicalities, cost, and schedule feasibility associated with potential R&TD investments in different capability need areas. This capability needs report is not owned by any one NASA organization and does not replace existing strategic or program planning processes; rather it aims to complement and inform them with a unified set of community generated priorities. These top capability needs will be re-evaluated periodically based on R&TD progress and the evolving M2M architecture.

technology gaps↗

NASA NTRSDate not supplied

NASA’s Top Human System Research and Technology Needs for Mars

technology gaps↗

DOE OSTI2022

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models

We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $\$$653 and $\$$685, respectively, at a willingness-to-pay (WTP) threshold of $\$$66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $\$$809 at the same WTP threshold. Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗

DOE OSTI2026

Expected sensitivity of the Light Dark Matter eXperiment to long-lived dark photons and axion-like particles

The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment primarily designed to achieve world-leading, model-independent sensitivity to sub-GeV dark matter particles. LDMX aims to identify dark sector particle production through the detection of events with substantial missing energy and momentum, a signature of invisible particles escaping detection. Beyond this primary objective, LDMX offers a complementary search strategy for long-lived, visibly decaying particles, such as dark photons and axion-like particles. We present the first detailed evaluation of the ability of LDMX to identify visibly decaying, long-lived particles that couple to electrons using a detailed simulation, based on the Geant 4-toolkit, that incorporates realistic detection efficiencies and background levels. We demonstrate that LDMX can achieve a sensitivity that is competitive with other experiments that are currently running. The models explored in this paper are distinct and complementary to those probed in the LDMX flagship missing-momentum analysis. Through searching for both invisible dark matter and visibly decaying long-lived signatures, LDMX will significantly advance the search for light dark matter and provide a broad exploration of the sub-GeV dark sector.[graphic not available: see fulltext]

Akesson, Torsten [Lund U.] (ORCID:0000000341415408↗

DOE OSTI2025

Investigating the Relationship Between Bolide Entry Angle and Apparent Direction of Infrasound Signal Arrivals

Infrasound sensing offers critical capabilities for detecting and geolocating bolide events globally. However, the observed back azimuths, directions from which infrasound signals arrive at stations, often differ from the theoretical expectations based on the bolide’s peak brightness location. For objects with shallow entry angles, which traverse longer atmospheric paths, acoustic energy may be emitted from multiple points along the trajectory, leading to substantial variability in back azimuth residuals. This study investigates how the entry angle of energetic bolides affects the back azimuth deviations, independent of extrinsic factors such as atmospheric propagation, station noise, and signal processing methodologies. A theoretical framework, the Bolide Infrasound Back-Azimuth EXplorer Model (BIBEX-M), was developed to compute predicted back azimuths solely from geometric considerations. The model quantifies how these residuals vary as a function of source-to-receiver distance, revealing that bolides entering at shallow angles, e.g., 10°, can produce average residuals of 20°, with deviations reaching up to 46° at distances below 1000 km, and remaining significant even at 5000 km (up to 8°). In contrast, bolides with steeper entry angles, e.g., > 60°, show smaller deviations, typically under 5° at 1000 km and diminishing to less than ~1° beyond 5000 km. These findings attest to the need for careful interpretation when evaluating signal detections and estimating bolide locations. This work is not only pertinent to bolides but also to other high-energy, extended-duration atmospheric phenomena such as space debris and reentry events, where similar geometric considerations can influence infrasound arrival directions.

Acoustics↗

DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Atomic structure and Mott nature of the insulating charge density wave phase of 1T-TaS 2

Here, using x-ray pair distribution function (PDF) analysis and computer modeling, we explore structure models for the complex charge density wave (CDW) phases of layered 1T-TaS 2 that both well capture their atomic-level features and are amenable to electronic structure calculations. The models give the most probable position of constituent atoms in terms of 3D repetitive unit cells comprising a minimum number of Ta–S layers. Structure modeling results confirm the emergence of star-of-David (SD) like clusters of Ta atoms in the high-temperature incommensurate (IC) CDW phase and show that, contrary to the suggestions of recent studies, the low-temperature commensurate (C) CDW phase expands upon cooling thus reducing lattice strain. The C-CDW phase is also found to preserve the stacking sequence of Ta–S layers found in the room temperature, nearly commensurate (NC) CDW phase to a large extent. DFT based on the PDF refined model shows that bulk C-CDW 1T-TaS 2 also preserves the insulating state of individual layers of SD clusters, favoring the Mott physics description of the metal-to-insulator (NC-CDW to C-CDW) phase transition in 1T-TaS 2 . Our work highlights the importance of using precise crystal structure models in determining the nature of electronic phases in complex materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

GW190814: Impact of a 2.6 solar mass neutron star on the nucleonic equations of state

Is the secondary component of GW190814 the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system? This is the central question animating this paper. Covariant density functional theory provides a unique framework to investigate both the properties of finite nuclei and neutron stars, while enforcing causality at all densities. By tuning existing energy density functionals we were able to: (i) account for a 2.6 M ⊙ neutron star, (ii) satisfy the original constraint on the tidal deformability of a 1.4 M ⊙ neutron star, and (iii) reproduce ground-state properties of finite nuclei. Yet, for the class of models explored in this work, we find that the stiffening of the equation of state required to support supermassive neutron stars is inconsistent with either constraints obtained from energetic heavy-ion collisions or from the low deformability of medium-mass stars. Furthermore, we speculate that the maximum neutron star mass can not be significantly higher than the existing observational limit and that the 2.6 M ⊙ compact object is likely to be the lightest black hole ever discovered.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Anomalous electroweak physics unraveled via evidential deep learning

The ever-growing ecosystem of beyond standard model (BSM) calculations and parametrizations has motivated the development of systematic methods for making quantitative cross-comparisons over the wide range of possible models, especially with controllable uncertainties. In this setting, the language of uncertainty quantification (UQ) furnishes useful metrics for assessing statistical overlaps and discrepancies among BSM and related models. In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. We construct several potentially BSM-motivated scenarios for the anomalous electroweak interaction (AEWI) of neutrinos with nucleons in deep inelastic scattering ( v DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $\varDelta \chi ^{2}$ statistic for a typical multi-GeV v DIS experiment, CDHSW. Our framework effectively highlights areas of model agreement and provides a classification of out-of-distribution (OOD) samples. By offering the opportunity to quantitatively understand model overlaps, the approach presented in this work can help facilitate efficient BSM model exploration and exclusion for future New Physics searches.

AI↗

DOE OSTI2023

Effects of Magnetic Fields on Gas Dynamics and Star Formation in Nuclear Rings

Nuclear rings at the centers of barred galaxies are known to be strongly magnetized. To explore the effects of magnetic fields on star formation in these rings and nuclear gas flows, we run magnetohydrodynamic simulations in which there is a temporally constant magnetized inflow to the ring, representing a bar-driven inflow. The mass inflow rate is 1M ⊙ yr –1 , and we explore models with a range of field strength in the inflow. We adopt the TIGRESS framework developed by Kim & Ostriker to handle radiative heating and cooling, star formation, and resulting supernova (SN) feedback. We find that magnetic fields are efficiently amplified in the ring due to rotational shear and SN feedback. Within a few 100 Myr, the turbulent component Btrb in the ring saturates at ~35 μG (in rough equipartition with the turbulent kinetic energy density), while the regular component Breg exceeds 50 μG. Expanding superbubbles created by clustered SN explosions vertically drag predominantly toroidal fields from near the midplane to produce poloidal fields in high-altitude regions. The growth of magnetic fields greatly suppresses star formation at late times. Simultaneously, strong magnetic tension in the ring drives radially inward accretion flows from the ring to form a circumnuclear disk in the central region; this feature is absent in the unmagnetized model.

79 ASTRONOMY AND ASTROPHYSICS↗