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DOE OSTI2023

DESI Observations of the Andromeda Galaxy: Revealing the Immigration History of Our Nearest Neighbor

Abstract We present Dark Energy Spectroscopic Instrument (DESI) observations of the inner halo of M31, which reveal the kinematics of a recent merger—a galactic immigration event—in exquisite detail. Of the 11,416 sources studied in 3.75 hr of on-sky exposure time, 7438 are M31 sources with well-measured radial velocities. The observations reveal intricate coherent kinematic structure in the positions and velocities of individual stars: streams, wedges, and chevrons. While hints of coherent structures have been previously detected in M31, this is the first time they have been seen with such detail and clarity in a galaxy beyond the Milky Way. We find clear kinematic evidence for shell structures in the Giant Stellar Stream, the Northeast Shelf, and Western Shelf regions. The kinematics are remarkably similar to the predictions of dynamical models constructed to explain the spatial morphology of the inner halo. The results are consistent with the interpretation that much of the substructure in the inner halo of M31 is produced by a single galactic immigration event 1–2 Gyr ago. Significant numbers of metal-rich stars ([Fe/H] > − 0.5) are present in all of the detected substructures, suggesting that the immigrating galaxy had an extended star formation history. We also investigate the ability of the shells and Giant Stellar Stream to constrain the gravitational potential of M31, and estimate the mass within a projected radius of 125 kpc to be log 10 M NFW ( < 125 kpc ) / M ⊙ = 11.80 − 0.10 + 0.12 . The results herald a new era in our ability to study stars on a galactic scale and the immigration histories of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Capacity Density Considerations for Floating Offshore Wind Farms in Ultradeep Waters

Capacity density describes the concentration of wind energy development in an area and is often specified in terms of megawatts-per-square-kilometer (MW/km2). Understanding capacity density trends in wind energy projects helps to inform both energy system and spatial planning efforts. Borrman et al. (2018) and Mulas Hernando et al. (2023) analyze capacity density trends for fixed-bottom offshore wind farms in Europe and the United States, respectively, and Cooperman et al. (2022) explores how floating offshore wind mooring technology choices may impact wind plant layout through setbacks from lease area boundaries in waters up to 1,300 m deep. Technical challenges facing floating offshore wind development in ultradeep waters (beyond 1,300 m) could impact achievable capacity densities, with potential implications to marine spatial planning and project economics. When compared to fixed-bottom commercial-scale wind farms, mooring system footprints from floating offshore wind systems can constrain capacity density in some circumstances. In this study, we conduct an initial investigation of how taut mooring configurations may constrain floating offshore wind turbine placement and estimate capacity density for representative floating wind plants in generic lease areas. In addition, we explore floating wind plant capacity density drivers in ultradeep waters by characterizing area utilization for a range of lease area characteristics. This analysis highlights the primary challenges that floating offshore wind systems may encounter in achieving capacity densities comparable to commercial-scale fixed-bottom projects at ultradeep water depths, from a technical standpoint.

capacity density↗

DOE OSTI2022

Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects

Abstract. A previous study on the use of nudging in E3SM Atmosphere Model version 1 (EAMv1) had an unresolved issue; i.e., a simulation nudged to EAMv1's own meteorology showed non-negligible deviations from the free-running baseline simulation over some of the subtropical marine stratocumulus and trade cumulus regions. Here, we demonstrate that the deviations can be substantially reduced by (1) changing where the nudging tendency is calculated in the time integration loop of a nudged EAM simulation so as to improve consistency with the free-running baseline and (2) increasing the frequency of the constraining data so as to better capture strong sub-diurnal variations. The fact that modification (2) improves the climate representativeness of the nudged simulations has motivated us to investigate whether the use of newer reanalysis products with higher data frequency can help improve nudged hindcast simulations by better capturing the observed weather events. To answer this question, we present simulations conducted at EAMv1's standard horizontal resolution (approximately 1∘) with nudging towards 6-hourly ERA-Interim reanalysis or 6-hourly, 3-hourly, or hourly ERA5 reanalysis. These simulations are evaluated against the climatology of free-running EAMv1 simulations as well as reanalyses, satellite retrievals, and in situ measurements from the Atmospheric Radiation Measurement user facility. For the 1∘ EAMv1 simulations, we recommend using the relocated nudging tendency calculation and the ERA5 reanalysis at 3-hourly or higher frequency. Simulations used for estimating the anthropogenic aerosol effects often use nudging to help discern signal from noise. The sensitivity of such estimates to the configuration of nudging is investigated in EAMv1, again using the standard 1∘ horizontal resolution. We find that, when estimating the global mean effects, the frequency of constraining data has relatively small impacts, while the choice of nudged variables can change the results substantially. The nudging of air temperature (in addition to horizontal winds) has two non-negligible effects. First, when the constraining data come from reanalysis, the nudging-induced mean bias correction can cause significant changes in the simulated clouds and hence substantially different estimates of the aerosol effects. The impact of the mean bias correction on ice cloud formation has been noted in previous studies and is also seen in EAMv1. For applications like ours, where the preferred configurations of nudging are those capable of providing results consistent with the multi-year free-running simulations, the consequence of the mean bias correction is undesirable. The second important impact of temperature nudging is a significant suppression of adjustments to aerosol forcing, which also causes changes in the estimated aerosol effects. This effect can be seen in simulations nudged to either reanalysis or EAM's own meteorology. These results suggest that nudging horizontal winds but not temperature is a better choice for estimating the anthropogenic aerosol effects.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Energetics of hydroxylbastnäsite solid solutions, La 1-$\chi$ Nd $\chi$ CO 3 OH

Bastnäsites (LnCO 3 (F,OH)) are a group of common rare earth elements (REE)-bearing minerals and are one of the primary global sources of REE. Due to the chemical similarities among REE, bastnӓsites tend to occur as solid solutions instead of end members in REE containing ores. To better understand the processes and the mechanisms of formation of such deposits, it is essential to determine the thermodynamic properties of bastnӓsites, including hydroxylbastnӓsite (LnCO 3 OH) solid solutions. In this work, we performed detailed structural and calorimetric investigations on synthetic hexagonal La–Nd hydroxylbastnӓsite (La 1-$\chi$ NdCO 3 OH, $\chi$ = 0, 0.25, 0.5, 0.75, 1) solid solutions. X-ray diffraction confirms the crystal structure of the solid solution series in the $\ P$6 space group, and pair distribution function (PDF) analysis reveals local bonding environments characterized by three different types of 9-coordinated metal-oxygen polyhedra. Unit cell parameters of La1–xNdxCO3OH exhibit a nearly linear relation with the Nd content x, suggesting a random distribution of La and Nd in the structure. Their standard enthalpies of formation (Δ$\ H$° f ) were determined by high temperature oxide melt drop solution calorimetry, from which the enthalpies of mixing (Δ$\ H$ mix ) were derived. The Δ$\ H$ mix can be fitted by a regular solution model with an interaction parameter of 12.58 ± 0.16 kJ/mol, suggesting enthalpic metastability of La1–xNdxCO3OH relative to the two endmembers. Combining entropy and enthalpy, we further estimated the Gibbs free energies of mixing (Δ$\ G$ mix ) at relevant temperatures, revealing favorable temperatures under which the intermediate La 1-$\chi$ NdCO 3 OH phases can be stabilized. Such entropy-driven stabilization, as is consistent with our geochemical modeling results, may explain the enhancement of thermal stability of the solid solutions in nature. Additionally, the temperature range constrained from this study may be used to estimate the thermal history of REE bastnӓsite deposit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Estimated timescales for wet deposition of organic compounds as a function of Henry's law constants

Atmospheric organic compounds may deposit to Earth's surfaces via dry deposition, driven by concentration gradients, and wet deposition, driven by the scavenging of compounds by precipitation. Their removal by deposition has downstream impacts on concentrations of secondary organic aerosol by removing potential aerosol precursors that would otherwise oxidize to form particulate matter. The impact of deposition processes can consequently be considered as competition between rates of oxidation and deposition, but timescales for deposition are not well constrained. While timescales for dry deposition have been estimated and experimentally validated in the past, understanding of wet deposition of organics is still very limited. In this work, we estimate the wet deposition timescale for gas-phase organic compounds in the atmosphere as a function of Henry's law constants, H, using real-world precipitation frequency and size distributions at five globally-distributed sites. The wet deposition timescale decreases significantly with the increase of H until reaching a stable minimum for compounds with H > 10 5 M per atm. We estimate that the median wet deposition timescale for highly soluble gases is approximately 5 hours during a continuous rain event for all sites. However, median estimated timescales ranged from 80 to 200 hours, depending on location. Timescales are found to depend primarily on the frequency and duration of precipitation events rather than their intensity or size characteristics. Based on these data, we demonstrate that timescales for wet deposition of gases can be estimated at any given location using only basic precipitation information, without detailed or high-precision measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗

DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗

DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation: Preprint

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.

chance constraints↗

DOE OSTI2020

Probing neutrino quantum decoherence at reactor experiments

We explore how well reactor antineutrino experiments can constrain or measure the loss of quantum coherence in neutrino oscillations. We assume that decoherence effects are encoded in the size of the neutrino wave-packet, σ . We find that the current experiments Daya Bay and the Reactor Experiment for Neutrino Oscillation (RENO) already constrain σ > 1 . 0 × 10 - 4 nm and estimate that future data from the Jiangmen Underground Neutrino Observatory (JUNO) would be sensitive to σ < 2 . 1 × 10 - 3 nm. If the effects of loss of coherence are within the sensitivity of JUNO, we expect σ to be measured with good precision. The discovery of nontrivial decoherence effects in JUNO would indicate that our understanding of the coherence of neutrino sources is, at least, incomplete.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

The solubility of titanite in silicate melt determined from growth and dissolution experiments

The solubility of titanite (CaTiSiO 5 ) in Si-rich melts was measured experimentally through growth experiments at 800–1000 °C, 0.5–1.0 GPa, log fO 2 ~ CCO–0.8, t = 72–168 h, and H 2 O = 0 to 4 wt.%, and in dissolution experiments at 925–1300 °C, 0.8 GPa, t = 18–118 h, and H 2 O = 1–10 wt.% in a piston-cylinder apparatus. Run product glasses in growth experiments were homogeneous, and iron loss suppressed ilmenite crystallization. Saturation concentrations in dissolution experiments were estimated by fitting measured diffusion profiles. Titanite solubility increases with increasing temperature and melt composition parameter $M = {\text{ molar }}\left( {{\text{Na }} + {\text{ K }} + {\text{ 2Ca}}} \right)/\left( {{\text{Al }} \times {\text{ Si}}} \right)$ . Multiple linear regression of glass composition data from growth and dissolution experiments ( n = 29) plus 39 experiments from the LEPR database (Hirschmann et al. 2008 ) yielded the titanite solubility equation (adj. r 2 = 0.95): $\left( {TiO_{2} } \right)^{melt} \left( {wt.\% } \right) = 0.978 \times M + 0.0048 \times T\left( K \right){-}5.90$ . This model correctly predicted undersaturation in 95% of 2344 experiments from the LEPR database that did not have titanite or rutile. Application to natural rocks yields saturation temperatures that are similar to independent temperature estimates. Finally, this equation should be useful for constraining the temperatures of titanite-saturated melts, for determining whether titanite saturation in magmatic source regions is likely, and for determining when titanite can crystallize and begin to exert an influence on melt geochemistry.

58 GEOSCIENCES↗

DOE OSTI2020

Assessing Impacts of Plant Stoichiometric Traits on Terrestrial Ecosystem Carbon Accumulation Using the E3SM Land Model

Carbon (C) enters into the terrestrial ecosystems via photosynthesis and cycles through the system together with other essential nutrients (i.e., nitrogen [N] and phosphorus [P]). Such a strong coupling of C, N, and P leads to the theoretical prediction that limited nutrient availability will limit photosynthesis rate, plant growth, and future terrestrial C dynamics. However, the lack of reliable information about plant tissue stoichiometric constraints remains a challenge for quantifying nutrient limitations on projected global C cycling. In this study, we harmonized observed plant tissue C:N:P stoichiometry from more than 6,000 plant species with the commonly used plant functional type framework in global land models. Using observed C:N:P stoichiometry and the flexibility of these ratios as emergent plant traits, we show that observationally constrained fixed plant stoichiometry does not improve model estimates of present-day C dynamics compared with unconstrained stoichiometry. However, adopting stoichiometric flexibility significantly improves model predictions of C fluxes and stocks. The 21st century simulations with RCP8.5 CO 2 concentrations show that stoichiometric flexibility, rather than baseline stoichiometric ratios, is the dominant controller of plant productivity and ecosystem C accumulation in modeled responses to CO 2 fertilization. The enhanced nutrient limitations and plant P use efficiency mainly explain this result. This study is consistent with the previous consensus that nutrient availability will limit xfuture land carbon sequestration but challenges the idea that imbalances between C and nutrient supplies and fixed stoichiometry limit future land C sinks. We show here that it is necessary to represent nutrient stoichiometric flexibility in models to accurately project future terrestrial ecosystem carbon sequestration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Convective Updraft and Downdraft Characteristics of Continental Mesoscale Convective Systems in the Model Gray Zone

Here, the "gray zone" of convective modeling is defined as the range of horizontal grid spacings (Δx) at which turbulent transport processes are only partially resolved by the dynamics of the numerical model. This zone typically covers Δx from a few kilometers to several hundred meters, wherein the realistic representation of convective cloud processes can be challenging. This study characterizes the convective draft behaviors at multiple Δx across the gray zone and determines the appropriate Δx that can reliably capture these salient convective properties. We perform an ensemble of idealized simulations of mesoscale convective systems (MCS) using the Weather Research and Forecasting model at various Δx from 4 km to 250 m over the central U.S. An evaluation of key MCS kinematic properties is constrained using unique, long-term vertical velocity estimates obtained by radar wind profilers deployed by the Department of Energy Atmospheric Radiation Measurement user facility. MCS simulations for all Δx tested overestimate (underestimate) the probabilities of convective updrafts (downdrafts) compared to the observations. In terms of the convective draft intensity, finer-Δx models overestimate the updraft intensity, while the opposite is found for downdrafts. Moving from Δx = 4 km to 250 m, downdrafts become stronger and more frequent especially at middle and upper levels, attributed to additional drag from an increasing graupel frequency and compensation of enhanced updraft velocity. Simulated draft characteristics, including core size, intensity, and probability of occurrence, exhibit pronounced changes at Δx greater than 500 m, but suggest less sensitivity when Δx is reduced below 500 m.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

STRIDES: a 3.9 per cent measurement of the Hubble constant from the strong lens system DES J0408−5354

We present a blind time-delay cosmographic analysis for the lens system DES J0408−5354. This system is extraordinary for the presence of two sets of multiple images at different redshifts, which provide the opportunity to obtain more information at the cost of increased modelling complexity with respect to previously analysed systems. We perform detailed modelling of the mass distribution for this lens system using three band Hubble Space Telescope imaging. We combine the measured time delays, line-of-sight central velocity dispersion of the deflector, and statistically constrained external convergence with our lens models to estimate two cosmological distances. We measure the ‘effective’ time-delay distance corresponding to the redshifts of the deflector and the lensed quasar |$D_{\Delta t}^{\rm eff}=$||$3382_{-115}^{+146}$| Mpc and the angular diameter distance to the deflector D_d = |$1711_{-280}^{+376}$| Mpc, with covariance between the two distances. From these constraints on the cosmological distances, we infer the Hubble constant H_0= |$74.2_{-3.0}^{+2.7}$| km s^−1 Mpc^−^1 assuming a flat ΛCDM cosmology and a uniform prior for Ω_m as |$\Omega _{\rm m} \sim \mathcal {U}(0.05, 0.5)$|⁠. This measurement gives the most precise constraint on H_0 to date from a single lens. Our measurement is consistent with that obtained from the previous sample of six lenses analysed by the H_0 Lenses in COSMOGRAIL’s Wellspring (H0LiCOW) collaboration. It is also consistent with measurements of H_0 based on the local distance ladder, reinforcing the tension with the inference from early Universe probes, for example, with 2.2σ discrepancy from the cosmic microwave background measurement.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Impact of recent updates to neutrino oscillation parameters on the effective Majorana neutrino mass in 0 ν β β decay

We investigate how recent updates to neutrino oscillation parameters and the sum of neutrino masses influence the sensitivity of neutrinoless double-beta ( 0 ν β β ) decay experiments. Incorporating the latest cosmological constraints on the sum of neutrino masses and laboratory measurements on oscillations, we determine the sum of neutrino masses for both the normal hierarchy (NH) and the inverted hierarchy (IH). Our analysis reveals a narrow range for the sum of neutrino masses, approximately 0.06 eV / c 2 for NH and 0.102 eV / c 2 for IH. Utilizing these constraints, we calculate the effective Majorana masses for both NH and IH scenarios, establishing the corresponding allowed regions. Importantly, we find that the minimum neutrino mass is nonzero, as constrained by the current oscillation parameters. Additionally, we estimate the half-life of 0 ν β β decay using these effective Majorana masses for both NH and IH. Our results suggest that upcoming ton-scale experiments will comprehensively explore the IH scenario, while 100-ton-scale experiments will effectively probe the parameter space for the NH scenario, provided the background index can achieve 1 event/kton-year in the region of interest. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2022

Estimating the local dark matter density in a non-axisymmetric wobbling disc

The density of dark matter near the Sun, ρDM, ⊙, is important for experiments hunting for dark matter particles in the laboratory, and for constraining the local shape of the Milky Way’s dark matter halo. Estimates to date have typically assumed that the Milky Way’s stellar disc is axisymmetric and in a steady-state. Yet the Milky Way disc is neither, exhibiting prominent spiral arms and a bar, and vertical and radial oscillations. Here, we assess the impact of these assumptions on determinations of ρDM, ⊙ by applying a free-form, steady-state, Jeans method to two different N-body simulations of Milky Way-like galaxies. In one, the galaxy has experienced an ancient major merger, similar to the hypothesized Gaia–Sausage–Enceladus; in the other, the galaxy is perturbed more recently by the repeated passage and slow merger of a Sagittarius-like dwarf galaxy. We assess the impact of each of the terms in the Jeans–Poisson equations on our ability to correctly extract ρDM, ⊙ from the simulated data. We find that common approximations employed in the literature – axisymmetry and a locally flat rotation curve – can lead to significant systematic errors of up to a factor ~1.5 in the recovered surface mass density ~2 kpc above the disc plane, implying a fractional error on ρDM, ⊙ of the order of unity. However, once we add in the tilt term and the rotation curve term in our models, we obtain an unbiased estimate of ρDM, ⊙, consistent with the true value within our 95 percent confidence intervals for realistic 20 percent uncertainties on the baryonic surface density of the disc. Other terms – the axial tilt, 2nd Poisson and time-dependent terms – contribute less than 10 percent to ρDM, ⊙ (given current data) and can be safely neglected for now. In the future, as more data become available, these terms will need to be included in the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗