Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data logging”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

BigPanDA monitoring system evolution in the ATLAS Experiment

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A resolved study of the inner regions of nearby galaxies with an excess of young massive stars: missing link in the AGN–starburst connection?

ABSTRACT We have selected galaxies with very high levels of H α emission (EQW(H α) >700 Å.) in their central regions from the final data release of the MaNGA survey. Our study focuses on 14 very well-resolved nearby galaxies with stellar masses in the range 9.5 < log M*/(M⊙) < 11.5. We investigate a variety of procedures for selecting galaxy regions that are likely to harbour excess populations of young massive stars, finding that selection in the 2D space of extinction-corrected H α EQW and [S iii]/[S ii] line ratio produces the best results. By comparing stacked spectra covering these regions with stacked spectra covering normal starburst regions with 100 Å<EQW(H α) <200 Å, we obtain the following main results: (1) Clear signatures of excess Wolf–Rayet stars are found in half of the H α excess regions, (2) galaxy regions containing excess Wolf–Rayet stars are more often associated with the presence of high-ionization emission lines characteristic of accreting black holes. Excess [Ne iii] is detected in 4 out of 8 of the WR regions and there are tentative [Fe x] detections in 2 galaxies. (3) Regions of the galaxy with excess Wolf–Rayet stars are located where the interstellar medium has larger ionized gas turbulent velocities and higher neutral gas overdensities. We make a first attempt to constrain changes in the high-mass end of the stellar initial mass function (IMF) using the HR-pyPopStar evolutionary population synthesis models that include high-wavelength-resolution theoretical atmosphere libraries for Wolf–Rayet stars.

Kauffmann, Guinevere↗
DOE OSTI2024

Collection And Analysis Of Telemetry For The Cyote Heuristic

CATCH CLI focuses on gathering telemetry data, storing it in the Neo4j database, querying for Mitre ATT&CK patterns, and creating STIX 2.1 reports. Key Components: Analysis Modules: Analyze data to detect attack patterns. GoSTOTS Collection Engines: Collect telemetry data. These tools can be used together or individually. Analysis modules rely on data from specific engines to identify attack patterns. Source Code Organization: Engines: CATCH/catch/cmd/collection Modules: CATCH/catch/cmd/analysis CGUI Overview CATCH Graphical User Interface (CGUI) offers a graphical shell to execute CATCH CLI, allowing easy editing of: Analysis Modules Database configurations Profiles (collection and device settings) Neo4j Overview Neo4j is a graph database using the Cypher query language, storing data in JSON. It seamlessly integrates with STIX 2.1 data for: Data Submission: CATCH Collection Engines Data Querying: Analysis Modules CATCH modifies STIX 2.1 data for Neo4j submission and reverts it back during querying. STIG Overview Structured Threat Intelligence Graph (STIG) is a tool for creating, editing, querying, analyzing, and visualizing threat intelligence using STIX 2.1 and storing data in Neo4j. Usage Tools can be run: Manually (CLI): Refer to CATCH documentation User Interface: Run ./cgui/CGUI or go run ./cgui/ Additional Information Logging System: Detailed in the config documentation Further Documentation: Available for CATCH and CGUI

Madsen, MichaelJ. [Idaho National Laboratory (INL)↗
DOE OSTI2023

Building an EPA Class VI Permit Application

Summary To accelerate the commercialization of carbon capture and storage (CCS), the US Department of Energy (US DOE) is building on decades of characterization efforts and pilot-scale projects through their CarbonSAFE program. Administered through their National Energy Technology Laboratory, this program seeks to bring fully integrated projects to the sector that can store more than 50 million tonnes of CO2 over a 30-year period. The program, which was enacted before the enhancement of Internal Revenue Code Section 45Q, is in the capture assessment, characterization, and permitting phase. The objectives of this paper are to discuss (a) the injection permitting requirements of the CarbonSAFE projects; (b) information gathering in support of the permit; (c) the timelines of field development and permit-related activities; (d) the major technical components of the field development plan; and (e) early feedback from the regulators toward acceptance of the permit. In Mississippi, more than 30,000 acres have been characterized by six deep characterization wells, a deep groundwater well, and 92 line miles of 2D seismic as part of the CarbonSAFE Project ECO2S. During the acquisition of seismic data, all receiver lines were live, which resulted in the generation of a pseudo-3D seismic design. The incorporation of a 3D seismic survey was not included as part of this project due to logistical difficulties presented by the undulating, wooded surface terrain. A suite of openhole geophysical logs was taken from each well, allowing for a detailed interpretation of prospective storage reservoirs and confining intervals to complement the analysis carried out on the 290 ft of a whole core that was cut through the prospective confining zone and storage reservoir. The detailed geologic and reservoir data were assembled and entered into a 3D model to assess the injection capacity and the area of review (AoR). This information fed into the detailed corrective action, monitoring, testing, and postinjection site care (PISC) modeling. The results have been exceptional. The geologic assessment has revealed three primary storage targets, ranging in depth from 3,500 ft to 6,000 ft. These storage reservoirs net 1,300 ft of sandstone, with mean porosity and permeability of 29% and 3.6 darcies, respectively. Together, these reservoirs have storage capacities that may exceed 20 million tonnes per square mile, making this a gigatonne prospect. Forward modeling of the project resulted in an AoR of 16 sq miles, injecting about 8000 t/d, for 30 years, via two deep injection wells. The excellent confining characteristics of the caprock, relatively simple geologic structure, and lack of historical well drilling activity in this area provide excellent containment of the injected CO2. Based on this work, the project has proposed 20 years of PISC. To date, only two US CO2 injection permits have been granted. These projects relied on a singular capture point feeding a singular sequestration point (source to sink), and considerations have not been made to garner CO2 emissions from other industrial sources. The Kemper County Storage Complex is a first-of-its-kind storage hub concept that looks to develop an area capable of storing significant quantities of CO2 from the region. Also, this work will show how characterization efforts, geological and numerical modeling efforts, and plan development were constructed in support of permit and incentives acceptance.

Energy & Fuels↗
DOE OSTI2026

The Stability of Nd Hydroxyl Complexes at Near Neutral to Alkaline pH from 25 to 75 °C: Implications for Monazite Solubility in Hydrothermal Aqueous Fluids

Speciation of rare earth elements (REE) in aqueous fluids controls their mobilization during fluid-rock interaction. Thermodynamic modeling provides important insights into the factors controlling REE mobility and fractionation in aqueous fluids, in which aqueous REE hydroxyl complexes are particularly important across a broad pH range. However, the stability of these REE hydroxyl complexes remains poorly determined due to limited experimental data above ambient temperature. In this work, UV–vis spectrophotometry experiments were conducted to determine the cumulative hydrolysis constants for neodymium (Nd) from 25 to 75 °C in near-neutral to alkaline solutions. The color indicator m -cresol purple was used to determine in situ pH. Alkaline NaOH-bearing aqueous solutions were doped with varying initial Nd concentrations (0 to ∼0.155 mmol/kg), which resulted in the release of protons (H + ) and pH decrease during hydrolysis of Nd 3+ according to Nd 3+ + n H 2 O = Nd(OH) n 3– n + H + . The average OH – ligand number coordinated to Nd 3+ was found to increase from 1.0 to 1.6 at 25 °C and from 1.2 to 2.8 at 75 °C over a pH range from 6.3 to 9.0. The measured speciation shows an increased predominance of Nd(OH) 3 0 over the Nd(OH) 2 + and Nd(OH) 2+ species with increased temperature and pH. The increasing cumulative formation constants (log*β° n , n = 1 to 3) retrieved from 25 to 75 °C differ by 0.1 to 1.9 logarithmic units in comparison to existing thermodynamic databases. These updated thermodynamic data have important implications for geochemical modeling of the speciation of Nd hydroxyl complexes and the solubility of monazite as a function of pH.

58 GEOSCIENCES↗
DOE OSTI2022

Effects of structure and volcanic stratigraphy on groundwater and surface water flow: Hat Creek basin, California, USA

Abstract Hydrogeologic systems in the southern Cascade Range in California (USA) develop in volcanic rocks where morphology, stratigraphy, extensional structures, and attendant basin geometry play a central role in groundwater flow paths, groundwater/surface-water interactions, and spring discharge locations. High-volume springs (greater than 3 m 3 /s) flow from basin-filling (<800 ka) volcanic rocks in the Hat Creek and Fall River tributaries and contribute approximately half of the average annual flow of the Pit River, the largest tributary to Shasta Lake. A hydrogeologic conceptual framework is constructed for the Hat Creek basin combining new geologic mapping, water-well lithologic logs, a database of active faults, LiDAR mapping of faults and volcanic landforms, streamflow measurements and airborne thermal infrared remote sensing of stream temperature. These data are used to integrate the geologic structure and the volcanic and volcaniclastic stratigraphy to create a three-dimensional interpretation of the hydrogeology in the basin. Two large streamflow gains from focused groundwater discharge near Big Spring and north of Sugarloaf Peak result from geologic barriers that restrict lateral groundwater flow and force water into Hat Creek. The inferred groundwater-flow barriers divide the aquifer system into at least three leaky compartments. The two downstream compartments lose streamflow in the upstream reaches (immediately downstream of the groundwater-flow barriers) and gain in downstream reaches with the greatest inflows immediately upstream of the barriers.

Geology↗
DOE OSTI2025

Current interrupt method for calculating the electrochemical impedance in a solid oxide electrolysis stack

Here, in this work the time domain response of Solid Oxide Electrolysis Cells (SOEC) to a current interruption was transformed into the frequency domain using a carrier function Laplace transform, which is fit to the experimental data using a MATLAB Complex Nonlinear Least Squares (CNLS) solver. The hardware implementation, consisting principally of a high-speed switch and a fast-logging Analog to Digital Converter (ADC), was assembled and tested using a calibration module to assess the accuracy, repeatability, and speed of acquisition of the prototype device as compared against a calibrated commercial impedance spectrometer. Additionally, the current interrupt device and commercial FRA were used to acquire the impedance spectra of a four cell SOEC stack with a large, 300 cm 2 , active cell area.

SOEC↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2025

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detection and characterization of detached tidal dwarf galaxies

Tidal interactions between galaxies often give rise to tidal tails, which can harbor concentrations of stars and interstellar gas resembling dwarf galaxies. Some of these tidal dwarf galaxies (TDGs) have the potential to detach from their parent galaxies and become independent entities, but their long-term survival is uncertain. In this study, we conducted a search for detached TDGs associated with a sample of 39 interacting galaxy pairs in the local Universe using infrared, ultraviolet, and optical images. We employed IR colors and UV/optical/IR spectral energy distributions to identify potential interlopers, such as foreground stars or background quasars. Through spectroscopic observations using the Boller and Chivens spectrograph at San Pedro Mártir Observatory, we confirmed that six candidate TDGs are at the same redshift as their putative parent galaxy pairs. We identified and measured emission lines in the optical spectra and calculated nebular oxygen abundances, which range from log(O/H) = 8.10 ± 0.01 to 8.51 ± 0.02. We have serendipitously discovered an additional detached TDG candidate in Arp72 using available spectra from SDSS. Utilizing the photometric data and the CIGALE code for stellar population and dust emission fitting, we derived the stellar masses, stellar population ages, and stellar metallicities for these detached TDGs. Compared to standard mass-metallicity relations for dwarf galaxies, five of the seven candidates have higher than expected metallicities, confirming their tidal origins. One of the seven candidates remains unclear due to large uncertainties in metallicity, and another has stellar and nebular metallicities compatible with those of a preexisting dwarf galaxy. The latter object is relatively compact in the optical relative to its stellar mass, in contrast to the other candidate TDGs, which have large diameters for their stellar masses compared to most dwarf galaxies. The derived stellar population ages range from 100 Myr to 900 Myr, while the inferred stellar masses are between 2 × 10 6 M ⊙ and 8 × 10 7 M ⊙ . Four of the six TDGs are associated with the gas-rich M51-like pair Arp 72, one TDG is associated with a second M51-like pair Arp 86, and another is associated with Arp 65, an approximately equal mass pair. In spite of the relatively low stellar masses of these TDGs, they have survived for at least 100–900 Myrs, suggesting that they are stable and in dynamical equilibrium. We conclude that encounters with a relatively low-mass companion (1/10th–1/4th of the mass of the primary) can also produce long-lasting TDGs.

Astronomy & Astrophysics↗
DOE OSTI2026

DESI DR2 Galaxy luminosity functions

We present galaxy luminosity functions (LFs) for the Dark Energy Spectroscopic Instrument (DESI) DR2 Bright Galaxy Survey (BGS) in the g, r, z, and $w1$ bands over $0.002\lt z\lt 0.6$. Our analysis uses updated k-corrections and evolutionary corrections, including new polynomial kcorrection fits derived from BGS Year 1 data that supersede earlier GAMA-based prescriptions. Exploiting the statistical power of DESI, we measure LFs to very faint magnitudes, reaching $^{0.1}M_r-5\log h\sim -10$. Independent measurements from the North and South survey regions show excellent agreement around the LF knee, but the very small statistical uncertainties reveal that simple analytic forms fail to capture the full LF shape. The bright end departs from a pure exponential decline, while the faint end exhibits complex, non-powerlaw behaviour, including a pronounced upturn at $^{0.1}M_r-5\log h\gtrsim -15$, which is stronger for red galaxies than for blue. We show that our LFs are largely complete for galaxies with surface brightness $\mu _{50}\lt 25$, and that an apparent steepening fainter than $-13$ is driven primarily by local overdensity and fragmentation of large galaxies. A systematic North–South offset at the brightest magnitudes is traced to red galaxies and may reflect shallower North photometry underestimating extended earlytype profiles, although this remains inconclusive. We therefore also provide LFs based on model Petrosian magnitudes. Redshift splitting reveals small but significant residuals, indicating limitations of a simple global evolutionary model. Using the redshift limits of J. Loveday et al. (2012), we find excellent agreement with GAMA, with substantially reduced statistical errors. These measurements provide a precise reference for studies of environmental and population-dependent LFs and for testing galaxy formation models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗
DOE OSTI2020

Logic in Memory Emulator

Logic in Memory Emulator (LiME) is a hardware/software tool specially designed for memory system evaluation and experiment. Emerging memories display a wide range of bandwidths, latencies, and capacities, making it challenging for the computer architect to navigate the design space of potential memory configurations, and for the application developer to assess performance implications of using such memories. With the LiME framework, architectural ideas can be prototyped in great detail yet with sufficient performance to support realistic evaluation on long running applications. LiME consists of two fundamental components: 1) the hardware and OS infrastructure for the emulator, and 2) a suite of benchmark applications to assist in characterizing the performance of current and future computer architectures. Some of the applications have been collected from other open source projects. Uses: Logging, replay and analysis of an application's memory behavior Evaluate impact of emerging memory technology on application performance. Emulate complex memory interactions in whole applications orders of magnitude faster than software simulation. Emulate acceleration hardware co-located with the memory subsystem. Features: Capture and log external memory accesses to a separate off-chip memory device without affecting application execution. Memory traces include the address, length, timestamp, and optionally the data for each transaction. Captured trace data can be saved to an SD card for off-line analysis. Configure a wide range of memory latencies in sub-nanosecond increments that encompass highbandwidth and storage class memories. Specify regions of interest (ROI) in applications to reduce the amount of trace data captured for analysis. Currently supports execution on Xilinx Zynq SoC which integrates an ARM processor with FPGA logic on a single device. Applications can be run under Linux or in bare metal mode on the ARM cores.

Jain, AbhishekK↗
DOE OSTI2020

Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multi-Scale Modeling Framework for Mercury Biogeochemistry

Multi-Scale modeling of mercury (Hg) geochemical speciation and reactions has been performed by integrating atomistic quantum chemical calculations with continuum scale speciation models. Major progress has been made in the improvement of quantum chemical models to calculate critical thermodynamic data for Hg complexes in aquatic environments. Rapid and reliable quantum chemical approaches have been developed for calculating acid dissociation constants (pK a ) and stability constants (log K), with calculated mean unsigned errors of 0.5 and 1.5 log units, respectively for ligand molecules and Hg complexes. At the continuum scale, systematic analysis of uncertainty propagation in mercury (Hg) speciation modeling has been conducted and was used to identify environmental conditions under which thermodynamic constant uncertainties are significant and recommended to be accounted for. The integrated framework for multi-scale modeling of mercury geochemistry is open to the research community through the web-based multiscale modeling aqueous speciation resource, AQUA-MER. The improved quantum chemical approaches for thermodynamic constant calculations are accessible through AQUA-MER and can be used to provide the missing constants in the continuum scale speciation calculations. In addition to low molecular mass Hg complex speciation, modeling natural aquatic environments also involve the transport of high molecular weight dissolved organic matter (DOM) in reactive flows simultaneously with equilibrium and kinetic reactions. To this end, atomistic MD simulations were performed to capture the details of aggregation, mechanisms and distribution of functional groups in DOM at the molecular level. The elemental composition and calculated bulk properties of the DOM models are in close agreement with experimental measurements. A travel-time based reactive transport model in the hyporheic zone of stream corridors was established for the multicomponent Hg-DOM-S system and implemented through PFLOTRAN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A DECADE of dwarfs: first detection of weak lensing around spectroscopically confirmed low-mass galaxies

We present the first detection of weak gravitational lensing around spectroscopically confirmed dwarf galaxies, using the large overlap between DESI DR1 spectroscopic data and DECADE/DES weak lensing catalogs. A clean dwarf galaxy sample with well-defined redshift and stellar mass cuts enables excess surface mass density measurements in two stellar mass bins ($\log \rm{M}_*=[8.2, 9.2]~M_\odot$ and $\log \rm{M}_*=[9.2, 10.2]~M_\odot$), with signal-to-noise ratios of $5.6$ and $12.4$ respectively. This signal-to-noise drops to $4.5$ and $9.2$ respectively for measurements without applying individual inverse probability (IIP) weights, which mitigates fiber incompleteness from DESI's targeting. The measurements are robust against variations in stellar mass estimates, photometric shredding, and lensing calibration systematics. Using a simulation-based modeling framework with stellar mass function priors, we constrain the stellar mass-halo mass relation and find a satellite fraction of $\simeq 0.3$, which is higher than previous photometric studies but $1.5σ$ lower than $Λ$CDM predictions. We find that IIP weights have a significant impact on lensing measurements and can change the inferred $f_{\rm{sat}}$ by a factor of two, highlighting the need for accurate fiber incompleteness corrections for dwarf galaxy samples. Our results open a new observational window into the galaxy-halo connection at low masses, showing that future massively multiplexed spectroscopic observations and weak lensing data will enable stringent tests of galaxy formation models and $Λ$CDM predictions.

To, Chun-Hao [Chicago U., Astron. Astrophys. Ctr.;↗