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We developed models to predict the photoactivity of organic molecules for photocatalytic hydrogen evolution by integrating experiment, computation, and machine learning. This marks a step toward the data-driven discovery of molecular photocatalysts.
The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.
The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.
Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.
Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.
A supermassive black hole (SMBH) ejected from the potential well of its host galaxy via gravitational wave recoil carries important information about the mass ratio and spin alignment of the pre-merger SMBH binary. Such a recoiling SMBH may be detectable as an active galactic nucleus (AGN) broad-line region offset by up to 10 kpc from a disturbed host galaxy. We describe a novel methodology using forward modeling with The Tractor to search for such offset AGNs in a sample of 5493 optically variable AGNs detected with the Zwicky Transient Facility (ZTF). We present the discovery of nine AGNs that may be spatially offset from their host galaxies and are candidates for recoiling SMBHs. Five of these offset AGNs exhibit double-peaked broad Balmer lines, which may have arisen from unobscured accretion disk emission, and four show radio emission indicative of a relativistic jet. The fraction of double-peaked emitters in our spatially offset AGN sample is significantly larger than the 16% double-peaked emitter fraction observed for ZTF AGNs overall. In our sample of variable AGNs we also identified 52 merging galaxies, including a new spectroscopically confirmed dual AGN. Finally, we detected the dramatic rebrightening of SDSS 1133, a previously discovered variable object and recoiling SMBH candidate, in ZTF. The flare was accompanied by the reemergence of strong P Cygni line features, indicating that SDSS 1133 may be an outbursting luminous blue variable star.
Some observational results are discussed which have a bearing on the problem of QSO redshifts. A general description of QSO absorption-line spectra is presented, and the spectra of four objects are reviewed. The hypothesis is advanced that production of absorption lines at many different redshifts is due to line-locking. Two QSOs with redshifts greater than 3 are described, and questions raised by the discovery of a double QSO with two different redshifts are discussed. Efforts to determine associations between QSOs and galaxies are outlined, and some questions are posed concerning physical models, effects of strong gravitational fields, and different types of QSOs.
Ultraviolet studies, originally undertaken to ascertain the state of the atmosphere of the K-supergiant component of the zeta Aurigae system, have been sidetracked by the discovery of significant accretion effects. An analysis of the phase dependence of the profiles of resonance lines in Mg II and C IV has led to a qualitative model of the wind flow from the K star. At the position of the B star the flow velocity is about 100 km/sec and the density is 0.000003 per cu cm, leading to a mass loss rate of 2 x 10 to the -8th solar masses per year. This wind interacts with the B star in a shock, which will be described, leading to accretion on the B star at a rate of 4 x 10 to the -10th solar masses per year.
The microbial fossil record encompasses a wide range of information, including cellular remains, stromatolites, biofabrics, trace fossils, biominerals and chemofossils. The preservation of fossils is strongly influenced by the physical, chemical and biological factors of the environment which, acting together, ultimately determine the types of information that will be captured and retained in the rock record. The critical factor in assessing the suitability of a site for a microbial fossil record is the paleoenvironment. The reconstruction of ancient sedimentary environments usually requires the integration of a wide variety of geological information, including the shape, geometry and internal structure of sedimentary deposits, their mineralogy, and geochemistry. For Mars, much of our knowledge about past environments is based on orbital imaging of geomorphic features. This evidence provides an important context and starting point for site selection. However, our knowledge of the martian surface is quite limited, and a major goal of the upcoming exploration effort is to reconstruct the history of Martian volatiles, climate, and hydrology as a context for the exploration for past or present life. Mineralogical mapping from orbit will be an important key in this effort. In exploring for evidence of past life, terrestrial experience suggests that the long-term preservation of biological information as fossils occurs under a fairly narrow range of geological conditions that are well known to paleontologists (1). In detrital sedimentary systems, microbial fossilization is favored by rapid burial in fine-grained, clay-rich sediments. In chemical sedimentary systems, preservation is enhanced by rapid entombment in fine-grained chemical precipitates. For long term preservation, host rocks must be composed of stable minerals that resist chemical weathering, and which form an impermeable matrix and closed chemical system that can protect biosignatures from alteration during subsequent diagenetic change or metamorphism. In this context, host rocks composed of highly ordered, chemically-stable mineral phases, like silica (forming cherts) or phosphate (forming phosphorites), are especially favored. Such lithologies tend to have very long crustal residence times and (along with carbonates and shales), are the most common host rocks for the Precambrian microfossil record on Earth. If we assume that a subsurface hydrosphere has been present throughout martian history, then life could have originated there at any time, perhaps emerging at the surface periodically when climate changes, induced by external forcing or endogenous processes (e.g. volcanism), allowed liquid water to exist at the surface. The recent discovery of subsurface chemolithoautotrophic organisms which are capable of synthesizing organic substrates from C02 and H2 liberated from the aqueous weathering of basalt, is especially. relevant as a model for martian life. While a subsurface habitable zone may yet exist on Mars, access to such environments will likely require drilling to depths of several kilometers. Given the technological challenge of deep drilling, this is unlikely to occur prior to human missions. So, even if there is extant life on Mars today in subsurface habitats, it may be much easier to find its fossil counterparts in ancient deposits exposed at the surface. In exploring for a fossil record in subsurface environments on Mars there are several geological situations that may provide access to the appropriate materials. These include 1) ejecta from impact craters, 2) talus slopes, debris flows or alluvial fans developed below the walls of deep canyons, and 3) the deposits of outflood channels. Examples of aqueous mineral deposits of formed in subsurface environments that could harbor a microbial fossil record include such things as cements in detrital sedimentary rocks, low temperature diagenetic minerals deposited in veins, or filling vesicles in volcanic rocks, and hydrothermal deposits formed below the upper temperature limit for life (about 160 degrees C). There are many sites within the present latitudinal constraints for the 2001 mission (15 deg S to 30 deg N) that meet these requirements. But the practical problem with these kinds of deposits is that they tend to be disseminated, making up only a small percentage of a host rock. Even with mineralogical information provided by the Thermal Emission Spectrometer (TES) presently in orbit around Mars, predicting their occurrence ahead of time may be quite difficult. The deposits of surficial aqueous sedimentary systems are likely to provide the largest targets for site selection in 2001. Of these, the deposits of hydrothermal systems (subaerial and subaqueous thermal springs) have been discussed previously. It is likely that hydrothermal systems were widespread on Mars early in its history and a number of common geo-tectonic settings on Mars are likely to have hosted hydrothermal activity. Most of these are represented within the latitudinal constraints presently identified for 2001. However, the deposits of surface spring systems are likely to be difficult to find as well. On Earth, exposure areas for hydrothermal spring mounds are typically a few square kms, less than a single TES pixel. But such deposits may be quite abundant within some volcanic terrains, It is estimated, for example, that between 15-20% of the floor of Yellowstone caldera is covered by thermal spring deposits. In such abundances, subaerial sinters could well be detected by TES. Where exposed, the shallow subsurface portions of these systems may be quite a lot larger (perhaps tens of square kms), although (as noted above) mineralization may be finely disseminated in the basement rock, making remote detection more difficult. Paleolake Basins. There are a large number of potential paleolake basins on Mars (inclusive of impact craters and volcanic calderas) that have been previously identified using Viking images. Most of these lie in the southern highlands beyond the l5 deg S constraint for 2001. However, deposits of paleolakes may offer the largest and most easily identified exopaleontological targets from orbit. Based on a variety of arguments, some workers have suggested that there was once an ancient ocean on the northern plains, and some sites of interest (potential shoreline terraces) fall within the 30 deg N constraint. From a paleontological standpoint the most interesting places of this type are terminal paleolake basins which are likely to have been both saline and alkaline. Models by Schaefer suggest such environments could be widespread on Mars. The conditions in terminal lake basin settings favor widespread chemical sedimentation, an important condition for microbial fossilization. Important lithological targets for a microbial fossil record in terminal lake basins include spring-deposited carbonates, shoreline cements, a wide variety of evaporite minerals and fine-grained detrital sediments including shales, marls, and water-lain volcanic ash deposits. In developing a strategy to explore for ancient hydrothermal deposits on Mars, we can learn from the methods that have been developed by explorationists to explore for economic mineral deposits on Earth. Due to their simple mineralogy, hydrothermal deposits can often be detected using remote sensing methods. Common thermal spring mineral assemblages include silica, carbonate, and various metallic oxides and sulfides. But there are also a number of diagnostic silicate minerals, including clays, formed by the hydrothermal alteration of country rocks. These hydrothermal minerals have characteristic spectral signatures that could be detected from Mars orbit using high resolution infrared remote sensing methods. In playa lake settings, evaporite deposits often form a predictable "bull's eye" pattern with carbonates being deposited in marginal basin areas, and sulfates and halides occurring progressively more basinward. The floors of some impact craters on Mars, such as "White Rock" and Bequeral Crater (see Oxia Palus NE, Site 148), have floor deposits that could be evaporites, inclusive of carbonates. Evaporite minerals possess characteristic spectral signatures in the infrared and could similarly be identified from Mars orbit using high resolution remote sensing methods. Clearly, utilization of TES data will be important for optimizing site selection for Exopaleontology, and every effort should be made to benefit from that data before a final decision is made.
Work System analysis and design is complex and non-deterministic. In this paper we describe Brahms, a multiagent modeling and simulation environment for designing complex interactions in human-machine systems. Brahms was originally conceived as a business process design tool that simulates work practices, including social systems of work. We describe our modeling and simulation method for mission operations work systems design, based on a research case study in which we used Brahms to design mission operations for a proposed discovery mission to the Moon. We then describe the results of an actual method application project-the Brahms Mars Exploration Rover. Space mission operations are similar to operations of traditional organizations; we show that the application of Brahms for space mission operations design is relevant and transferable to other types of business processes in organizations.
In this paper, we report the results of an X-ray monitoring campaign on the heavily obscured Seyfert galaxy, Markarian 3, carried out between the fall of 2014 and the spring of 2015 with NuSTAR, Suzaku and XMMNewton. The hard X-ray spectrum of Markarian 3 is variable on all the time-scales probed by our campaign, down to a few days. The observed continuum variability is due to an intrinsically variable primary continuum seen in transmission through a large, but still Compton-thin column density (N(sub H) approx. 0.8-1.1 x 10(exp 24)/sq cm). If arranged in a spherical-toroidal geometry, the Compton scattering matter has an opening angle approx. 66deg, and is seen at a grazing angle through its upper rim (inclination angle approx. 70deg). We report a possible occultation event during the 2014 campaign. If the torus is constituted by a system of clouds sharing the same column density, this event allows us to constrain their number (17 +/- 5) and individual column density, [approx. (4.9 +/- 1.5) x 10(exp 22)/ sq cm]. The comparison of IR and X-ray spectroscopic results with state-of-the art torus models suggests that at least two-thirds of the X-ray obscuring gas volume might be located within the dust sublimation radius. We report also the discovery of an ionized absorber, characterized by variable resonant absorption lines due to He- and H-like iron. This discovery lends support to the idea that moderate column density absorbers could be due to clouds evaporated at the outer surface of the torus, possibly accelerated by the radiation pressure due to the central AGN emission leaking through the patchy absorber.
At the NASA Goddard Earth Sciences Data and Information Services (GES DISC), we strive to simplify data discovery and data access to our wide range of global climate data, concentrated primarily in the areas of atmospheric composition, atmospheric dynamics, global precipitation, solar irradiance, and several modeling data sets related to land surface hydrology. To help meet user needs, we will demonstrate how you can use the GES DISC knowledge-base resources (HowTo's) and we also encourage community contributions.
This paper reviews the orbit determination performance for the last five years of the Cassini Mission Solstice Tour. During this period of time, Cassini had more than 30 satellite encounters, including Titan, Rhea, and Dione. We report on the navigational flyby accuracy, comparing post-flyby reconstructions and encounter predictions, and discuss the performance improvement and challenges over the years. Finally, we give an overview of the "Grand Finale" end of mission planned for 2017.The Cassini mission has been in orbit in the Saturnian system for more than 11 years, and hasreturned a wealth of discoveries and operational knowledge in the outer Solar System. In this paper, we reported on the last three years of navigation operations focusing on orbit determinationand encounter performance. Modeling and strategy changes over the past years now allow us tonavigate bodies at 100s of meters in accuracy, and consider a miss larger than 1 km an outlier.The Cassini Grand Finale will be spectacular - do not miss it!
Datasets, unlike publications, can be updated over time, with each new version receiving a DOI but not always being linked to previous ones. This complicates tracking citations across a dataset’s lifecycle. We address this by integrating dataset versions and citations into a knowledge graph (KG), which helps trace dataset citations and analyze dataset usage in applied research. To categorize publications from various journals, we fine-tuned NASA IMPACT INDUS Large Language Model (LLM) on a labeled publication set, assigning publications to one of twenty applied research areas. By linking datasets to these research areas, we improved dataset searchability and discovery through these domains.
For a class of macroscopic dark matter models, inelastic scattering of dark matter off a nucleus can generate electromagnetic signatures with GeV-scale energy. The IceCube detector, with its kilometer-scale size, is ideal for directly detecting such inelastic scattering. Based on the slow particle trigger for the DeepCore detector, we perform a detailed signal and background simulation to estimate the discovery potential. For order 1 GeV deposited energy in each interaction, we find that IceCube can probe the dark matter masses up to one gram.
In this work we study the asymptotic consistency of the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy) in the identification of differential equations from noisy samples of solutions. We prove that the WSINDy estimator is unconditionally asymptotically consistent for a wide class of models that includes the Navier–Stokes, Kuramoto–Sivashinsky and Sine–Gordon equations. We thus provide a mathematically rigorous explanation for the observed robustness to noise of weak-form equation learning. Conversely, we also show that, in general, the WSINDy estimator is only conditionally asymptotically consistent, yielding discovery of spurious terms with probability one if the noise level exceeds a critical threshold σ c . We provide explicit bounds on σ c in the case of Gaussian white noise and we explicitly characterize the spurious terms that arise in the case of trigonometric and/or polynomial libraries. Furthermore, we show that, if the data is suitably denoised (a simple moving average filter is sufficient), then asymptotic consistency is recovered for models with locally-Lipschitz, polynomial-growth nonlinearities. Our results reveal important aspects of weak-form equation learning, which may be used to improve future algorithms. We demonstrate our findings numerically using the Lorenz system, the cubic oscillator, a viscous Burgers-growth model and a Kuramoto–Sivashinsky-type high-order PDE.
We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.