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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science

Abstract The Vera C. Rubin Observatory is expected to start the Legacy Survey of Space and Time (LSST) in early to mid-2025. This multiband wide-field synoptic survey will transform our view of the solar system, with the discovery and monitoring of over five million small bodies. The final survey strategy chosen for LSST has direct implications on the discoverability and characterization of solar system minor planets and passing interstellar objects. Creating an inventory of the solar system is one of the four main LSST science drivers. The LSST observing cadence is a complex optimization problem that must balance the priorities and needs of all the key LSST science areas. To design the best LSST survey strategy, a series of operation simulations using the Rubin Observatory scheduler have been generated to explore the various options for tuning observing parameters and prioritizations. We explore the impact of the various simulated LSST observing strategies on studying the solar system’s small body reservoirs. We examine what are the best observing scenarios and review what are the important considerations for maximizing LSST solar system science. In general, most of the LSST cadence simulations produce ±5% or less variations in our chosen key metrics, but a subset of the simulations significantly hinder science returns with much larger losses in the discovery and light-curve metrics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Causal machine learning uncovers conditions for convective intensification driven by organic and sulfate aerosols

Aerosols are often hypothesized to invigorate deep convective clouds (DCCs), but observational evidence remains limited and inconclusive. Clarifying this hypothesis is critical for regions vulnerable to thunderstorms and flooding, particularly highly polluted coastal cities. Leveraging a novel causal discovery–inference pipeline and high-resolution observations near Houston, TX, we identify multiple causal pathways among aerosols (mostly organic and sulfate), DCCs, and meteorological factors. However, a direct causal link from aerosols to DCCs is found to be uncommon, occurring in less than 35% of analyzed scenarios, and is characterized by strong conditionality and nonlinearity. When aerosol impacts on DCCs do occur, they can be substantial, enhancing DCC core heights by approximately 1.7 km, with 92% of this effect concentrated in warmer-phase cloud regions. Notably, the presence of sea breezes and the inclusion of all measured aerosol particles each enhance DCCs in over 95% of aerosol-sensitive cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Electric dipole moments in a leptoquark scenario for the B-physics anomalies

Vector leptoquarks can address the lepton flavor universality anomalies in decays associated with the b → cℓν and b → sℓℓ transitions, as observed in recent years. While not required to explain the anomalies, these leptoquarks generically yield new sources of CP violation. In this paper, we explore constraints and discovery potential for electric dipole moments (EDMs) in leptonic and hadronic systems. We provide the most generic expressions for dipole moments induced by vector leptoquarks at one loop. We find that O(1) CP-violating phases in tau and muon couplings can lead to corresponding EDMs within reach of next-generation EDM experiments, and that existing bounds on the electron EDM already put stringent constraints on CP-violating electron couplings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

TOI-1634 b: An Ultra-short-period Keystone Planet Sitting inside the M-dwarf Radius Valley

Studies of close-in planets orbiting M dwarfs have suggested that the M-dwarf radius valley may be well explained by distinct formation timescales between enveloped terrestrials and rocky planets that form at late times in a gas-depleted environment. This scenario is at odds with the picture that close-in rocky planets form with a primordial gaseous envelope that is subsequently stripped away by some thermally driven mass-loss process. These two physical scenarios make unique predictions of the rocky/enveloped transition’s dependence on orbital separation such that studying the compositions of planets within the M-dwarf radius valley may be able to establish the dominant physics. Here, we present the discovery of one such keystone planet: the ultra-short-period planet TOI-1634 b (P = 0.989 days, F=121F{sub ⊕}, r{sub p}=1.790{sub -0.081}{sup +0.080} R {sub ⊕}) orbiting a nearby M2 dwarf (K {sub s} = 8.7, R {sub s} = 0.450 R {sub ⊙}, M {sub s} = 0.502 M {sub ⊙}) and whose size and orbital period sit within the M-dwarf radius valley. We confirm the TESS-discovered planet candidate using extensive ground-based follow-up campaigns, including a set of 32 precise radial velocity measurements from HARPS-N. We measure a planetary mass of 4.91{sub -0.70}{sup +0.68} M {sub ⊕}, which makes TOI-1634 b inconsistent with an Earth-like composition at 5.9σ and thus requires either an extended gaseous envelope, a large volatile-rich layer, or a rocky composition that is not dominated by iron and silicates to explain its mass and radius. The discovery that the bulk composition of TOI-1634 b is inconsistent with that of Earth supports the gas-depleted formation mechanism to explain the emergence of the radius valley around M dwarfs with M{sub s}≲0.5 M {sub ⊙}.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mining G.O.L.D. (Geothermal Opportunities Leveraged Through Data): Exploring Synergies Between the Geothermal and Mining Industries

This report analyzes potential collaborations between the geothermal and locatable mineral industries (focused on the portion of the Basin and Range Province within Nevada, United States of America). The objectives of this study included analyzing: 1. The type and quality of data collected by the locatable mineral industry to determine feasibility for geothermal resource exploration; 2. The regulatory pathways and potential barriers that could prevent development of geothermal resources discovered via a mining claim (and vice versa) in the United States; 3. The historical development of geothermal resources discovered via mineral exploration data in the United States and illustrations of co-located mining and geothermal power projects; 4. The value propositions for both the locatable mineral and the geothermal industries to collaborate. This article concludes that many of the data collected by the mining industry as part of locatable mineral exploration (e.g., copper, gold, lithium) would also be useful for identifying and developing previously unknown geothermal resources (and in some notable cases, already have led to geothermal resource development). In addition, for minimal costs, the mining industry could catalogue these data and potentially monetize the data itself or use the data in the future to develop a geothermal project. Leveraging these locatable mineral data to develop geothermal resources and/or co-located minerals and geothermal resources would represent significant cost savings when compared to developing geothermal resources under a business-as-usual scenario as well as compared to current generating technologies (e.g., diesel-powered generators) employed at remote mining operations. Ultimately, leveraging mining industry data, knowledge, and expertise serves to effectively expand the geothermal exploration workforce, increase the rate of geothermal resource discovery, and potentially reduce geothermal electricity's levelized cost of energy (LCOE) by 23%-29%.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Precision unification and the scale of supersymmetry

In this paper, we study the implications of precise gauge coupling unification on supersymmetric particle masses. We argue that precise unification favors the superpartner masses that are in the range of several TeV and well beyond. We demonstrate this in the minimal supersymmetric theory with a common sparticle mass threshold, and two simple high-scale scenarios; minimal supergravity and minimal anomaly-mediated supersymmetry. We also identify candidate models with a Higgsino or a wino dark matter candidate. Finally, the analysis shows unambiguously that unless one takes foggy naturalness notions too seriously, the lack of direct superpartner discoveries at the LHC has not diminished the viability of supersymmetric unified theories in general nor even precision unification in particular. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Complementary signals of lepton flavor violation at a high-energy muon collider

A muon collider would be a powerful probe of flavor violation in new physics. There is a strong complementary case for collider measurements and precision low-energy probes of lepton flavor violation (as well as CP violation). We illustrate this by studying the collider reach in a supersymmetric scenario with flavor-violating slepton mixing. We find that the collider could discover sleptons and measure the slepton and neutralino masses with high precision, enabling event reconstruction that could cleanly separate flavor-violating new physics signals from Standard Model backgrounds. The discovery reach of a high-energy muon collider would cover a comparably large, and overlapping, range of parameter space to future μ → e conversion and electron EDM experiments, and unlike precision experiments could immediately shed light on the nature of new physics responsible for flavor violation. This complementarity strengthens the case that a muon collider could be an ideal energy-frontier laboratory in the search for physics beyond the Standard Model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Pair production of charged IDM scalars at high energy CLIC

The Compact Linear Collider (CLIC) was proposed as the next energy-frontier infrastructure at CERN, to study e ^+ + e ^- − collisions at three centre-of-mass energy stages: 380,GeV, 1.5,TeV and 3,TeV. The main goal of its high-energy stages is to search for the new physics beyond the Standard Model (SM). The Inert Doublet Model (IDM) is one of the simplest SM extensions and introduces four new scalar particles: H ^\pm ± , A and H; the lightest, H, is stable and hence a natural dark matter (DM) candidate. A set of benchmark points is considered, which are consistent with current theoretical and experimental constraints and promise detectable signals at future colliders. Prospects for observing pair-production of the IDM scalars at CLIC were previously studied using signatures with two leptons in the final state. In the current study, discovery reach for the IDM charged scalar pair-production is considered for the semi-leptonic final state at the two high-energy CLIC stages. Full simulation analysis, based on the current CLIC detector model, is presented for five selected IDM scenarios. Results are then extended to the larger set of benchmarks using the Delphes fast simulation framework. The CLIC detector model for Delphes has been modified to take pile-up contribution from the beam-induced \gamma\gamma γ γ interactions into account, which is crucial for the presented analysis. Results of the study indicate that heavy, charged IDM scalars can be discovered at CLIC for most of the proposed benchmark scenarios, with very high statistical significance.

Klamka, Jan↗
DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗
DOE OSTI2020

Future material demand for automotive lithium-based batteries

The world is shifting to electric vehicles to mitigate climate change. Here, we quantify the future demand for key battery materials, considering potential electric vehicle fleet and battery chemistry developments as well as second-use and recycling of electric vehicle batteries. We find that in a lithium nickel cobalt manganese oxide dominated battery scenario, demand is estimated to increase by factors of 18–20 for lithium, 17–19 for cobalt, 28–31 for nickel, and 15–20 for most other materials from 2020 to 2050, requiring a drastic expansion of lithium, cobalt, and nickel supply chains and likely additional resource discovery. However, uncertainties are large. Key factors are the development of the electric vehicles fleet and battery capacity requirements per vehicle. If other battery chemistries were used at large scale, e.g. lithium iron phosphate or novel lithium-sulphur or lithium-air batteries, the demand for cobalt and nickel would be substantially smaller. Closed-loop recycling plays a minor, but increasingly important role for reducing primary material demand until 2050, however, advances in recycling are necessary to economically recover battery-grade materials from end-of-life batteries. Second-use of electric vehicles batteries further delays recycling potentials.

25 ENERGY STORAGE↗
DOE OSTI2025

E-Area Low-Level Waste Facility Inadvertent Human Intruder Limits and Doses in Support of the PA2022

This report documents the inadvertent human intruder (IHI) analysis for the E-Area Low-Level Waste Facility (ELLWF) at the Savannah River Site (SRS), near Aiken, South Carolina. This analysis supports the revised ELLWF Performance Assessment (PA), complying with the Department of Energy standard for operation of low-level waste disposal facilities (USDOE, 2017). The ELLWF is an operating waste disposal facility and is scheduled to continue accepting waste to 2065. One task of the revised PA is to establish waste inventory limits for the various disposal units at ELLWF. This is done by modeling future contaminant release and transport through applicable pathways to human receptors, comparing predicted doses per disposed curie with applicable performance measures, to obtain inventory limits which will assure that doses to receptors do not exceed performance measures. This report documents results of modeling future doses to one class of receptor, the inadvertent human intruder. It is assumed that after site closure, public knowledge of the site is lost, and IHIs will engage in activities on the ELLWF that will disrupt the closure cap, causing dose to the IHI. Following USDOE (2017), six different stylized exposure scenarios are considered, simulating activities by an IHI which could result in a radiological dose. The six scenarios are: • Acute – Basement Construction: IHI constructs a basement and encounters waste during excavation which is inadvertently mixed with clean soil and diluted. • Acute – Well Drilling: IHI drills a water well through waste and is exposed to drill cuttings mixed with clean soil that are brought to the surface. • Acute – Discovery: IHI begins constructing a basement but stops when encountering the riprap in the final closure cap and is exposed to photon radiation from unexcavated material residing in the undisturbed waste zone. • Chronic – Agriculture: Resident IHI is exposed to waste that was excavated for basement construction and mixed with native soil in the intruder’s vegetable garden. • Chronic – Post-Drilling: Resident IHI is exposed to waste from drill cuttings mixed with native soil and scattered in the garden area. • Chronic – Residential: Resident IHI is exposed to external radiation while in home located above waste with shielding provided by the concrete basement floor and any soil or engineered material remaining between the basement and waste. Dose calculations are performed using the SRNL Dose Toolkit (Aleman, 2023), following the approach of Smith et al (2019). Calculations are performed separately for 27 of the 33 disposal units (DUs) at ELLWF and are radionuclide specific. The results of the IHI analysis include: • Dose Factors: mrem per disposed curie (acute) and mrem/yr per disposed curie (chronic) for each parent radionuclide, for each DU. • Inventory Limits: in curies, for each parent radionuclide, for each DU. • Estimated Dose to IHI: mrem (acute) and mrem/yr (chronic), for each DU, given its projected closure inventory without inventory biases applied. Most DU-specific IHI inventory limits are in the range of 10 3 to 10 7 curies per nuclide. The lowest inventory limits are associated with gamma-emitters such as Sn-126, Ra-226, Th-232, and Cm-248. Radionuclides with short half-lives such as Pu-241, and nuclides which are pure beta emitters or which decay by electron capture, such as Ni-59 and Ni-63, have the highest limits. For the 27 evaluated DUs, predicted IHI doses are shown in Table ES-1. The maximum acute dose is 1.18 mrem, at ST23, much less than the DOE performance measure of 500 mrem (USDOE, 2017). The highest chronic dose is 37.2 mrem/yr at ST02, below the DOE performance measure of 100 mrem/yr. Also shown are estimated inventory sums of fractions (SOFs) at closure in 2065, for groundwater (GW) and IHI pathways. For each DU, the inventory is constrained by the GW pathway. For most DUs, the IHI SOFs are approximately 1000 times lower than the GW SOF values, and the IHI pathway does not drive risk for any disposal unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Kinematic Evidence for Bipolar Ejecta Flows in the Galactic Supernova Remnant W49B

W49B is a unique Galactic supernova remnant with centrally peaked, “bar”-like ejecta distribution, which was once considered evidence for a hypernova origin that resulted in a bipolar ejection of the stellar core. However, chemical abundance measurements contradict this interpretation. Closely connected to the morphology of the ejecta is its velocity distribution, which provides critical details for understanding the explosion mechanism. We report the first ever observational constraint on the kinematics of the ejecta in W49B using the Resolve microcalorimeter spectrometer on the X-ray Imaging and Spectroscopy Mission (XRISM). Using XRISM/Resolve, we measured the line-of-sight velocity traced by the Fe Heα emission, which is the brightest feature in the Resolve spectrum, to vary by ±300 km s −1 with a smooth east-to-west gradient of a few tens of kilometers per second per parsec along the major axis. Similar trends in the line-of-sight velocity structure were found for other Fe-group elements Cr and Mn, traced by the Heα emission, and also for intermediate-mass elements Si, S, Ar, and Ca, traced by the Lyα emission. The discovery of the east–west gradient in the line-of-sight velocity, together with the absence of a twin-peaked line profile or enhanced broadening in the central region, clearly rejects the equatorially expanding disk model. In contrast, the observed velocity structure suggests bipolar flows reminiscent of a bipolar explosion scenario. An alternative scenario would be a collimation of the ejecta by an elongated cavity sculpted by bipolar stellar winds.

High resolution spectroscopy↗
DOE OSTI2021

Searching for leptoquarks at future muon colliders

A high energy muon collider can provide new and complementary discovery potential to the LHC or future hadron colliders. Leptoquarks are a motivated class of exotic new physics models, with distinct production channels at hadron and lepton machines. We study a vector leptoquark model at a muon collider with √s = 3, 14 TeV within a set of both UV and phenomenologically motivated flavor scenarios. We compute which production mechanism has the greatest reach for various values of the leptoquark mass and the coupling between leptoquark and Standard Model fermions. We find that we can probe leptoquark masses up to an order of magnitude beyond √s with perturbative couplings. Additionally, we can also probe regions of parameter space unavailable to flavor experiments. In particular, all of the parameter space of interest to explain recent low-energy anomalies in B meson decays would be covered even by a √s = 3 TeV collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Fork Experiments in the Hot Cell Using Spent Fuel Rods for International Nuclear Safeguards

This work leveraged the rare availability of 25 full-length pressurized water reactor spent fuel rods and 1 irradiated mixed-oxide rod at an Oak Ridge National Laboratory hot cell. This was done to collect measurement data with two Fork detectors to assess the detectors’ capability of verifying operator declaration data and detecting partial defects in spent fuel, which are the two primary goals of international safeguards on spent nuclear fuel. The data can also be used to benchmark the ORIGEN module, which has been adopted in the International Atomic Energy Agency’s (IAEA’s)/European Atomic Energy Community’s (Euratom’s) Integrated Review and Analysis Program to predict the Fork detector count rates in real time. In this project, the authors first calibrated two Fork detectors—a standard one and a modified one—by using known strong neutron and gamma sources. Then, the authors measured all 26 fuel rods at multiple locations along the length. The fuel rods were then assembled into three arrays—2 × 2, 3 × 3, and 5 × 5—by using specially designed support grids to mimic fuel assemblies and measure the arrays with both detectors. For the 5 × 5 array, 4 and 8 fuel rods of the array were replaced in two separate cases with short stainless-steel rods to mimic two partial defect scenarios, and the arrays were measured before and after the replacements. Polyethylene blocks were used in this experiment to mimic water. The results show that the Fork detectors were able to verify operator declarations and detect partial defects in spent fuel, and the authors were the first to demonstrate this through experiments. A discovery was also made that determined the root cause of the nonlinear response to gamma dose in the ion chambers used in IAEA and Euratom’s Fork detectors. After the experiments, both detectors were retrieved from the hot cell for future use. The data collected in this project will be used in a parallel International Nuclear Safeguards Engagement Program (INSEP) project to enhance the safeguards in the Finnish spent fuel encapsulation plant, and the data will be useful to other projects in the future given the increased safeguards needs due to spent fuel transfer and disposal activities worldwide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Dark matter discovery potential of the Advanced Particle-Astrophysics Telescope

Gamma-ray observations of Milky Way dwarf galaxies have been used to place stringent constraints on the dark matter’s annihilation cross section. Here, in this paper, we evaluate the sensitivity of the proposed Advanced Particle-Astrophysics Telescope (APT) to dark matter in these systems, finding that such an instrument would be capable of constraining thermal relics with masses as large as m X ~ 600 GeV. Furthermore, in dark matter scenarios motivated by the observed Galactic Center gamma-ray excess, we predict that APT would detect several dwarf galaxies with high significance. Such observations could be used to test the predicted proportionality between the gamma-ray fluxes and J-factors of individual dwarf galaxies, providing us with an unambiguous test of the origin of the Galactic Center excess.

79 ASTRONOMY AND ASTROPHYSICS↗