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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Challenges in Product Selectivity for Electrocatalytic Reduction of Amine-Captured CO 2 : Implications for Reactive Carbon Capture

CO 2 is a potential feedstock for carbon-based fuels or materials, but is only available in dilute streams. Integrated processes for CO 2 capture and conversion directly valorize the CO 2 captured by sorbent materials, skipping the energetically expensive sorbent regeneration step. Amines are the most heavily studied liquid-phase sorbent materials for CO 2 capture from dilute streams. Amines react with CO 2 in a 2:1 ratio to form the corresponding ammonium carbamate. Ammonium carbamate [NH 4 ][H 2 NCO 2 ] was tested as the substrate using the highly selective and robust CO 2 -to-formate reduction electrocatalyst [( tBu POCOP)Ir(H)(NCCH 3 ) 2 ], where ( tBu POCOP) is the tridentate pincer ligand 2,6-bis(di tert -butyl-phosphonito). When ammonium carbamate was used as the substrate instead of CO 2 , only hydrogen was produced. An equivalent electrolysis with ammonium hexafluorophosphate with CO 2 also resulted in primarily hydrogen. Methyl carbamate and urea were also tested as substrates as proxies for carbamate that do not contain an equivalent of ammonium, and there was also negligible reduction to carbon-based products. These results indicate that the loss of selectivity observed for aminecaptured CO 2 , or ammonium carbamate, is likely due to the generation of the acidic ammonium equivalent as well as the greater challenge of reducing carbamate compared to CO 2 . This study illustrates that catalysts with high selectivity for concentrated CO 2 can favor hydrogen evolution and loss of carbon-based products when amine-captured CO 2 is used instead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Recovery of p -Hydroxybenzoic Acid from Cu-Catalyzed Alkaline Hydrogen Peroxide Pretreatment of Hybrid Poplar

p-Hydroxybenzoic acid (pHBA) is present in some hardwood biomass feedstocks as an ester attached to the polymeric lignin subunits. Here, we report the isolation and recovery of pHBA from alkaline liquor waste streams obtained from fractionation of hybrid poplar using the “Cu-AHP process”. This process features two stages: alkaline pre-extraction (AP) of the biomass and copper-catalyzed alkaline hydrogen peroxide pretreatment. On a biomass scale of 100 g, 0.5 g of pHBA with a purity of 96% was obtained after decolorization and crystallization. The scalability of the purification process was demonstrated by using liquor obtained from the first-stage AP with 900 g of poplar biomass to afford 3.5 g of crystalline pHBA with 97% purity. Altogether, these results show that pHBA may be isolated and purified from waste streams generated in the two-stage Cu-AHP process without any alteration of the process conditions or impact on carbohydrate and lignin fractionation.

09 BIOMASS FUELS↗
DOE OSTI2024

ZeroCAL: Eliminating Carbon Dioxide Emissions from Limestone’s Decomposition to Decarbonize Cement Production

Limestone (calcite, CaCO 3 ) is an abundant and cost-effective source of calcium oxide (CaO) for cement and lime production. However, the thermochemical decomposition of limestone (~800 °C, 1 bar) to produce lime (CaO) results in substantial carbon dioxide (CO 2(g) ) emissions and energy use, i.e., ~1 tonne [t] of CO 2 and ~1.4 MWh per t of CaO produced. Here, we describe a new pathway to use CaCO 3 as a Ca source to make hydrated lime (portlandite, Ca(OH) 2 ) at ambient conditions (p, T) while nearly eliminating process CO 2(g) emissions (as low as 1.5 mol. % of the CO 2 in the precursor CaCO 3 , equivalent to 9 kg of CO 2(g) per t of Ca(OH) 2 ) within an aqueous flowelectrolysis/ pH-swing process that coproduces hydrogen (H 2(g) ) and oxygen (O 2(g) ). Because Ca(OH) 2 is a zero-carbon precursor for cement and lime production, this approach represents a significant advancement in the production of zero-carbon cement. The Zero CArbon Lime (ZeroCAL) process includes dissolution, separation/recovery, and electrolysis stages according to the following steps: (Step 1) chelator (e.g., ethylenediaminetetraacetic acid, EDTA)-promoted dissolution of CaCO 3 and complexation of Ca 2+ under basic (>pH 9) conditions, (Step 2a) Ca enrichment and separation using nanofiltration (NF), which allows separation of the Ca-EDTA complex from the accompanying bicarbonate (HCO 3 – ) species, (Step 2b) acidity-promoted decomplexation of Ca from EDTA, which allows near-complete chelator recovery and the formation of a Ca-enriched stream, and (Step 3) rapid precipitation of Ca(OH) 2 from the Ca-enriched stream using electrolytically produced alkalinity. These reactions can be conducted in a seawater matrix yielding coproducts including hydrochloric acid (HCl) and sodium bicarbonate (NaHCO 3 ), resulting from electrolysis and limestone dissolution, respectively. Careful analysis of the reaction stoichiometries and energy balances indicates that approximately 1.35 t of CaCO 3 , 1.09 t of water, 0.79 t of sodium chloride (NaCl), and ~2 MWh of electrical energy are required to produce 1 t of Ca(OH) 2 , with significant opportunity for process intensification. This approach has major implications for decarbonizing cement production within a paradigm that emphasizes the use of existing cement plants and electrification of industrial operations, while also creating approaches for alkalinity production that enable cost-effective and scalable CO 2 mineralization via Ca(OH) 2 carbonation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dynamic Behavior of Pt Multimetallic Alloys for Active and Stable Propane Dehydrogenation Catalysts

Improving the use of platinum in propane dehydrogenation catalysts is a crucial aspect to increasing the efficiency and sustainability of propylene production. A known and practiced strategy involves incorporating more abundant metals in supported platinum catalysts, increasing its activity and stability while decreasing the overall loading. Here, using colloidal techniques to control the size and composition of the active phase, we show that Pt/Cu alloy nanoparticles supported on alumina (Pt/Cu/Al 2 O 3 ) displayed elevated rates for propane dehydrogenation at low temperature compared to a monometallic Pt/Al 2 O 3 catalyst. We demonstrate that the enhanced catalytic activity is correlated with a higher surface Cu content and formation of a Pt-rich core and Cu-rich shell that isolates Pt sites and increases their intrinsic activity. However, rates declined on stream because of dynamic metal diffusion processes that led to a more uniform alloy structure. This transformation was only partially inhibited by adding excess hydrogen to the feed stream. Instead, cobalt was introduced to provide trimetallic Pt/Cu/Co catalysts with stabilized surface structure and stable activity and higher rates than the original Pt/Cu system. In conclusion, the structure–activity relationship insights in this work offer improved knowledge of propane dehydrogenation catalyst development featuring reduced Pt loadings and notable thermal stability for propylene production.

Alloys↗
DOE OSTI2026

pH-Driven Restructuring of Hydration Layers and Cation Adsorption at the Alumina–Water Interface

Oxide−water interfaces underpin ion separations, catalysis, and electrochemical energy technologies, where the electrical double layer (EDL) controls adsorption, transport, and reactivity. However, the molecular-scale links between pHdependent surface protonation, hydration-layer structure, and counterion adsorption remain poorly defined. Here, we combine in situ crystal truncation rod (CTR) and resonant anomalous X-ray reflectivity (RAXR) with streaming potential measurements and ab initio molecular dynamics (AIMD) simulations to resolve the chemical and structural evolution of the EDL at the single-crystal alumina (012)−water interface in 10 mM Rb + over pH 3−12. CTR measurements reveal two distinct adsorbed water layers at ∼2.2 and ∼3.5 Å above the surface. Each water layer shifts toward the substrate at transition pHs near 6.5 and 10.6, respectively, reflecting changes in primary hydration layer structure in response to the deprotonation of bridging and terminal aluminol groups. RAXR shows a 10-fold increase in Rb + coverage and a decrease in mean adsorption height from ∼3.5 to ∼2.7 Å with increasing pH, indicating enhanced counterion binding accompanied by Stern layer contraction. Streaming potential measurements demonstrate that the zeta potential, i.e., the potential at the hydrodynamic shear plane, is positive at pH 3 and becomes negative at pH ≥ 3.5. The negative charge magnitude increases with increasing pH, consistent with progressive surface deprotonation at higher pH. AIMD identifies inner- and outer-sphere Rb+ complexes whose adsorption heights and coordination geometries depend sensitively on the protonation state of surface oxygens, providing atomistic support for the experimentally inferred trends. These measurements establish two discrete, site-specific pH transitions in hydration-layer structure that track aluminol (de)protonation and quantitatively link them to a pH-driven contraction of the Stern layer (increasing Rb + coverage and decreasing adsorption height). This provides a direct structural basis for connecting surface acid−base chemistry to ion binding distances at an oxide−water interface.

Adsorption↗
DOE OSTI2024

Constraints and Drivers of Dissolved Fluxes of Pyrogenic Carbon in Soil and Freshwater Systems: A Global Review and Meta‐Analysis

Abstract Pyrogenic carbon (PyC) is a significant component of the global soil carbon pool due to its longer environmental persistence than other soil organic matter components. Despite PyC's persistence in soil, recent work has indicated that it is susceptible to loss processes such as mineralization and leaching, with the significance and magnitude of these largely unknown at the hillslope and watershed scales. We present a review of the work concerning dissolved PyC transport in soil and freshwater. Our analysis found that the primary environmental controls on dissolved PyC (dPyC) transport are the formation conditions and quality of the PyC itself, with longer and higher temperature charring conditions leading to less transport of dPyC. While correlations between dPyC and dissolved organic carbon in rivers and other pools are frequently reported, the slope of these correlations was pool‐dependent (i.e., soil‐water, precipitation, lakes, streams, rivers), suggesting site‐specific environmental controls. However, the lack of consistency in analytical techniques and sample preparation remains a major challenge to quantifying environmental controls on dPyC fluxes. We propose that future research should focus on the following: (a) consistency in methodological approaches, (b) more quantitative measures of dPyC in pools and fluxes from soils to streams, (c) turnover times of dPyC in soils and aquatic systems, and (d) improved understanding of how mechanisms controlling the fate of dPyC in dynamic post‐fire landscapes interact. With more refined quantitative information about the controls on dPyC transport at the hillslope and landscape scale, we can increase the accuracy and utility of global carbon models.

58 GEOSCIENCES↗
DOE OSTI2025

The Role of Bedrock Circulation Depth and Porosity in Mountain Streamflow Response to Prolonged Drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi-year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage-limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Global River Topology (GRIT): A Bifurcating River Hydrography

Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km 2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗
DOE OSTI2025

Interconnected nanoconfining pore networks enhance catalyst CO 2 interaction in electrified reactive capture

Systems that sequentially capture and upgrade CO 2 from air to fuels/fuel-intermediates, such as syngas and ethylene, rely on an energy-intensive CO 2 release process. Electrified reactive capture systems transform CO 2 obtained directly from carbonate capture liquid into products. Previous reactive capture systems show a decline in Faradaic efficiencies (FE) at current densities above 200 mA/cm 2 . Here we show the chemical origins of this problem, finding that prior electrocatalyst designs failed to arrest, activate, and reduce in situ-generated CO 2 (i-CO 2 ) before it traversed the catalyst layer and entered the tailgas stream. We develop a templated synthesis to define pore structures and the sites of Ni single atoms, and find that carbon-nitrogen-based nanopores are effective in accumulating i-CO 2 via short-range, non-electrostatic interactions between CO 2 molecules and the nanochannel walls. These interactions confine and enrich i-CO 2 within the pores, enhancing its binding and activation. We report as a result carbonate electrolysis at 300 mA/cm 2 with FE to CO of 50% ± 3%, and with <1% CO 2 in the tailgas outlet stream. This corresponds to a projected energy efficiency (EE) to 2:1 syngas of 46% at 300 mA/cm 2 when H 2 is added using a water electrolyzer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Elastic deformation as a tool to investigate watershed storage connectivity

Storage-discharge relationships and dynamic changes in storage connectivity remain key unknowns in understanding and predicting watershed behavior. In this study, we use Global Positioning System measurements of load-induced Earth surface displacement as a proxy for total water storage change in four climatologically diverse mountain watersheds in the western United States. Comparing total water storage estimates with stream-connected storage derived from hydrograph analysis, we find that each of the investigated watersheds exhibits a characteristic seasonal pattern of connection and disconnection between total and stream-connected storage. We investigate how the degree and timing of watershed-scale connectivity is related to the timing of precipitation and seasonal changes in dominant hydrologic processes. Our results show that elastic deformation of the Earth due to water loading is a powerful new tool for elucidating dynamic storage connectivity and watershed discharge response across scales in space and time.

Environmental Sciences & Ecology↗
DOE OSTI2024

Discerning molecular-level CO 2 adsorption behavior in amine-modified sorbents within a controlled CO 2 /H 2 O environment towards direct air capture

Sorbents designed for direct air capture (DAC) play a crucial role in the pursuit of achieving net-zero carbon dioxide emissions. This study elucidates CO 2 adsorption from dilute, humidified CO 2 streams onto an amine-modified benchmark DAC adsorbent via solid-state NMR spectroscopy. Various NMR techniques, including 1D 1 H MAS, 13 C MAS, 2D 1 H– 13 C HETCOR NMR, and 1 H R 2 and R 1ρ relaxometry reveal the impact of CO 2 partial pressure and H 2 O on CO 2 adsorption behavior. Here we find that CO 2 concentration governs the stepwise formation of ammonium carbamate, carbamic acid, and physisorbed CO 2 , where relative humidity (RH) at a desired low (<400 ppm) CO 2 loading affects total CO 2 uptake. The relaxation studies reveal the cooperative or competitive nature of H 2 O–CO 2 sorption in CO 2 -dilute humid gas, and in particular polymer swelling upon humidification. From those results, we demonstrate that the observed absorption capacity enhancement by humidity is caused by pore opening due to sorbent swelling, and not by bicarbonate formation. This NMR-discerned speciation provides insights into sorption behavior at different RHs in dilute CO 2 gas streams, simulating real-world atmospheric conditions, and governs the design of efficient and adaptable material-process combinations for solid sorbent DAC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Harnessing ketones as hydrogen acceptors for atom-efficient upgrading of oxygenates to fuels over H/ZSM-5

Heterogeneous mixtures of bio-derived oxygenates are promising feedstocks for synthetic aviation fuel (SAF), but conversion strategies for one common component—short-chain (C 5–7 ) internal ketones—are lacking. Previous work has shown that cyclization of ketones over H/ZSM-5 is limited by its high productivity of light paraffins. We study 4-heptanone upgrading over H/ZSM-5 and show that aromatics and olefins can be formed at high carbon yield when operating at up to 90% conversion. The yield of desirable products is not impacted by the introduction of a recycle stream of the unconverted 4-heptanone and other products with similar boiling points. We hypothesize, based on first-principles calculations, that the higher olefin yield is driven by the ease of hydrogen transfer to unreacted ketones as opposed to hydrogenating olefin products. We demonstrate how this ease of hydrogen transfer to ketones can be leveraged to enhance olefin selectivity in the conversion of methanol to olefins as well by co-feeding ketones. Olefinic products of the cyclization reaction are then oligomerized to a SAF blendstock to demonstrate an end-to-end ketone-to-SAF process facilitated by upgrading ketones over H/ZSM-5 at partial conversion with a recycle stream. The results of this work demonstrate a strategy to improve the carbon yield from bio-derived acids to SAF to over 75%, representing a relative improvement of more than 50% compared to previously reported data.

08 HYDROGEN↗
DOE OSTI2026

Energy utilization strategies in lignocellulosic biorefineries

Lignocellulosic biorefineries can generate various energy-rich side streams, including biogas from anaerobic digestion, a lignin stream from biomass pretreatment, and conversion residues. This study evaluates different chemical energy utilization strategies in lignocellulosic biorefineries based on energy and carbon efficiencies, cost, and greenhouse gas mitigation potential. Specifically, using modeling and optimization, we examine strategies that include electricity generation, biogas upgrade to biomethane, lignin valorization, and carbon capture and storage. We find that upgrading biogas to biomethane demonstrates the highest energy efficiency and leads to the lowest minimum fuel selling price for the main product, ethanol. The biorefinery with carbon capture and storage achieves the lowest net carbon footprint. Lignin valorization has the highest carbon footprint due to the additional materials required for lignin depolymerization, despite its potential to produce high value bioproducts.

Aboagye, Emmanuel A. [Princeton Univ., NJ (United ↗
DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗
DOE OSTI2025

Candidate strongly lensed type Ia supernovae in the Zwicky Transient Facility archive

Gravitationally lensed type Ia supernovae (glSNe Ia) are unique astronomical tools that can be used to study cosmological parameters, distributions of dark matter, the astrophysics of the supernovae, and the intervening lensing galaxies themselves. A small number of highly magnified glSNe Ia have been discovered by ground-based telescopes such as the Zwicky Transient Facility (ZTF), but simulations predict that a fainter, undetected population may also exist. We present a systematic search for glSNe Ia in the ZTF archive of alerts distributed from June 1 2019 to September 1 2022. Using the AMPEL platform, we developed a pipeline that distinguishes candidate glSNe Ia from other variable sources. Initial cuts were applied to the ZTF alert photometry (with constraints on the peak absolute magnitude and the distance to a catalogue-matched galaxy, as examples) before forced photometry was obtained for the remaining candidates. Additional cuts were applied to refine the candidates based on their light curve colours, lens galaxy colours, and the resulting parameters from fits to the SALT2 SN Ia template. The candidates were also cross-matched with the DESI spectroscopic catalogue. Seven transients were identified that passed all the cuts and had an associated galaxy DESI redshift, which we present as glSN Ia candidates. Although superluminous supernovae (SLSNe) cannot be fully rejected as contaminants, two events, ZTF19abpjicm and ZTF22aahmovu, are significantly different from typical SLSNe and their light curves can be modelled as two-image glSN Ia systems. From this two-image modelling, we estimate time delays of 22 ± 3 and 34 ± 1 days for the two events, respectively, which suggests that we have uncovered a population of glSNe Ia with longer time delays. The pipeline is efficient and sensitive enough to parse full alert streams. It is currently being applied to the live ZTF alert stream to identify and follow-up future candidates while active. This pipeline could be the foundation for glSNe Ia searches in future surveys, such as the Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Accurately simulating core-collapse self-interacting dark matter halos

The properties of satellite halos provide a promising probe for dark matter (DM) physics. Observations have motivated current efforts to explain surprisingly compact DM halos. If DM is not collisionless, but has strong self-interactions, halos can undergo gravothermal collapse, leading to higher densities in the central region of the halo. However, it is challenging to model this collapse phase from first principles. To improve on this, we sought to better understand the numerical challenges and convergence properties of self-interacting dark matter (SIDM) N-body simulations in the collapse phase. Especially, our aim was to better understand the evolution of satellite halos. To do so, we ran SIDM N-body simulations of a low-mass halo in isolation and within an external gravitational potential. The simulation set-up was motivated by the perturber of the stellar stream GD-1. We find that the halo evolution is very sensitive to energy conservation errors, and a SIDM kernel size that is too large can artificially speed up the collapse. Moreover, we demonstrate that the King model can describe the density profile at small radii for the late stages that we have simulated. Furthermore, for our most highly resolved simulation (N = 5 × 10 7 ) we have made the data public. It can serve as a benchmark. Overall, we find that the current numerical methods do not suffer from convergence problems in the late collapse phase and provide guidance on how to choose numerical parameters, for example that the energy conservation error is better kept well below 1%. This allows simulations to be run of halos that become concentrated enough to explain observations of GD-1-like stellar streams or strong gravitational lensing systems.

dark matter↗