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DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗

DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗

DOE OSTI2025

Utilization of the LS-APGD microplasma/orbitrap-FTMS booster system for detection and isotopic analysis of neodymium nanoparticles

Detection and isotopic analysis of particle populations has seen rapid growth across several application areas, including environmental analysis, nuclear forensics, and food safety. The ability to characterize the particles' unique elemental and isotopic fingerprints could provide information related to formation, processing history, and transport. Regarding nuclear forensics, isotopic analysis of particles derived from diverse materials is often used as a tool to trace the origin and processing history. Mass spectrometric-based techniques currently used for particle population analysis often suffer from limited mass resolution, particularly when dealing with real-world samples that are affected by isobaric and polyatomic interferences from the matrix. To address these analytical challenges, we propose a novel method utilizing the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma ionization source coupled to an ultrahigh resolution Orbitrap mass spectrometer, further enhanced with the FTMS X2T Booster data acquisition and processing unit. The FTMS Booster enables acquisition of extended transient times of up to 3 s, significantly improving mass resolution, thereby reducing or even eliminating the need for prior separation of isobaric or polyatomic interferences. Additionally, the detection of low-abundance isotopes was improved by increasing the signal-to-noise (S/N) ratio. As proof of concept, this study demonstrates the feasibility of the LS-APGD/Orbitrap-FTMS X2T Booster platform for direct analysis using a suspension of well-characterized ∼120 nm neodymium particles. The quality of the isotope ratios values obtained from a few hundred particles were in good agreement with those obtained from homogeneous ionic solutions. These results highlight the potential of the LS-APGD/Orbitrap platform for rapid, accurate, and interference-resilient isotope ratio analysis of particle populations without the need for dissolution and subsequent chemical separations, offering significant advantages for nuclear forensics, safeguards, and environmental applications. The effort here also points to further paths forward, hopefully towards single particle (SP) analysis using microplasma ionization and the ultrahigh resolving power of the Orbitrap mass analyzer.

FTMS X2T booster↗

DOE OSTI2022

Sequential polymer infusion into solid substrates (SPISS): Impact of processing on sorbent CO 2 adsorption properties

Solid sorbents made of small amine molecules and polyamines infused into mesoporous substrates are promising materials for CO 2 capture technologies. To date, their preparation is mainly based on wet infusion with focus on increasing amine content by varying the structure of the amine sorbent and designing solid substrates of various pore parameters. Less explored in the field are changes in processing to afford efficient CO 2 sorbents. In this study, branched poly(ethylenimine) (bPEI, M w = 800 Da) alone and blends with linear poly(propylenimine) (LPPI, M n = 6,700 Da) are infused into solid SBA-15 substrates by a method that varies the solution processing called sequential polymer infusion into solid substrates (SPISS). The reference 40 % bPEI-SBA-15 samples are split by two methods: split batch in dry suspension (SBD) and split batch in liquid suspension (SBL). Sequentially, alcoholic 10% of bPEI (non-blends) and 10% of LPPI (blends) solutions are introduced to afford the desired products. Under dry conditions, the resulting 50% bPEI-SBA-15 SBD & SBL sorbents display high CO 2 capacities up to 3.47 mmol CO 2 /g SiO 2 for simulated flue gas (10% CO 2 ) and 2.62 mmol CO 2 /g SiO 2 for direct air capture (DAC, 400 ppm CO 2 ). Under humid DAC conditions the CO 2 performance is further enhanced with an uptake of 4.62 mmol CO 2 /gSiO 2 and amine efficiency of 0.22 mmol CO 2 /mmol N. Subjected to extended temperature swing adsorption kinetic cycling (20 cycles), the SPISS samples display stable working capacities and retain over 70% (blends) and over 90% (non-blends) of their initial 12 h adsorption performance. LPPI is demonstrated to be an effective water sorption limiting agent using dynamic vapor sorption measurements. Solid state NMR techniques reveal important insights into the dynamics of the amine polymers confined into SBA-15 pores, as impacted by processing conditions. The results suggest that the conformation of the polymers is different depending on the processing method, displaying relatively tight (SBD) and loose (SBL) packing. Finally, the simple solution processing approaches presented here show that processing variations may guide the design of solid amine sorbents with desirable properties relevant for integration into CO 2 capture technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗

DOE OSTI2026

Omnigenous umbilic stellarators

To better understand the dependence of the magnetic field structure in the plasma edge on the plasma boundary shape, in the context of X-point and island divertor designs, we define and develop a class of stellarators called umbilic stellarators. These equilibria are characterised by a single continuous high-curvature edge on the plasma boundary that goes around multiple times toroidally before meeting itself. We develop a technique that allows us to simultaneously optimise the plasma boundary along with a curve lying on the boundary on which we impose a high curvature while imposing omnigenity – a property of the magnetic field that ensures trapped particle confinement throughout the plasma volume. We find that umbilic stellarators naturally tend to favour piecewise omnigenity instead of omnigenity with a specific helicity. After generating omnigenous umbilic stellarators, we design coil sets for some of them and explore the fieldline structure in the edge and its sensitivity to small fluctuations in the plasma. Finally, using single-stage optimisation, we simultaneously modify the plasma and coil shape and propose an experiment to modify an existing tokamak to a finite-𝛽 stellarator using this technique and explore a potentially simpler way to convert a limited tokamak into a diverted stellarator.

fusion plasma↗

DOE OSTI2026

Post-Mortem Characterization of Cerium Speciation in Molten Calcium Chloride

Abstract Advancing radiochemistry and nuclear materials science requires understanding actinide interactions with molten salts, which are used in next-generation nuclear reactors and for processing of actinides to recover useful fissile material. Understanding chemical interactions of actinides with molten salts has been limited by challenges in developing spectroscopic and X-ray techniques that are compatible with the high temperatures necessary to study molten salts. In this work, interactions of CeO2 (serving as a nonradioactive surrogate for uranium and plutonium) with CaCl2 are characterized following heating. Scanning electron microscopy indicates CeO2 morphological changes from small (<1 μm) particles to 3–5 μm sheets. Powder X-ray diffraction and infrared and Raman spectroscopies show the formation of CeOCl at higher (850–1050 °C) temperatures. In the absence of a chemical reducing agent, it was found that a high-temperature, low-oxygen environment is the key to the formation of oxychloride and that oxychloride formation is inhibited by annealing the CeO2 starting material. Lastly, thermal analysis revealed lowering of the melting point of CaCl2 after heating with CeO2. In all, this work identifies applicable spectroscopic techniques to target studies of heavy elements in molten salt environments and highlights the relationship between chemical speciation and melting behavior, a key thermophysical property.

Kelly, Sheridon N. [Lawrence Livermore National La↗

DOE OSTI2025

Solvent Polarity Independent Symmetry-Breaking Charge Separation in a Slip-Stacked Covalent Terrylene Monoimide Dimer

The design of organic materials capable of efficient photoinduced charge separation in low-polarity environments is a critical challenge for advancing organic photovoltaics. Symmetry-breaking charge separation (SB-CS) offers a promising route to efficient charge separation with minimal energy loss; however, in conventional organic materials this process typically relies on polar solvents to stabilize the charge-separated state. Here, we investigate a slip-stacked terrylene monoimide dimer (TMI 2 ) engineered with intrinsic electronic asymmetry to circumvent this limitation. We demonstrate through a comprehensive suite of ultrafast spectroscopic techniques that TMI 2 undergoes remarkably efficient SB-CS regardless of solvent polarity enabled by the permanent dipole moment of TMI and the accessible intramolecular charge-transfer (ICT) character of its monomer units. Furthermore, analysis of vibronic coherences reveals that the initial state mixing is actively driven by a 193 cm –1 intermolecular mode, while the final SB-CS state is marked by a distinct vibrational fingerprint of the radical ion pair product. Our findings reveal how the intrinsic electronic asymmetry of the TMI monomers that constitute TMI 2 creates an efficient charge separation pathway that precludes the need for external stabilization from a polar solvent. Here, this work establishes a powerful molecular design principle for leveraging intrinsic monomeric asymmetry to achieve efficient charge separation in low-dielectric environments, with significant implications for the development of next-generation organic optoelectronic materials.

Aromatic compounds↗

DOE OSTI2021

Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC-based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Fast superconducting qubit control with subharmonic drives

Increasing the fidelity of single-qubit gates requires a combination of faster pulses and increased qubit coherence. However, these requirements can be contradictory. Additionally, increasing the drive power can heat the qubit’s environment and degrade coherence. In this work, we circumvent this issue and achieve rapid gates by pumping a transmon’s native Kerr at approximately one third of the qubit’s resonant frequency. The subharmonic Rabi rate of the process is proportional to applied drive amplitude cubed, allowing for rapid gates. In addition, we demonstrate that filtering can be used to protect the qubit’s coherence while performing rapid gates. Single qubit gates as short as 37.4 ns are demonstrated with fidelity of 99.91%. We present theoretical calculations indicating that drive induced multi-photon decay will not limit qubit lifetime; calculated power absorption also indicates that this technique could reduce cryostat heating for fast gates, a vital requirement for large-scale quantum computers.

Xia, Mingkang [Univ. of Pittsburgh, PA (United Sta↗

DOE OSTI2022

Understanding What Limits the Voltage of Polycrystalline CdSeTe Solar Cells

The origin of voltage deficits in polycrystalline cadmium selenide telluride (CdSeTe) solar cells is unclear. Here, we present a comprehensive voltage loss analysis performed on state-of-the-art CdSeTe devices - fabricated at Colorado State University and First Solar - using photoluminescence techniques, including external radiative efficiency (ERE) measurements. More specifically, we report the thermodynamic voltage limit Voc,ideal, internal voltage iVoc and external voltage Voc of partially and fully finished cells fabricated with different dopant species, dopant concentrations and back contacts. Arsenic-doped aluminium-oxide-passivated cells made at Colorado State University present remarkably high ERE (>1%) - translating into iVoc above 970 mV - but suffer from poor back-contact selectivity. On the other hand, arsenic-doped devices from First Solar present almost perfect carrier selectivity (Voc=iVoc), leading to Voc above 840 mV, and are limited by recombination in various parts of the device. Thus, development of contact structures that are both passivating and selective in combination with highly luminescent absorbers is key to reducing voltage losses.

CdSeTe devices↗

DOE OSTI2020

Room-temperature superconductivity in a carbonaceous sulfur hydride

One of the long-standing challenges in experimental physics is the observation of room-temperature superconductivity (RTSC). Recently, high temperature conventional superconductivity in hydrogen-rich materials has been reported in several systems under high pressure. The most significant discovery in reaching RTSC is the pressure-driven disproportionation of hydrogen sulfide (H 2 S) to H 3 S with a confirmed Tc of 203 kelvin at 155 gigapascals. As H 2 S readily mixes with hydrogen to form guest-host structures at lower pressures, the comparable size of methane to H 2 S should allow molecular exchange within a large assemblage of van der Waals solids that are (highly) hydrogen-rich with H 2 inclusions that are then the building blocks for novel superconducting compounds at extreme conditions. Here, we report superconductivity in a photochemically transformed carbonaceous sulfur hydride system with a maximum superconducting transition temperature of 287.7 ± 1.2 kelvin (~15° celsius) achieved at 267 ± 10 gigapascals. The superconducting state is observed over a broad pressure range from 140 to 275 gigapascals, with a dramatic upturn in Tc above 220 gigapascals. Superconductivity is established by the observation of zero resistance, magnetic susceptibility up to 190 gigapascals, and reduction of Tc under an external magnetic field up to 9 tesla, with an upper critical magnetic field of ~62 tesla based on Ginzburg–Landau (GL) model at zero temperature. The light, quantum nature of hydrogen limits structural and stoichiometric determination using x-ray scattering techniques. Raman spectroscopy is used to probe the chemical and structural transformations pre-metallization. In conclusion, the introduction of chemical tuning within our reported ternary system could be the path forward to lowering the pressure while preserving the RTSC properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

RIFTA: A Robust Iterative Fourier Transform-based dwell time Algorithm for ultra-precision ion beam figuring of synchrotron mirrors

With the rapid evolution of synchrotron X-ray sources, the demand for high-precision X-ray mirrors has greatly increased. Single nanometer profile error is required to keep imaging capability at the diffraction limit. Ion Beam Figuring (IBF), as a highly deterministic surfacing technique, has been used for ultra-precision finishing of mirrors. One crucial step that guides the IBF process is dwell time calculation. A valid dwell time solution should be non-negative and duplicate the shape of the desired removal map. Another important aspect is to minimize the total dwell time. In this study, we propose a Robust Iterative Fourier Transform-based dwell time Algorithm (RIFTA) that automatically fulfills these requirements. First, the thresholded inverse filtering in Fourier transform-based deconvolution is stabilized and automated by optimizing the threshold value using the Nelder-Mead simplex algorithm. Second, a novel two-level iterative scheme is proposed to guarantee the minimized total dwell time with its non-negativity at each dwell point. Third, a bicubic resampling is employed to flexibly adapt the calculated dwell time map to any IBF process intervals. The performance of RIFTA is first studied with simulation, followed by a comparison with the other state-of-the-art dwell time algorithms. We then demonstrate with an experiment that, using the dwell time calculated by the RIFTA, the total dwell time is shortened by a factor of two and the RMS in a 5 × 50 mm clear aperture was reduced from 3.4 nm to 1.1 nm after one IBF run, which proves the effectiveness and the efficiency of the proposed algorithm.

42 ENGINEERING↗

DOE OSTI2026

Ultra-low field 13 C MRI of hyperpolarized pyruvate

Medicine is evolving beyond therapy largely predicated on anatomical information and towards incorporating patient-specific molecular biomarkers of disease for more accurate diagnosis and effective treatment. The complementary combination of hyperpolarization by spin-lock induced crossing signal amplification by reversible exchange (SLIC SABRE) and low field magnetic resonance imaging (MRI) can enable accessible metabolic imaging to advance personalized medicine. Hyperpolarized 13 C-enriched pyruvate has demonstrated promise for imaging metabolism in cancer, heart disease and neurodegenerative disorders; however, broader clinical adoption awaits validated clinical indications, and is further constrained by the cost and limited availability of current hyperpolarization technology. Parahydrogen-based polarization techniques, paired with low-cost high-performance MRI at millitesla fields, offer a means of broadening the reach of metabolic imaging. Here we show results demonstrating in situ hyperpolarization of pyruvate at 6.5 mT by SLIC SABRE, followed by immediate readout without field cycling or sample shuttling. We achieve 13 C signal enhancements several million times above thermal equilibrium at 6.5 mT, corresponding to polarization levels of approximately 3%. Leveraging this enhancement, we perform 13 C MRI and acquire NMR spectra with resolution sufficient to distinguish chemical shifts between pyruvate isotopomers. These results show a viable pathway towards accessible metabolic imaging with hyperpolarized 13 C MRI at ultra-low field.

Medical and clinical diagnostics↗

DOE OSTI2026

The sweeper spectrometer for neutron invariant-mass spectroscopy at FRIB

Neutron invariant-mass spectroscopy (NIMS) is a key technique for studying unbound and weakly bound nuclei at the limits of stability. At the Facility for Rare Isotope Beams (FRIB), such measurements are performed using the Sweeper spectrometer, a large-gap, high-rigidity dipole system coupled to the MoNA-LISA neutron detector arrays. To meet the demands imposed by higher beam energies (>130 MeV/u) and the broad cocktail-beam selection available at FRIB, the spectrometer has recently been upgraded to improve particle-identification and detection performance. Upstream of the reaction target, a plastic scintillator with Silicon photomultiplier (SiPM) readout provides the global trigger and time reference, two parallel plate avalanche counters (PPACs) track the trajectories of incoming beam particles, and a silicon PIN detector measures the energy loss, ΔE, for charge (Z) identification. After the Sweeper magnet, the trajectories of the reaction products are tracked by two micro-pattern drift chambers (MPDCs), their charge (Z) is identified by a Frisch-grid ionization chamber (FG-IC), and their mass-to-charge ratio (A/Q) is deduced by time-of-flight measurement using a fast plastic scintillator read out by an array of photomultiplier tubes (PMTs). The detection system also incorporates the Modular Neutron Array (MoNA) for neutron detection and the CAESium-iodide scintillator ARray (CAESAR) for high-efficiency γ-ray measurements to enable full kinematic reconstruction. Performance was evaluated using a cocktail beam around 37 Al accelerated at E ≈ 130 MeV/u during the first FRIB campaign, demonstrating the readiness of the upgraded system for future studies of nuclei at and beyond the neutron drip line.

Particle identification methods↗

DOE OSTI2022

The DESI N -body Simulation Project – II. Suppressing sample variance with fast simulations

ABSTRACT Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim 20\, h^{-3}\, \mathrm{Gpc}^{3}$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\, h^{-3}\, \mathrm{Gpc}^{3}$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-N-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k \lt 0.3\, h\, \mathrm{Mpc}^{-1}$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

cosmology: theory↗

DOE OSTI2023

Optical polarization and spectral properties of the hydrogen-poor superluminous supernovae SN 2021bnw and SN 2021fpl

ABSTRACT New optical photometric, spectroscopic, and imaging polarimetry data are combined with publicly available data to study some of the physical properties of the two hydrogen-poor superluminous supernovae (SLSNe) SN 2021bnw and SN 2021fpl. For each SLSN, the best-fitting parameters obtained from the magnetar model with Modular Open-Source Fitter for Transients do not depart from the range of parameter obtained on other SLSNe discussed in the literature. A spectral analysis with SYN++ shows that SN 2021bnw is a W type, fast evolver, while SN 2021fpl is a 15bn type, slow evolver. The analysis of the polarimetry data obtained on SN 2021fpl at four epochs (+1.8, +20.6, +34.1, and +43.0 d, rest frame) shows >3σ polarization detections in the range of 0.8–1 per cent. A comparison of the spectroscopy data suggests that SN 2021fpl underwent a spectral transition a bit earlier than SN 2015bn, during which, similarly, it could have underwent a polarization transition. The analysis of the polarimetry data obtained on SN 2021bnw does not show any departure from symmetry of the photosphere at an empirical diffusion time-scale of ≈2 (+81.1 d rest frame). This result is consistent with those on the sample of W-type SLSN observed at empirical diffusion time-scale ≤ 1 with that technique, even though it is not clear the effect of limited spectral windows varying from one object to the other. Measurements at higher empirical diffusion time-scale may be needed to see any departure from symmetry as it is discussed in the literature for SN 2017egm.

Astronomy & Astrophysics↗