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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2021

EXPERIMENTAL STUDIES OF NONLINEAR INTEGRABLE OPTICS

State-of-the-art accelerators at energy and intensity frontiers require increasingly bright and powerful particle beams. In conventional linear lattices, intense beams suffer from collective instabilities, resulting in beam losses and maximum beam intensity limits. This thesis presents experimental studies of a novel lattice design concept, the nonlinear integrable optics (NIO), aimed at enhancing beam stability limits with little to no beam dynamics degradation. Single-particle beam dynamics measurements of two NIO devices, the quasi-integrable octupole system and the fully integrable Danilov-Nagaitsev system, were carried out at the purpose-built Integrable Optics Test Accelerator (IOTA) at Fermilab. Their simulation, hardware design, and commissioning process are presented. Extensive model and analysis algorithm development and benchmarking is described. Electron beam data from two scientific runs is analyzed, yielding frequency and phase space dynamics consisten t with m odels. These results demonstrate viability and advantages of the NIO design, providing the groundwork for proton studies in the strong space-charge regime and future integrable accelerators.

Kuklev, Nikita↗
DOE OSTI2023

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments

CH 4 emissions from inland waters are highly uncertain in the current global CH 4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH 4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, in this study, we combine sediment CH 4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH 4 flux at the sediment–water interface. CH 4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O 2 into riverbed sediments, which inhibit CH 4 production and induce CH 4 oxidation, and low VHEFs cause transient reduction in CH 4 flux (relative to production) due to reduced advective CH 4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH 4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH 4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH 4 production and emission in riverbed alluvial sediments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Insights into Tetravalent Np Speciation in HNO 3 through Spectroelectrochemistry and Multivariate Analysis

In situ optical spectroscopy, spectropotentiometry, and multivariate analysis were applied to the Np(IV) nitrate system to better understand speciation and quantify HNO 3 concentration. Thin-layer spectropotentiometry, or spectroelectrochemistry, was leveraged to isolate and stabilize Np(IV) without compromising the solution conditions and generate representative Vis-NIR absorption spectra from 0.5 to 10 M HNO 3 and benchmark the corresponding Np(IV) molar absorptivity coefficients. Spectra were described with principal component analysis (PCA) to identify the purest Np(IV) absorbance spectra among other oxidation states [e.g., Np(V/VI)] at each acid concentration and then to identify the primary sources of variance within each Np(IV) spectrum with respect to Np(IV) nitrate complexes. Then, partial least-squares regression (PLSR) and support vector regression (SVR) models were built to predict HNO 3 concentration from the Np(IV) spectral data. The nonlinear SVR model outperformed the linear PLSR model for the HNO 3 concentration predictions. Finally, the inclusion of spectra collected in edge and center point HNO 3 concentrations in the calibration set was determined to be crucial for producing models with strong predictive capabilities. The multivariate approach used in this study makes it possible to quantify HNO 3 concentration solely based on Np(IV) absorption spectra, which is essential to quantifying processing streams in various online monitoring applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Compact high repetition rate Thomson parabola ion spectrometer

Here, we present the development of a compact Thomson parabola ion spectrometer capable of characterizing the energy spectra of various ion species of multi-MeV ion beams from >10 20 W/cm 2 laser produced plasmas at rates commensurate with the highest available from any of the current and near-future PW-class laser facilities. This diagnostic makes use of a polyvinyl toluene based fast plastic scintillator (EJ-260), and the emitted light is collected using an optical imaging system coupled to a thermoelectrically cooled scientific complementary metal–oxide–semiconductor camera. This offers a robust solution for data acquisition at a high repetition rate, while avoiding the added complications and nonlinearities of micro-channel plate based systems. Different ion energy ranges can be probed using a modular magnet setup, a variable electric field, and a varying drift-distance. We have demonstrated operation and data collection with this system at up to 0.2 Hz from plasmas created by irradiating a solid target, limited only by the targeting system. With the appropriate software, on-the-fly ion spectral analysis will be possible, enabling real-time experimental control at multi-Hz repetition rates.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Distributed Aerodynamic Control using Active Trailing-Edge Flaps for Large Wind Turbines

This work presents a numerical framework to investigate distributed aerodynamic control devices for application in large wind turbines. Tool capabilities were extended to facilitate multiple aerodynamic polar tables. The airfoil aerodynamics characteristics were automatically determined, and blade-pitch, generator-torque, and trailing-edge-flap controllers were tuned in-the-loop according to a specific blade design. This automated workflow allows analysis and optimization of trailing-edge flaps, enabling codesign studies. Results targeted reductions of root-flap-bending moment derivatives. The applied trailing-edge-flap control reduced the standard deviation of root-flap-bending moments by more than 6% and benefit related parameters, e.g., reduce blade-tip deflections, by up to 8%. Because of varying thrust distributions along the blade span, different flap designs have nonlinear characteristics in terms of the control objective and show best performance when located at the radial position with maximum thrust. In general, larger flaps provide a greater influence to reduce the target control objective.

17 WIND ENERGY↗
DOE OSTI2025

Hierarchical Network Partitioning for Solution of Potential-Driven, Steady-State Nonlinear Network Flow Equations

The solution of potential-driven steady-state flow in large networks is a task which manifests in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and in general, there is no numerical algorithm that offers guaranteed convergence to the solution (assuming a solution exists). Some methods offer guarantees in cases where the network topology satisfies certain assumptions, but these methods fail for larger networks. On the other hand, the Newton-Raphson algorithm offers a convergence guarantee if the starting point lies close to the (unknown) solution. It would be advantageous to compute the solution of the large nonlinear system through the solution of smaller nonlinear sub-systems wherein the solution algorithms (Newton-Raphson or otherwise) are more likely to succeed. Here, this letter proposes and describes such a procedure, a hierarchical network partitioning algorithm that enables the solution of large nonlinear systems corresponding to potential-driven steady-state network flow equations.

42 ENGINEERING↗
DOE OSTI2026

Understanding Recombination Pathways of CdSeTe/CdTe Devices Using Temperature Dependence of Fill Factor

Understanding the temperature dependence of photovoltaic parameters is critical for identifying performance limiting mechanisms in CdTe-based solar cells. In this study, we investigate the temperature dependence of fill factor (FF(T)) in CdSeTe/CdTe solar cells using a combination of experimental current density-voltage (JV) measurements and numerical simulations. The measured FF(T) exhibits a nonlinear response with temperature, peaking at moderate temperature values (10–30°C). Through numerical modeling, we reveal that this peaking behavior in FF(T) arises when the dominant recombination mechanism transitions from bulk to interface with increasing temperature. This transition is primarily driven by low hole mobility (0.6–1.2 cm2/V·s), while electron mobility, interface recombination velocities, and carrier lifetime also contribute to the effect. Our findings demonstrate that FF(T) analysis is a promising diagnostic tool for distinguishing recombination pathways and guiding the optimization of thin-film CdTe solar cells under real-world conditions.

CdTe photovoltaics↗
DOE OSTI2025

Structured light approaches in laser-based plasma diagnostics

There is a growing demand for plasma diagnostics suitable for industrial plasma reactors employed in semiconductor nanofabrication, especially relevant to microelectronics and quantum information systems. Such reactors typically have limited optical access and pose considerable diagnostic challenges, including intense background emission, significant thermal loads, and contamination of optical viewports. In this study, we outline research into structured light techniques (laser beams with tailored spatial, temporal, or phase characteristics) that effectively overcome these issues using laser-induced fluorescence (LIF) as an example. The focus of presented diagnostics is on ion kinetics analysis within an industrial plasma source, although this approach is broadly applicable to other plasma systems and diagnostic contexts. We present a confocal LIF implementation using an axicon-generated Bessel annular beam, achieving spatial resolutions of approximately 5 mm at a focal distance of 300 mm, with potential improvements to about 1 mm. This approach matches conventional orthogonal LIF performance but requires only one optical port. Wavelength-modulation LIF employs nonlinear laser wavelength tuning to measure spectral line derivatives, suppressing background emission and enhancing details of spectral line shape. Additionally, we present new results on applying vortex beams (laser beams carrying orbital angular momentum, OAM) for LIF measurements in an industrial plasma device. These measurements enable simultaneous axial and tangential velocity determination using a single laser beam and have been tested with xenon ion transition. Initial quantification of results was performed. Together, these structured-light approaches provide robust, background-resilient, multi-dimensional diagnostics for complex plasma environments.

Romadanov, Ivan [Princeton Plasma Physics Laborato↗
DOE OSTI2024

New insights into supradense matter from dissecting scaled stellar structure equations

The strong-field gravity in general relativity (GR) realized in neutron stars (NSs) renders the equation of state (EOS) P(ε) of supradense neutron star matter to be essentially nonlinear and refines the upper bound for Φ ≡ P/ε to be much smaller than the special relativity (SR) requirement with linear EOSs, where P and ε are respectively the pressure and energy density of the system considered. Specifically, a tight bound Φ ≲ 0.374 is obtained by perturbatively anatomizing the intrinsic structures of the scaled Tolman–Oppenheimer–Volkoff (TOV) equations without using any input nuclear EOS. New insights gained from this novel analysis provide EOS-model-independent constraints on the properties (e.g., density profiles of the sound speed squared s 2 = dP/dε and trace anomaly Δ = 1/3 – Φ) of cold supradense matter in NS cores. Using the gravity-matter duality in theories describing NSs, we investigate the impact of gravity on supradense matter EOS in NSs. In particular, we show that the NS mass M NS , radius R, and compactness ξ ≡ M NS /R scale with certain combinations of its central pressure and energy density (encapsulating its central EOS). Thus, observational data on these properties of NSs can straightforwardly constrain NS central EOSs without relying on any specific nuclear EOS model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Paraxial, Thin-Lens Analysis of Fixed-Tune, Non-Scaling FFAs with Two Magnets per Cell

Fixed-field accelerators (FFAs) have closed orbits that change as a function of beam momentum. It is sometimes useful to avoid various resonances by keeping the tunes constant as these orbits change. A well-known example is the scaling FFA where the entire beam orbit and optics are geometrically scaled as a function of momentum. However, this is stricter than necessary: cells with three or more lenses can have fixed tunes even when the focussing strengths in the lenses change. Recently, Dejan Trbojevic has found a pair of nonlinear magnets that produce fixed tunes and fixed beta functions while not being a scaling FFA. This improves on a more approximate solution in. Notably a scaling FFA would require one magnet to be entirely reverse-bending, whereas the Trbojevic solution does not do this and instead resembles an intermediate point between the traditional scaling field profiles and the nonscaling profiles centred around a momentum with equal and positive fields. This suggests there are at least three levels of stringency that one can apply to a fixed-tune FFA design: 1. Fixed cell tunes (in both planes) as a function of momentum; 2. Fixed optics (beta functions) as a function of momentum; 3. Similarity of all orbits via a scaling symmetry law ↔ traditional scaling FFA. This note studies the interesting case #2 above (#3 being fully characterised by the orbit at a single energy) in the simplest possible example: a cell of two thin lenses in the small angle (paraxial) approximation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Using Machine Learning to Understand Electric and Hybrid Vehicles Ownership in Burdened and Nonburdened Communities

Transitioning to electric and hybrid vehicles (EHVs) for all communities is a pivotal step toward sustainable transportation and environmental conservation. This paper aims to understand the adoption of EHVs, focusing on burdened communities (BCs) in the United States. The EHV ownership-based analysis combines two datasets—behavioral data from the Puget Sound Regional Travel Survey integrated with BCs (Justice40) data covering transportation insecurity, environmental burden, social vulnerability, health vulnerability, and climate and disaster risk burden. After creating this unique database, descriptive analysis and modeling are used to analyze the data and predict EHV ownership in the future. Specifically, we use a new method that combines particle swarm optimization (PSO) with a stacking model named PSO-Stacking, which incorporates heterogeneous base learners of machine learning and deep learning. PSO applies a customized objective function to select the optimal hyperparameters for heterogeneous learners within the stacking model, effectively addressing challenges such as multicollinearity, data imbalance, nonlinearity, and overfitting. The proposed solution covers more accurate results than standard benchmark models for EHV ownership in BCs and non-BCs. In addition, the results of the PSO-Stacking method are explained using the local interpretable model-agnostic explanations technique. Results show a negative correlation between the BCs indicators, that is, higher transportation insecurity associated with lower EHV ownership. Furthermore, BCs have higher future climate risk scores, diesel particulate matter levels, and PM2.5 in the air than non-BCs because of higher conventional vehicle ownership. These communities are at higher risk and can benefit from electrification, EV infrastructure, and EV policies to address environmental challenges.

Aslam, Zeeshan [ORNL]↗
DOE OSTI2021

Numerical Codes for the DESC-LSST Analysis Pipeline: Core Cosmology Library Standard Modules and Beyond wCDM Modules (Final Technical Report)

The overall objective of the project is to investigate and develop specific software modules and analysis components for the software pipeline of LSST Dark Energy Science Collaboration (DESC). Following the key projects of DESC Science Roadmap (SRM), we will write and test computer codes for the Core Cosmology Library (CCL) in order to complete its modules, functionalities, and interface to work with the analysis pipelines from the five science probes of DESC (parts of SRM deliverables CX4.2TJP, CX6.2CS). We will also code modules for CCL to test models beyond w-Cold-Dark-Matter (wCDM) and modification to gravity (MG). Interfaces for MG models will also be developed for the TJPCOSMO software which is the main pipeline of the Theory and Joint Probe (TJP) working group (deliverable TJP2.3). In order to use the full power of LSST data to constrain MG models, we will also work on constraints from nonlinear regime by running and analyzing MG N-Body simulations using a Parameterized-Post-Friedmann framework into the Gadget-2 simulation package (deliverable TJP2.2). Preliminary results for the simulations were obtained in the past. In collaboration with other DESC groups, we plan to make these simulations feedable to cosmic emulators that are practical for likelihood analyzes (deliverable TJP2.2, parts of CX6.2CS). We will also modify and integrate our current codes for consistency tests between data sets and probes into the pipeline (parts of deliverables CX8.2TJP, TJP2.3). We understand that other groups will contribute to some of these objectives but our team will focus and collaborate with others on the particular part of testing MG and models beyond wCDM and refine the DESC pipeline for this purpose. PI has been coordinating his work with the TJP and CS working groups and the DESC management team. PI is a full member of DESC since June 2013. He and his students have been contributing to LSST-DESC activities and work including TJP telecons, collaboration meetings, hack-weeks, and workshops. PI chaired or co-chaired sessions at collaboration meetings and hack-weeks about testing gravity and models beyond wCDM using LSST. He is coordinating the TJP2 projects for testing models beyond wCDM including the writing of DESC-research-note, development of code for pipeline, and N-Body simulations for MG and beyond wCDM models testable with LSST analyses. As stressed in the DESC white paper, SRM, and P5 report, one of the important questions in understanding cosmic acceleration and dark energy is to be able to distinguish whether the acceleration is due to a dark energy component in the universe or a modification to gravity. Answering these questions will have a significant impact on the question of cosmic acceleration and dark energy. The methods that we will use include analytical work, numerical code, and N-Body simulations. A first approach that that we will use consists of using growth rate parameters that enter the perturbed dynamics equations. These parameters take distinctive values for distinct gravity theories and have potential to distinguish between Dark Energy and Modified Gravity. The second method is to look for inconsistencies in Dark Energy parameter spaces using specific combinations of cosmological data sets. Our investigation addresses the Dark Energy problem that is relevant to the mission of the HEP program to understand how our universe works at its most fundamental level. It will allow us to make progress on the HEP mission to explore the nature of Dark Energy and the basic nature of space and time using future surveys such as LSST. The investigation supports the DOE HEP program Cosmic Frontier as it will contribute to the study and understanding of dark energy and fundamental properties of the universe. The investigation contributes directly to LSST-DESC key projects and their deliverables as described in the Science Road-map document to build analysis pipeline and to test dark energy and beyond wCDM models using LSST.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2020

UNESE Data Analysis-Disko Elm Gas Transport Characteristics (NA-22 Quarterly Report)

Recently published papers have demonstrated the impact of sorption mechanisms on gas transport in the subsurface following an underground nuclear explosion. To include sorption processes in the multi-physics models of noble gas production and transport, researchers have conducted experiments of gas sorption in geologic materials and calibrated a linear (Henry’s law) sorption model for all gases in various geologic materials. As a part of Task #4, we requested the experimental data from Sandia Laboratories and derived a dual-mode (Henry’s absorption and Langmuir adsorption) sorption model. The derived model better describes the nonlinear sorption mechanism with much higher fitness with the experimental data. When gas concentrations are relatively low (in the conditions of field experiments or underground nuclear explosions) compared to those used sorption experiments, the nonlinear sorption model can be approximated as a linear sorption model. The equivalent Henry’s constant, which equals the product of Langmuir capacity and affinity, is systematically higher than the literature values. Further analyses and comparison of sorption models are planned in FY2020 Q4 and the sorption model will be coupled in models of nonisothermal multiphase transport for studying noble gas detectability.

58 GEOSCIENCES↗
DOE OSTI2021

Characterizing the Non-linear Interactions Between Tide, Storm Surge, and River Flow in the Delaware Bay Estuary, United States

Low-lying coastal areas in the mid-Atlantic region are prone to compound flooding resulting from the co-occurrence of river floods and coastal storm surges. To better understand the contribution of non-linear tide-surge-river interactions to compound flooding, the unstructured-grid Finite Volume Community Ocean Model was applied to simulate coastal storm surge and flooding in the Delaware Bay Estuary in the United States. The model was validated with tide gauge data in the estuary for selected hurricane events. Non-linear interactions between tide-surge-river were investigated using a non-stationary tidal analysis method, which decomposes the interactions’ components at the frequency domain. Model results indicated that tide-river interactions damped semidiurnal tides, while the tide-surge interactions mainly influenced diurnal tides. Tide-river interactions suppressed the water level upstream while tide-surge interaction increased the water level downstream, which resulted in a transition zone of damping and enhancing effects where the tide-surge-river interaction was prominent. Evident compound flooding was observed as a result of non-linear tide-surge-river interactions. Furthermore, sensitivity analysis was carried out to evaluate the effect of river flooding on the non-linear interactions. The transition zone of damping and enhancing effects shifted downstream as the river flow rate increased.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Recent advances in computational mathematics and applications

We are honored to bring you this special issue dedicated to recent advances in computational mathematics and applications in science and engineering. The papers in this special issue were presented in ”Conference on Computational Mathematics and Applications (CCMA)” held at the University of Nevada Las Vegas (UNLV) during October 25–27, 2019. The conference has attracted about 90 attendees from six countries. The 15 papers in the special issue comprise a diverse collection of theoretical numerical analysis as well as applications in various subjects. Topics cover optimal control of PDEs, discontinuous Galerkin methods, fluid flow in porous media, turbulence flow, static and moving interface problems, complex fluids composed by the mixture of Newtonian fluid and nematic (liquid crystal) flows, modeling of ocean–atmosphere system, image segmentation algorithm, uncertainty quantification problems for turbulence model and Maxwell’s equations, iterative solver for systems resulting from mixed methods, and novel methods for solving systems of nonlinear equations. As the Guest Editors we would like to thank Dr. Leland Jameson at NSF (National Science Foundation) for kindly supporting our conference proposal which made this conference possible. We would also like to thank Darren Sugrue from Elsevier who provided partial support for our conference, and Thennarasu Gunasekaran and his production team from Elsevier who produced this nice special issue. We are grateful to many people (Dr. Zhijian Wu, Lori Ornelas, Elsa Juarez, and many Ph.D. students) at the Department of Mathematical Sciences of UNLV who provided tremendous support for the conference. Finally, we very much appreciate all authors who contributed their precious results to this special issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving cosmological analyses of HI clustering by reducing stochastic noise

High-number-density tracers of large-scale structure, such as the HI-rich galaxies measured by 21 cm intensity mapping, have low sampling noise, making them particularly promising as cosmological probes. At large scales, this sampling noise can be subdominant to other scale-independent contributions to the power spectrum; such contributions arise from nonlinear bias, and exceed the sampling noise if at least one of the associated bias coefficients is sufficiently large. This has important consequences for cosmological constraints obtained from such tracers, since it indicates that using the power spectrum does not lead to optimal constraints even in the linear regime. In this paper, we provide a conservative estimate of the possible improvement in constraining power of a 21 cm survey if one were to use an optimal analysis strategy (such as field-level analysis), where only the true sampling noise enters the error budget. We find that improvements in uncertainties on some cosmological parameters can be as large as 50%, depending on redshift, foreground cleaning efficiency, scales used in the analysis, and instrumental noise. One byproduct of our work is measurements of bias parameters and stochasticity for neutral hydrogen in the IllustrisTNG simulation over a wide range of redshifts; we provide simple fitting formulas for these measurements. Furthermore, our results motivate further exploration of new optimal analysis techniques and provide important insights into the constraining power of current and future 21 cm surveys.

79 ASTRONOMY AND ASTROPHYSICS↗