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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2024

SIREN: Scaling Ion-Traps by REquiring iNnovative Heterogenous Integration

The SIREN (Scaling Ion Traps by Requiring iNnovative heterogenous integration) project explores the feasibility of heterogeneous integration (HI) as a transformative approach to scaling ion traps, a critical technology for advancing quantum computers and atomic clocks. Traditional ion trap architectures face significant challenges in scalability due to limitations in optical access, fabrication techniques, and material constraints. SIREN addresses these challenges by leveraging HI, which combines different materials and fabrication processes to create more complex and efficient ion trap structures. HI integrated structures can be manufactured without compromising the process to maintain compatibility to ion traps. This project focuses on integrating a separately fabricated waveguide with a fully functional ion trap. The respective alignment between the pieces needs to be accurate to less than 2 µm to ensure that the light from the waveguide can overlap with the trapping region. The fine alignment must also be maintained through an ultra-high vacuum bake, a critical step in preparing an ion trap experiment. The project's outcomes suggest that heterogeneous integration is a promising pathway for overcoming current scalability barriers, paving the way for the next generation of quantum technologies. SIREN's findings contribute significantly to the field, offering a scalable solution that could accelerate the development of practical quantum computers and highly accurate atomic clocks.

42 ENGINEERING↗
DOE OSTI2026

Utilizing Time Reversal Ultrasonics to Detect the Removal of Nuclear Materials from Geological Repositories (FY26 Mid-Year)

Detecting unauthorized nuclear material removal from storage environments, such as geological repositories, is a critical safeguards task essential to ensuring the integrity and non-diversion of nuclear materials. However, this process is fraught with significant technical challenges. Storage configurations often involve tightly packed nuclear material containers or obstructed environments, making detection of removal events exceedingly difficult. Optical surveillance cameras, which are commonly used for monitoring, suffer from substantial limitations, including restricted coverage, reliance on line-of-sight measurements, and vulnerability to environmental conditions in certain storage scenarios. As the global inventory of monitored nuclear materials increases and storage configurations become more complex— such as deep geological repositories, inaccessible storage vaults, and tightly packed containers—there is an urgent need for innovative detection technologies that can reliably identify unauthorized diversion events in these challenging environments. The challenge of detecting nuclear material removal in complex storage environments is both significant and urgent. Preventing unauthorized access, diversion, or tampering with nuclear materials is a cornerstone of global nuclear safeguards and nonproliferation efforts. Current detection methods are increasingly inadequate as storage configurations become more intricate and inaccessible. The limitations of existing technologies—such as their inability to detect changes behind obstructions, reliance on costly and labor-intensive processes, and vulnerability to environmental conditions—pose risks to the effectiveness of safeguards systems. Addressing this challenge is critical to maintaining international trust in nuclear safeguards frameworks and ensuring compliance with nonproliferation agreements. Our project builds on the proven concept of TRU technology that can address this unmet need. TRU has demonstrated exceptional spatial sensitivity and change detection capabilities in complex non-line-ofsight environments, making it uniquely suited for detecting unauthorized nuclear material removal in challenging storage configurations. Unlike optical methods, TRU is not limited by line-of-sight constraints or environmental conditions, enabling reliable detection of subtle alterations even behind obstructions. By leveraging TRU’s ability to identify removal or tampering events, we aim to develop a robust detection system that enhances safeguards in geological repositories, storage vaults, and other complex environments.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms

The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints. In this work, we develop an algorithm that decomposes the QAOA input problem graph into a smaller problem and solves MaxCut using QAOA on the reduced graph. The algorithm requires a subroutine that can be classical or quantum—in this work, we implement the algorithm twice on each graph. One implementation uses the classical solver Gurobi in the subroutine and the other uses QAOA. We solve these reduced problems with QAOA. On average, the reduced problems require only approximately 1/10 of the number of vertices than the original MaxCut instances. Furthermore, the average approximation ratio of the original MaxCut problems is 0.75, while the approximation ratios of the decomposed graphs are on average of 0.96 for both Gurobi and QAOA. With this decomposition, we are able to measure optimal solutions for ten 100-vertex graphs by running single-layer QAOA circuits on the Quantinuum trapped-ion quantum computer H1-1, sampling each circuit only 500 times. This approach is best suited for sparse, particularly k-regular graphs, as k-regular graphs on n vertices can be decomposed into a graph with at most $\frac{nk}{k+1}$ vertices in polynomial time. Further reductions can be obtained with a potential trade-off in computational time. In conclusion, while this paper applies the decomposition method to the MaxCut problem, it can be applied to more general classes of combinatorial optimization problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploring the role of 𝑑* hexaquarks on quark deconfinement and hybrid stars

We investigate the impact of the d*( 2380) hexaquark on the equation of state (EOS) of dense matter within hybrid stars (HSs) using the chiral mean-field model (CMF). The hexaquark is included as a new degree of freedom in the hadronic phase, and its influence on the deconfinement transition to quark matter is explored. We reparametrize the CMF model to ensure compatibility with recent astrophysical constraints, including the observation of massive pulsars and gravitational wave events. Our results show that the presence of d* significantly modifies the EOS, leading to a softening at high densities and a consequent reduction in the predicted maximum stellar masses. Furthermore, we examine the possibility of a first-order deconfinement phase transition within the context of the extended stability branch of slow stable HSs (SSHSs). We find that the presence of hexaquarks can delay the deconfinement phase transition and reduce the associated energy density gap, affecting the structure and stability of HSs. Our results suggest that, as the hexaquark appearance tends to destabilize stellar configurations, fine-tuning of model parameters is required to obtain both the presence of hexaquarks and quark deconfinement in these systems. In this scenario, the SSHS branch plays a crucial role in obtaining HSs with hexaquarks that satisfy current astrophysical constraints. Our work provides new insights into the role of exotic particles like d* in dense matter and the complex interplay between hadronic and quark degrees of freedom inside compact stellar objects.

Nuclear astrophysics↗
DOE OSTI2025

Emulator-based Bayesian calibration of a subglacial drainage model

Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.

58 GEOSCIENCES↗
DOE OSTI2026

Hydrogen applications in airport operations: a review using the Port Authority of New York and New Jersey as an illustrative airport system

Airports combine aircraft propulsion, ground operations, stationary power systems, and fuel logistics in ways that make emissions reduction technically and operationally complex. Existing studies often assess hydrogen applications in these areas separately, limiting understanding of the shared infrastructure, safety, and operational constraints that shape airport deployment. This review evaluates hydrogen across three airport-relevant operational domains: aviation propulsion, ground support equipment and vehicles, and stationary power systems. Within aviation propulsion, the review examines sustainable aviation fuel production and hydrogen-powered aircraft as two distinct hydrogen-relevant pathways. The Port Authority of New York and New Jersey is used as an illustrative airport system to relate the literature to a real operating context. Drawing on peer-reviewed studies, technical reports, demonstration projects, and public operational information, the review also includes screening-level calculations of hydrogen demand and potential CO 2 e reductions for selected applications. The findings show that hydrogen's role is highly application-specific. Near-term opportunities are strongest where hydrogen serves as a low-carbon process input, supports selected high-utilization ground equipment, or contributes to resilient stationary power-system configurations. Hydrogen-powered aircraft remain a longer-term option because storage, fueling infrastructure, certification, cost, and NO x management continue to constrain deployment. Across all domains, infrastructure readiness, fuel logistics, safety requirements, and leakage management emerge as recurring determinants of viability. Future research should focus on cross-domain infrastructure planning, comparative assessment of hydrogen against alternative pathways, improved treatment of leakage and non-CO 2 effects, and clearer safety and regulatory frameworks for airport deployment.

08 HYDROGEN↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2026

Micro-architected material design for mechanical response

Rapid advances in additive manufacturing (AM) have enabled the creation of micro-architected materials—also known as mechanical metamaterials—with unprecedented control over fine-scale geometries and arrangements of multiple material constituents. These “materials” can achieve unique and extraordinary effective mechanical properties through their complex architectures rather than composition alone. A key challenge is to design for these bespoke effective mechanical responses within the constraints of available AM techniques (i.e., given a set of desired effective properties), identify a (often nonunique) micro-architecture and selection of material constituents that achieves them. Two main strategies have emerged. Gradient-based methods use sensitivity analysis to iteratively refine candidate designs, while data-driven methods learn micro-architecture-constituent relationships from existing examples to propose new designs. This article reviews these design approaches for micro-architected materials with tailored mechanical responses that can be fabricated by AM as well as their applications.

Spadaccini, Christopher M [Lawrence Livermore Nati↗
DOE OSTI2026

Energy Adequacy Studies: Nodal vs Zonal and Ramp-Rate Representation

This work investigates limitations in current energy adequacy studies, particularly concerning the representation of grid complexities. We highlight how traditional zonal modeling approaches, which simplify large geographic areas and generator capabilities, can mask critical transmission constraints and localized resource shortfalls. By analyzing nodal versus zonal pricing differences and the empirical distribution of generator ramp-rates, we demonstrate that these simplified assumptions may misrepresent system flexibility and deliverability. Our findings underscore the need for more granular, nodal-level analyses and data-driven characterizations of generation assets to provide a more accurate and robust assessment of energy reliability and adequacy, ultimately supporting a more resilient and efficient energy infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Spatiotemporal development of expanding bacterial colonies driven by emergent mechanical constraints and nutrient gradients

Abstract Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process forEscherichia colicells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes.

Science & Technology - Other Topics↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2026

Regularization via f -Divergence: An Application to Multi-Oxide Spectroscopic Analysis

In this paper, we explore the application of convolutional neural networks (CNNs) for predicting the chemical composition of complex geologic samples in a simulated Martian atmospheric environment. Specifically, we aim to characterize oxide weight percentages (wt.%) of rock samples analyzed by remote Laser-Induced Breakdown Spectroscopy (LIBS), framing the problem as a multi-target regression task . Neural networks trained on LIBS spectra are prone to overfitting due to high spectral complexity, limited labeled data, and measurement noise. While regularization is critical for improving generalization, common methods (e.g., ℓ 2 regularization) impose constraints not directly tied to data distribution properties. We propose a novel regularization method based on a specific ƒ-divergence induced by a graph-based estimator, designed to constrain the distributional discrepancy between predictions and targets. This regularizer serves a dual purpose: (a) mitigating overfitting by enforcing a constraint on the distributional difference between predictions and noisy targets, and (b) acting as an auxiliary loss that penalizes large divergences. To enable backpropagation, we develop a differentiable approximation of this particular ƒ-divergence, making the method feasible for neural networks. Experiments on ChemCam and SuperCam LIBS calibration spectra show that mathematical equation-divergence regularization outperforms or matches standard regularization methods (ℓ 1 , ℓ 2 , dropout) and the classical baseline, partial least squares (PLS). Combining ƒ-divergence regularization with standard regularization yields further performance gains, indicating that distributional regularization is useful in this context giving a promising direction for robust model training in planetary science applications. Source code is publicly available at Klein and Li (2025), https://doi.org/10.11578/dc.20250530.7.

58 GEOSCIENCES↗
DOE OSTI2025

Quantitative dissection of Agrobacterium T-DNA expression in single plant cells reveals density-dependent synergy and antagonism

Agrobacterium pathogenesis, which involves transferring T-DNA into plant cells, is the cornerstone of plant genetic engineering. As the applications that rely on Agrobacterium increase in sophistication, it becomes critical to achieve a quantitative and predictive understanding of T-DNA expression at the level of single plant cells. Here we examine if a classic Poisson model of interactions between pathogens and host cells holds true for Agrobacterium infecting Nicotiana benthamiana. Systematically challenging this model revealed antagonistic and synergistic density-dependent interactions between bacteria that do not require quorum sensing. Using various approaches, we studied the molecular basis of these interactions. To overcome the engineering constraints imposed by antagonism, we created a dual binary vector system termed ‘BiBi’, which can improve the efficiency of a reconstituted complex metabolic pathway in a predictive fashion. Our findings illustrate how combining theoretical models with quantitative experiments can reveal new principles of bacterial pathogenesis, impacting both fundamental and applied plant biology.

Alamos, Simon↗
DOE OSTI2026

A flexible and differentiable coil proxy for stellarator equilibrium optimization

Balancing plasma performance and coil cost is a significant challenge when designing a stellarator power plant. Most current stellarator designs are produced through two-stage optimization: stage-1 for the equilibrium and stage-2 for a coil design that reproduces its magnetic configuration. Because few proxies connect both stages, two-stage optimization can produce plasmas that have high-quality physical properties but overly complex coils. In recent years, single-stage optimization has increasingly been used to optimize the plasma and coils simultaneously in order to improve the plasma–coil balance. However, all existing single-stage tools are specialized for filament coils, cannot model coil systems containing permanent magnets or dipole arrays, and continue to be challenged by numerical problems. The quasi-single-stage optimization finds a middle ground by integrating a coil optimization subproblem into stage-1 optimization. We present a flexible, differentiable coil complexity proxy based on the newly developed QUADCOIL coil optimization code. QUADCOIL is fast and can target realistic coil metrics and constraints that are unavailable to codes with comparable speed. We demonstrate the effectiveness and flexibility of the QUADCOIL proxy by presenting two QSS optimization studies. The first study produces a permanent magnet solution for the MUSE stellarator with 34% fewer magnets than previous solutions. The second study produces a coil solution for the ARIES-CS stellarator with 30% reductions in both peak and root-mean-square force.

Fu, Lanke [Princeton Plasma Physics Laboratory (PP↗
DOE OSTI2025

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time-of-flight matrix-assisted laser desorption/ionization, and time-of-flight secondary ion mass spectrometry imaging techniques. By leveraging FT-ICR's unparalleled spectral resolution and ToF-SIMS's exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

canonical correlation analysis↗
DOE OSTI2025

Validation Testing for Molten Chloride Reactor Experiment Equipment Removal and Disposal Techniques

The Molten Chloride Reactor Experiment (MCRE) will be the first reactor featuring a fast-spectrum molten chloride circulating nuclear fuel system in the world. Planning for equipment removal and disposal (ERD) of MCRE has identified several technology gaps due to the unique environment of this nuclear experiment. Some of the gaps arise from the application of existing disassembly and/or sizing methods to novel material forms or in novel configurations. Others arise from unknown material behavior. This paper summarizes proposed test plans for ERD validation experiments to address these complicated or unknown equipment removal procedures. At the Waste Management Symposia in 2024, the Idaho National Laboratory (INL) MCRE ERD team presented the challenges associated with hosting multiple nuclear experiments in series with only brief transition periods between systems. Such difficulties include higher dose rates, the presence of radioisotopes infrequently encountered in reactor decommissioning and radioactive waste management, lack of intrinsic remote-operations infrastructure in the test bed, space constraints in the test bed, and contamination minimization requirements. To address these challenges, remote or semi-remote technologies are planned to be implemented in a non-hot cell environment with limited space availability. The team also discussed how a systems engineering approach is being used for conceptual development and design of equipment removal systems to address these challenges. For example, to reduce constraints for the removal of more difficult components, non-activated, noncontaminated elements are planned to be taken out first where possible. Still, there are complexities associated with the remaining components. In this work, the operational framework for MCRE ERD was reviewed for technical gaps and open questions, and test plans were drafted to address these areas. The tests plans were written for the following categories: vision systems, pipe cutting, drill/grout/filler, flush salt, and miscellaneous, with the miscellaneous group consisting of tests like techniques for removing bearings and reflector bricks. The test plans explore material, infrastructure, and staffing requirements needed for test execution. The test plans additionally focus on the evaluation of success. Determining the outcome of a test is imperative - as these explorative actions have the potential to rearrange or re-scope planned ERD activities. Success criteria identified thus far include required tool output, required area(s), debris production and mitigation, and repeatability. Test plans are an essential aspect of the systems engineering approach to MCRE ERD. They are used as the beginning steps in defining use cases for the ERD system. Performance of the validation tests is expected to begin in the summer of 2025 and will take approximately 9 to 12 months to complete. Execution of these plans will be expedited by specifying test needs ahead of time, facilitating efficient interactions with any subcontractors tasked with running the requested tests. Evaluating the outcomes of these tests will inform MCRE ERD procedures and timing and will also identify additional technical constraints for the MCRE ERD System. This upfront process optimization effort will help the project save time and resources at the end of the experiment.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

NASA’s Pandora SmallSat Mission: Simulating the Impact of Stellar Photospheric Heterogeneity and Its Correction

Stellar photospheric heterogeneity is a dominant astrophysical systematic impacting exoplanet transmission spectroscopy. NASA’s Pandora SmallSat Mission is designed to address this challenge through contemporaneous visible-band photometry and near-infrared spectroscopy of exoplanet host stars. Here, we present an end-to-end simulation study quantifying Pandora’s ability to infer stellar photospheric properties and correct stellar contamination using out-of-transit observations. We construct eight representative stellar activity scenarios and generate 160 simulated Pandora datasets, incorporating time-dependent stellar spectra, instrument response, and noise. Given accurate models, Bayesian retrievals of joint visible photometry (0.4–0.7 μm) and near-infrared spectroscopy (0.9–1.6 μm, R ≈ 120) recover photospheric temperatures with typical uncertainties of ≈30 K, with no significant bias. Models with two spectral components (i.e., a quiescent photosphere and spots) are strongly favored in 95% of cases; one-component models are preferred when true spot filling factors fall below a detection threshold of ≈0.3%. We propagate the true and inferred stellar parameters to compute true, inferred, and residual contamination signals under physically motivated spot geometries. For simple spot distributions, contamination signals of 10 2 –10 3 ppm are reduced to ≲10 ppm—well below Pandora’s expected transmission spectroscopy precision (30–100 ppm). For more complex spot distributions, geometric degeneracies limit deterministic corrections, leaving residual contamination at the 10 3 ppm level that must be mitigated using additional constraints, such as spot-crossing events and joint stellar–planetary retrievals of transmission spectra. These results define regimes in which stellar contamination can be corrected from stellar observations alone and show how Pandora stellar observations can identify cases where additional information is required.

Astronomy and AstroPhysics↗