Although solar imaging spectroscopy has been widely used to study the Sun, the inherent rastering process in conventional slit spectrographs required to generate spatial-spectral images limits their capability for studying dynamic events. In contrast, slitless imaging spectrographs can enable cotemporal imaging and spectroscopy by removing the slit and the rastering process, at the expense of producing overlapped spectra on the observations, implying the necessity for the solution of the resulting inverse problem. In this work, we develop a deep learning approach to disentangle the overlapped spectra for a slitless spectrograph design with a narrow bandpass in extreme ultraviolet (EUV) and detectors placed at multiple orders. The bandpass is designed to isolate a dominant spectral line, and our method recovers its three Gaussian parameters (intensity, Doppler velocity, and line width) from the multiorder observations. We train a convolutional neural network as a mapping from the spectrograph observations to the spectral line parameters in a supervised manner, using a large dataset derived from Hinode EUV Imaging Spectrometer (EIS) observations. We compare the proposed method to a widely used tomographic reconstruction approach, MART, and a recently proposed parametric inversion approach. Experiments on various observational settings show that the proposed method substantially outperforms both baselines in intensity and velocity reconstruction (velocity errors reduced by more than a factor of 2 relative to the closest baseline), and is the only method to achieve meaningful line width recovery. Reconstruction is completed in under a second, orders of magnitude faster than the alternative methods.

Imaging spectroscopy is a fundamental technique for investigating various physical phenomena. Traditional imaging spectrographs require a time-consuming scanning process to construct the spatial-spectral data cube, rendering them unsuitable for dynamic scenes. Slitless spectrographs have been proposed as snapshot imagers; however, their measurements consist of overlapped spectra, requiring to solve a limited-angle tomography problem. The existing reconstruction approaches have limited accuracy due to the highly ill-posed nature of the problem. In this work, we adapt the diffusion posterior sampling (DPS) method which uses a trained diffusion model as a prior. Targeting applications where spectrum consists of discrete emissions, we use a Gaussian parametrization for the spectrum, and use DPS to solve the resulting nonlinear inverse problem to estimate the Gaussian line parameters at each spatial location. Effectiveness of the approach is demonstrated on a solar imaging application.

Incoherent scatter radar probing of the topside F-region ionosphere with reduced electron densities residing at relatively large radar ranges requires the use of uncoded long pulse (ULP) transmissions. In these ULP measurements the range resolution is necessarily poor, rendering the inference of ionospheric state parameters such as composition, temperature, and drift velocity challenging. Specifically, accurate estimation of composition and temperature is crucial for determining the differential velocity measurements of topside constituents; a topic that is addressed in further detail in a companion paper. In this paper, we describe approaches to accurate and efficient estimation of plasma composition and temperature using long pulses. The first approach is based on the deconvolution of the lag profile matrix generated from ULP measurements, aiming to mitigate the range mixing effect due to the low range resolution. By exploiting the singular value decomposition of the range mixing ambiguity matrix, a data-independent strategy is used to determine the regularization parameter for the deconvolution. The second approach is based on a correction of the lag-dependent height offset errors by re-aligning each column of the lag profile matrix to its proper height. These strategies are tested and compared through a synthetic test and validated with experimental data collected from Arecibo over a 63-hr-long observation interval in September 2016. Results show that these strategies are effective in mitigating the range mixing inherent in estimating ionospheric state parameters by spectral fitting.

Direct imaging simulations of starshades and other proposed mission concepts are needed to characterize planet detection performance and inform mission design trades. In order to assess the complementary role of a 60 m starshade for the Habitable Worlds Observatory (HWO), we develop the optical model of a starshade and simulate solar system imaging at 0 degrees and 60 degrees inclinations. The optical core throughput of a direct-imaging system is a key metric that governs exposure time and the potential exoplanetary yield of a mission. We use our optical model to evaluate core throughput, incorporating 6 m segmented and obscured telescope apertures, over the visible to near-infrared wavelength band (500-1000 nm). Accurate diffractive optical simulations of this form require many large Fourier transforms, with prohibitive run times, as both the starshade mask and telescope aperture require fine-scale spatial sampling. We introduce a Fourier sampling technique, the Bluestein fast Fourier transform (B-FFT), to efficiently simulate diffractive optics and enable high-fidelity simulations of core throughput. By characterizing sampling requirements and comparing B-FFTs computational complexity to standard Fourier methods (e.g., DFT, FFT), we demonstrate its efficiency in our optical pipeline. These methods are implemented in PyStarshade, an open-source Python package offering flexible diffraction tools and imaging simulations for starshades. Our results show the HWO starshade used with a segmented off-axis telescope aperture achieves an optimal core throughput measured within a photometric aperture of radius 0.7 lambda/D of 68%. With an additionally obscured aperture, a 66% core throughput is achieved.

Imaging submesoscale atmospheric dynamics-features on spatial scales of kilometers to tens of kilometers-is essential for understanding key processes in the upper atmosphere, yet it remains a persistent challenge due to the implicit stationary assumption in conventional radar imaging techniques. Emerging radar systems such as MAARSY-3-D and EISCAT-3-D, along with advancements in multiple-input multiple-output (MIMO) radar technology, promise unprecedented spatial resolution. Yet, traditional methods still suffer from motion-induced blurring, which critically degrades features at these scales. To address this limitation, we propose a dynamic ensemble-based method for imaging (DEMI), a computational imaging approach that treats the atmosphere as a dynamic system and employs the ensemble Kalman filter (EnKF) to sequentially reconstruct time-varying reflectivity fields. Unlike static techniques, conventionally applied in radar imaging, such as Capon or maximum entropy (MaxEnt), DEMI explicitly accounts for target motion, significantly reducing motion blur. This dynamic modeling framework provides clearer, more accurate depictions of submesoscale structures while remaining computationally efficient through adaptive filtering. Results from both simulations and experimental data demonstrate that DEMI substantially improves spatial resolution and image quality, opening new opportunities to study previously unresolved submesoscale dynamics and their broader implications in the upper atmosphere.

The Carruthers Geocorona Observatory, launched in September 2025, is NASA's first mission devoted to investigating the fundamental nature of Earth's exosphere from its distant vantage in halo orbit around the Earth-Sun Lagrange 1 (L1) point. Its primary payload, the GeoCoronal Imager, consists of two coaligned photometric imagers that measure ultraviolet Lyman-alpha emission radiance from exospheric hydrogen simultaneously at wide- and narrow- fields of view. These observations will map the exosphere's global spatial structure and observe its temporal variability in response to geomagnetic storms. However, a critical step in that analysis is isolating the in-band exospheric hydrogen Lyman-alpha signal from any other source of photons, including in-band InterPlanetary Hydrogen photon background and out-of-band photon backgrounds, which are emitted from Earth's limb. This paper details the algorithms used to retrieve and remove photon backgrounds from the GCI science images acquired on-orbit. Finally, the science data processing pipeline that transforms instrument-effect corrected images (L1B science data product) into absolutely-calibrated exospheric H measurements in physical units (L1C science data product) is detailed. Evaluation of algorithm performance based on a realistic pre-flight case study using synthetic data demonstrates that these photon background removal algorithms achieve high accuracy, leaving a residual systematic bias in isolated exospheric Lyman-alpha of only 3

In this work, we apply an exploratory joint Bayesian transit detector, previously evaluated using Kepler data, to the 2 minutes simple aperture photometry light-curve data in the continuous viewing zone for the Transiting Exoplanet Survey Satellite (TESS) over 3 yr of observation. The detector uses Bayesian priors, adaptively estimated, to model unknown systematic noise and stellar variability incorporated in a Neyman–Pearson likelihood ratio test for a candidate transit signal; a primary goal of the algorithm is to reduce overfitting. The detector was adapted to the TESS data and refined to improve outlier rejection and suppress FA detections in postprocessing. The statistical performance of the detector was evaluated using transit injection tests, where the joint Bayesian detector achieves an 80.0% detection rate and a 19.1% quasi-false-alarm rate at a detection threshold τ = 10; this is a marginal, although not statistically significant, improvement of 0.2% over a reference sequential detrending and detection algorithm. In addition, a full search of the input TESS data was performed to evaluate the recovery rate of known TESS Objects of Interest (TOIs) and to perform an independent search for new exoplanet candidates. The joint detector has a 73% recall rate and a 63% detection rate for known TOIs; the former considers a match against all detection statistics above threshold, while the latter considers only the maximum detection statistic.

A switching linear dynamic system (SLDS) represents a system wherein the state evolution model randomly switches between several operation modes over the observation interval. Bayesian, recursive state estimation in an SLDS is performed by running a bank of standard Kalman filters (KF) at each time step in the form of switching KF (SKF). In this paper we develop the aggregated Kalman filter (AKF), which produces a computationally efficient approximation to the SKF estimator by merging the prior conditional distributions calculated in the prediction step of SKF in terms of Kullback-Leibrer (KL) divergence, and subsequently performs the update step for a single Gaussian distribution. Considering some practical approximations, the AKF is shown to be the optimal estimator in the mean squared error (MSE) sense for the class of linear filters given the posterior probability of the modes, i.e., it is the projection of the optimal estimator onto the space of linear estimators. Quantifying the induced mean squared error by AKF projection is in general only possible via numerical integration, but its closed form is obtained by approximating operating modes’ conditional probabilities with deterministic quantities. The quantification of this approximation error provides insight into the impact of parameters of the SLDS model on the induced approximation error due to aggregation, which we then verify through experiments. We show that AKF provides significant computational savings over the SKF while maintaining near identical performance.

A dynamic system may vary with time via dependencies of its underlying parameters on exogenous variables. Learning about if/how variation of an exogenous variable affects the dynamic system based on observational data is important for analysis, prediction, and control of the dynamic system. This work proposes a method to quantify the impact of exogenous drivers of a dynamic system given observations from the system’s state variables. This is accomplished by performing a modal decomposition of the dynamic operator and quantifying modal similarities. Numerical experiments demonstrate successful application of the proposed method to identify causal drivers of systems governed by common differential equations, i.e, diffusion process and oscillatory models. The proposed method enables investigating the dependence of decay rate, oscillation frequency, and spatial modes of the dynamic system on the exogenous drivers.

Light curves produced by wide-field exoplanet transit surveys such as CoRoT, Kepler, and the Transiting Exoplanet Survey Satellite are affected by sensor-wide systematic noise, which is correlated both spatiotemporally and with other instrumental parameters such as the photometric magnitude. Robust and effective systematics mitigation is necessary to achieve the level of photometric accuracy required to detect exoplanet transits and to faithfully recover other forms of intrinsic astrophysical variability. We demonstrate the feasibility of a new exploratory algorithm to remove spatially correlated systematic noise and detrend light curves obtained from wide-field transit surveys. This spatial systematics algorithm is data-driven and fits a low-rank linear model for the systematics conditioned on a total-variation spatial constraint. The total-variation constraint models spatial systematic structure across the sensor on a foundational level. The fit is performed using gradient descent applied to, a variable reduced least-squares penalty and a modified form of total-variation prior; both the systematics basis vectors and their weighting coefficients are iteratively varied. The algorithm was numerically evaluated against a reference principal component analysis, using both signal injection on a selected Kepler dataset, as well as full simulations within the same Kepler coordinate framework. We develop our algorithm to reduce the overfitting of astrophysical variability over longer signal timescales (days) while performing comparably relative to the reference method for exoplanet transit timescales. The algorithm performance and application are assessed, and future development is outlined.

This work introduces a method to enable accurate forecasting of time series governed by ordinary differential equations (ODE) through the usage of cost functions explicitly dependent on the future trajectory rather than the past measurement times. We prove that the space of solutions of an $N$-dimensional, smooth, Lipschitz ODE on any given finite time horizon is an $N$-dimensional Riemannian manifold embedded in the space of square integrable continuous functions. This finite dimensional manifold structure enables the application of common statistical objectives such as maximum likelihood (ML), maximum a posteriori (MAP), and minimum mean squared error (MMSE) estimation directly in the space of feasible ODE solutions. The restriction to feasible trajectories of the system limits known issues such as oversmoothing seen in unconstrained MMSE forecasting. We demonstrate that direct optimization of trajectories reduces error in forecasting when compared to estimating initial conditions or minimizing empirical error. Beyond theoretical justifications, we provide Monte Carlo simulations evaluating the performance of the optimal solutions of six different objective functions: ML, MAP state estimation, MMSE state estimation, MAP trajectory estimation, MMSE trajectory estimation over all square integrable functions, and MMSE trajectory estimation over solutions of the differential equation.

Accurate determination of the ionospheric parameters is one of the important objectives of the Ionospheric Connection Explorer (ICON) mission. Recent analyses of the current ICON Level 2.5 (L2.5) data product have shown that the ionospheric parameters (e.g., the peak electron density, n_mF_2 , and the peak height, h_mF_2 ) that are retrieved from the nighttime OI 135.6 nm emission observed by ICON’s Far Ultraviolet (FUV) imager exhibit a systematic bias when compared to external radio measurements. In this study, we demonstrate that the bias was introduced by Tikhonov regularization that was used for the FUV Level 1 data inversion to generate the L2.5 data product. To address the bias, we develop a Bayesian framework for accurate determination of the nighttime ionospheric parameters through the Maximum A Posteriori (MAP) estimation. We show through analysis of synthetic observations that the key to an accurate MAP estimation is to construct a series of prior distributions associated with different h_mF_2 using climatological empirical models. Implementation of the MAP estimation with this series of prior distributions to the ICON FUV observations and comparison of the ionospheric retrievals with external radio measurements verify that the Bayesian method can reduce the systematic bias to a negligible level of ∼1 n_mF_2 and ∼1 km in the retrieved h_mF_2 . Our study provides a novel method for FUV remote sensing data analysis and an improved data set for ionospheric research.

The Virtual Super-resolution Optics with Reconfigurable Swarms (VISORS) is a National Science Foundation (NSF) space physics mission which will detect and study fundamental energy-release regions in the solar corona. The VISORS mission will image extreme ultraviolet (EUV) features on the Sun at a resolution of at least 0.2 arcseconds from Low Earth Orbit (LEO). To accomplish this objective, VISORS will use a pair of formation flying 6U CubeSats: one of which carries the observatory optics while the other contains the detector instrument. VISORS will serve as a proof of concept for this distributed instrument approach by obtaining at least one 10-second exposure image during its six-month mission lifetime. Meeting the strict relative orbit requirements during science observations will demonstrate several technologies key to precise formation flying including intersatellite link, relative navigation, and autonomous maneuver planning. To satisfy these stringent mission requirements, a concept of operations has been established that requires maneuvering between a standby orbit where housekeeping tasks are performed and an actively maintained science orbit where observations are conducted. Formation acquisition, re-acquisition, fault recovery, and escape operations are also planned. This paper provides a description of the VISORS formation flying concept of operations: explaining the function and rationale of each operation mode, how these modes are designed, and how they collectively meet the mission requirements. Specific challenges and mission trades related to performing precision formation flight with CubeSats are discussed. A Failure Mode Effects and Criticality Analysis (FMECA) is conducted to assess the risk of collision under the most probable fault scenarios, which is used to inform the development of operational mitigation strategies and on-board fault tolerant collision avoidance (COLA) logic.

This work introduces a method to select linear functional measurements of a vector-valued time series optimized for forecasting distant time-horizons. By formulating and solving the problem of sequential linear measurement design as an infinite-horizon problem with the time-averaged trace of the Cramér-Rao lower bound (CRLB) for forecasting as the cost, the most informative data can be collected irrespective of the eventual forecasting algorithm. By introducing theoretical results regarding measurements under additive noise from natural exponential families, we construct an equivalent problem from which a local dimensionality reduction can be derived. This alternative formulation is based on the future collapse of dimensionality inherent in the limiting behavior of many differential equations and can be directly observed in the low-rank structure of the CRLB for forecasting. Implementations of both an approximate dynamic programming formulation and the proposed alternative are illustrated using an extended Kalman filter for state estimation, with results on simulated systems with limit cycles and chaotic behavior demonstrating a linear improvement in the CRLB as a function of the number of collapsing dimensions of the system.

We propose a method for the Bayesian prediction of shocks in scalar partial differential equations (PDEs) representing conservation equations from noisy observations of the boundary conditions. By considering the implicit transformation from boundary conditions to shocks, we construct an arrival process interpretation of shocks as well as an associated arrival rate function. We then introduce a Monte Carlo method to approximate the arrival rate of shocks based on the probability of a sufficiently large range of values in an epsilon ball conditioned on noisy boundary measurements. We illustrate the method with simulations of Burgers' equation with initial conditions set by Brownian motion. Despite the non-smooth boundary, our proposed method constructs a sparse and readily interpretable probabilistic structure of shock arrival and propagation.

We propose a method for tracking linear representations of a nonlinear dynamic system with time-varying parameters based on a continuous representation of its switching linear dynamic system (SLDS) model. Given approximate linear representations for a finite set of unknown intrinsic parameters of the dynamics, a combination of autoencoder-based dimensionality reduction and cubic curve-fitting are applied to learn the continuous manifold of dynamics embedded in the evolution operator. This representation enables a significant reduction of the squared Frobenius norm of error in maximum likelihood (ML) system identification relative to that of the original SLDS model. Numerical experiments also verify this result.

The NASA Ionospheric Connection Explorer (ICON) was launched in October 2019 and has been observing the upper atmosphere and ionosphere to understand the sources of their strong variability, to understand the energy and momentum transfer, and to determine how the solar wind and magnetospheric effects modify the internally-driven atmosphere-space system. The Far Ultraviolet Instrument (FUV) supports these goals by observing the ultraviolet airglow in day and night, determining the atmospheric and ionospheric composition and density distribution. Based on the combination of ground calibration and flight data, this paper describes how major instrument parameters have been verified or refined since launch, how science data are collected, and how the instrument has performed over the first 3 years of the science mission. It also provides a brief summary of science results obtained so far.

The Far Ultraviolet Imaging Spectrograph (FUV) onboard the NASA-ICON spacecraft has been providing nighttime O+ density profiles over mid- and low-latitude since December 2019. These profiles are compared to electron density profiles provided by GNSS radiooccultations and ground-based ionosondes, mainly at the F-peak level where both density and height are compared. This work is an important update of the earlier study published by Wautelet et al. (J. Geophys. Res. Space Phys. 126(11):e2021JA029360, 2021) for two reasons: First, several methodological improvements have been implemented at the calibration and inversion levels. Second, the present work relies on an extended time range, ranging from December 2019 to August 2022, covering therefore periods of increased solar activity, which was not the case for the previous work. It is found that the peak density and height are, on average, similar to radio-based observations by about 10% in density and 7 km in height, meaning that FUV provides peak characteristics compatible with existing ionospheric datasets based on radio signals. However, comparisons of FUV and radio-occultation profiles have to be considered very carefully due to the potentially large difference in the observation geometry, which can account for large density discrepancies even between profiles being closely located and mostly simultaneous. This is particularly important around the crests of the equatorial anomaly where the largest density discrepancies have been observed. In addition, this study highlights the variability of the FUV profiles at the bottomside level, with the analysis of cases where rather large density values were observed while small density values are expected. The latter observation does nevertheless not impact the statis-tics concerning the F-peak characteristics, which show that FUV reliably monitors the peak density and height with an accuracy compatible to that of external data sources.

Why the Sun has a tenuous upper atmosphere some 1000 times hotter than the photosphere is a fundamental open problem in space plasma physics despite decades of study.A leading hypothesis, supported by indirect evidence, is that in most of the corona heating is confined to narrow current sheets in which energy is dissipated despite the low large-scale resistivity of the coronal plasma.Although the kinetic scales of reconnection or wave heating are beyond remote observation, thermal structure on scales ≲100 km is expected to be produced by the primary heating mechanisms.This white paper considers what could be learned from direct observations of coronal plasma on those scales and from observationally connecting microscales to larger scales and the formation of the solar wind.The paper concludes by outlining a mission concept that is more fully described in a Heliophysics Mission Concept Study for the Coronal Microscale Observatory.