arXiv subjects

Farzad Kamalabadi

Publications and source records attributed to Farzad Kamalabadi.

14 recordsLinked to original sources

arXiv2026

Carruthers Data Processing Pipeline: Photon Background Removal

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% under beginning-of-life conditions.

astro-ph.IM↗

arXiv2026

Solar Wind Classifications at Mars using Machine Learning Techniques

Understanding solar wind variability throughout the heliosphere is essential for fundamental space physics and future exploration of the Moon and Mars. The Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft has provided upstream solar wind measurements at Mars spanning Solar Cycles 24 and 25, enabling a statistical investigation of solar wind regimes at this heliocentric distance. In this work, we apply an unsupervised machine-learning framework combining Principal Component Analysis and K-Means clustering to a normalized, multi-dimensional solar wind dataset to identify recurrent solar wind regimes in a physically interpretable, data-driven manner. The resulting classification reveals distinct slow, fast, intermediate, and compressed solar wind regimes whose relative occurrence and temporal organization are strongly modulated by solar activity. This manuscript is part of the Heliophysics Summer School Machine Learning Special Collection.

physics.space-ph↗

arXiv2025

Efficient Exoplanet Imaging Simulations of the Habitable Worlds Observatory

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 (BFFT), to efficiently simulate diffractive optics and enable high-fidelity simulations of core throughput. By characterizing sampling requirements and comparing BFFT's computational complexity to standard Fourier methods (for example, DFT and FFT), we demonstrate its efficiency in our optical pipeline. These methods are implemented in PyStarshade (Taaki 2025), 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 percent. With an additionally obscured aperture, a 66 percent core throughput is achieved.

astro-ph.IM↗

arXiv2025

A Search for Exoplanet Candidates in TESS 2-min Light Curves using Joint Bayesian Detection

In this work, we apply an exploratory joint Bayesian transit detector (Taaki et al. 2020), previously evaluated using Kepler data, to the 2 min simple aperture photometry light curve data in the continuous viewing zone for the Transiting Exoplanet Survey Satellite (TESS) over three years 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 false alarm detections in post-processing. 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 (TOI) 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 TOI; the former considers a match against all detection statistics above threshold while the latter considers only the maximum detection statistic.

astro-ph.EP↗

arXiv2023

Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-Rank Methods

Light curves produced by wide-field exoplanet transit surveys such as CoRoT, Kepler, and TESS are affected by sensor-wide systematic noise which is correlated both spatiotemporally and with other instrumental parameters such as 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 find our algorithm to reduce 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 is assessed and future development outlined.

astro-ph.EP↗

arXiv2023

Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting

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.

cs.LG↗

arXiv2022

Efficient model selection in switching linear dynamic systems by graph clustering

The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes. In this paper, a computationally tractable graph representation is proposed for a switching linear dynamic system (SLDS) along with the solution of a minimum-sum optimization problem for clustering to reduce the switching mode cardinality offline, before collecting measurements. It is shown that upon perfect mode detection, the induced error caused by mode clustering can be quantified exactly in terms of the dissimilarity measures in the proposed graph structure. Numerical results verify that clustering based on the proposed framework effectively reduces model complexity given uncertain mode detection and that the induced error can be well approximated if the underlying assumptions are satisfied.

eess.SP↗

arXiv2022

Low SNR Multiframe Registration for Cubesats

We present a registration algorithm which jointly estimates motion and the ground truth image from a set of noisy frames under rigid, constant translation. The algorithm is non-iterative and needs no hyperparameter tuning. It requires a fixed number of FFT, multiplication, and downsampling operations for a given input size, enabling fast implementation on embedded platforms like cubesats where on-board image fusion can greatly save on limited downlink bandwidth. The algorithm is optimal in the maximum likelihood sense for additive white Gaussian noise and non-stationary Gaussian approximations of Poisson noise. Accurate registration is achieved for very low SNR, even when visible features are below the noise floor.

eess.IV↗

arXiv2021

High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physical limitations, in this paper, we develop a novel multi-spectral imaging modality that enables higher spatial and spectral resolutions. In the developed computational imaging modality, we exploit a diffractive lens, such as a photon sieve, for both dispersing and focusing the optical field, and achieve measurement diversity by changing the focusing behavior of this lens. Because the focal length of a diffractive lens is wavelength-dependent, each measurement is a superposition of differently blurred spectral components. To reconstruct the individual spectral images from these superimposed and blurred measurements, model-based fast reconstruction algorithms are developed with deep and analytical priors using alternating minimization and unrolling. Finally, the effectiveness and performance of the developed technique is illustrated for an application in astrophysical imaging under various observation scenarios in the extreme ultraviolet (EUV) regime. The results demonstrate that the technique provides not only diffraction-limited high spatial resolution, as enabled by diffractive lenses, but also the capability of resolving close-by spectral sources that would not otherwise be possible with the existing techniques. This work enables high resolution multi-spectral imaging with low cost designs for a variety of applications and spectral regimes.

eess.IV↗

arXiv2021

Spatial-temporal switching estimators for imaging locally concentrated dynamics

The evolution of images with physics-based dynamics is often spatially localized and nonlinear. A switching linear dynamic system (SLDS) is a natural model under which to pose such problems when the system's evolution randomly switches over the observation interval. Because of the high parameter space dimensionality, efficient and accurate recovery of the underlying state is challenging. The work presented in this paper focuses on the common cases where the dynamic evolution may be adequately modeled as a collection of decoupled, locally concentrated dynamic operators. Patch-based hybrid estimators are proposed for real-time reconstruction of images from noisy measurements given perfect or partial information about the underlying system dynamics. Numerical results demonstrate the effectiveness of the proposed approach for denoising in a realistic data-driven simulation of remotely sensed cloud dynamics.

eess.SY↗

arXiv2020

Quantification of mismatch error in randomly switching linear state-space models

Switching Kalman Filters (SKF) are well known for their ability to solve the piecewise linear dynamic system estimation problem using the standard Kalman Filter (KF). Practical SKFs are heuristic, approximate filters that are not guaranteed to have optimal performance and require more computational resources than a single mode KF. On the other hand, applying a single mode mismatched KF to a switching linear dynamic system (SLDS) results in erroneous estimation. This paper aims to quantify the average error an SKF can eliminate compared to a mismatched, single mode KF in a known SLDS before collecting measurements. Mathematical derivations for the first and second moments of the estimators errors are provided and compared. One can use these derivations to quantify the average performance of filters beforehand and decide which filter to run in operation to have the best performance in terms of estimation error and computation complexity. We further provide simulation results that verify our mathematical derivations.

eess.SP↗

arXiv2020

Optimal Measurement Configuration in Computational Diffractive Imaging

Diffractive lenses have recently been applied to the domain of multispectral imaging in the X-ray and UV regimes where they can achieve very high resolution as compared to reflective and refractive optics. Conventionally, spectral components are reconstructed by taking measurements at the focal planes. However, the reconstruction quality can be improved by optimizing the measurement configuration. In this work, we adapt a sequential backward selection algorithm to search for a configuration which minimizes expected reconstruction error. By approximating the forward system as a circular convolution and making assumptions on the source and noise, we greatly reduce the complexity of the algorithm. Numerical results show that the configuration found by the algorithm significantly improves the reconstruction performance compared to a standard configuration.

eess.IV↗

arXiv2020

Bayesian Methods for Joint Exoplanet Transit Detection and Systematic Noise Characterization

The treatment of systematic noise is a significant aspect of transit exoplanet data processing due to the signal strength of systematic noise relative to a transit signal. Typically the standard approach to transit detection is to estimate and remove systematic noise independently of and prior to a transit detection test. If a transit signal is present in a light curve, the process of systematic noise removal may distort the transit signal by overfitting and thereby reduce detection efficiency. We present a Bayesian framework for joint detection of transit signals and systematic noise characterization and describe the implementation of these detectors as optimal Neyman-Pearson likelihood ratio tests. The joint detectors reduce to closed form as matched filters under the assumption of a Gaussian Bayesian prior for the systematic noise. The performance of the exploratory detectors was evaluated in injection tests and show $\sim 2\%$ improvement in overall detection efficiency relative to the standard approach. We find that joint detection efficiency is specifically improved for short-period, low transit-depth exoplanet transits, providing evidence in support of the hypothesis that joint detection may indeed help to mitigate overfitting. In addition, an initial feasibility test to detect known exoplanets in Kepler data using the joint detectors produced encouraging preliminary results.

astro-ph.EP↗

arXiv2018

Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System

The authors of this report met on 28-30 March 2018 at the New Jersey Institute of Technology, Newark, New Jersey, for a 3-day workshop that brought together a group of data providers, expert modelers, and computer and data scientists, in the solar discipline. Their objective was to identify challenges in the path towards building an effective framework to achieve transformative advances in the understanding and forecasting of the Sun-Earth system from the upper convection zone of the Sun to the Earth's magnetosphere. The workshop aimed to develop a research roadmap that targets the scientific challenge of coupling observations and modeling with emerging data-science research to extract knowledge from the large volumes of data (observed and simulated) while stimulating computer science with new research applications. The desire among the attendees was to promote future trans-disciplinary collaborations and identify areas of convergence across disciplines. The workshop combined a set of plenary sessions featuring invited introductory talks and workshop progress reports, interleaved with a set of breakout sessions focused on specific topics of interest. Each breakout group generated short documents, listing the challenges identified during their discussions in addition to possible ways of attacking them collectively. These documents were combined into this report-wherein a list of prioritized activities have been collated, shared and endorsed.

astro-ph.SR↗