arXiv subjects

Jamila Taaki

Publications and source records attributed to Jamila Taaki.

5 recordsLinked to original sources

arXiv2026

Using Astrometry to Break Degeneracies in Stellar Surface Mapping

Astrometric jitter noise arises when starspots on a rotating stellar surface move in and out of view, shifting the photocenter. This noise may limit our ability to detect and weigh small, sub-Neptune-sized planets around active stars. By deriving a linear forward model for the astrometric jitter signal of a rotating star in a spherical-harmonic coordinate system, we show that jitter noise can be used to reconstruct surface-brightness maps and, in principle, disentangle jitter from stellar reflex motion due to an orbiting planet. Furthermore, we show that astrometry and photometry probe complementary surface information: photometry measures even-degree spherical harmonic surfaces that are symmetric about the equator, while astrometry measures odd-degree modes. Their joint use, therefore, breaks degeneracies in surface mapping. Our model further quantifies the variation in the astrometric signal with inclination angle, which is foundational for studies of worst-case configurations of astrometric star-spot noise. For example, we show that pole-on stellar inclinations lead to poorly constrained inversions, as any stellar surface produces a purely circular astrometric jitter signal. We characterize the degeneracy in jointly identifying the stellar surface and stellar inclination, and develop a surface estimation approach. Using this approach, we present example simulations and reconstructions that demonstrate the use of astrometry data alongside light-curve data to improve stellar surface mapping and localize spot positions in latitude and longitude. With forthcoming high-precision Gaia astrometry, astrometric surface mapping provides a promising new approach to probe stellar activity.

astro-ph.SR↗

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

Identifiability of Rotating Stellar Surfaces from Astrometric Jitter

Astrometry measures shifts in a star's photocentre and can be used to detect reflex motion due to orbiting exoplanets. Brightness asymmetries (e.g. starspots) rotating in and out of view can also cause apparent motion of the photocenter, termed astrometric jitter, that has previously been considered a source of noise. Here, we explore whether it can be used to map stellar surfaces. We derive a Cramer-Rao bound on the minimum variance for which a stellar surface can theoretically be estimated, quantifying the information content in rotational astrometric jitter. To regularize and break singularities in the Fisher information, we impose a spatial-smoothness Gaussian-Markov random field prior. A key challenge in mapping surfaces arises for stars with unknown rotational axis inclinations, requiring joint estimation of the inclination and the stellar surface. We characterize the coupling between them and quantify the precision gain when inclination is known versus unknown.

astro-ph.IM↗

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↗

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↗