NASA's \textit{Kepler} primary mission observed about 116 \(deg^2\) in the sky for 3.5 consecutive years to discover Earth-like exoplanets. This mission recorded pixel cutouts, known as Target Pixel ...Files (TPFs), of over \(200,000\) targets selected to maximize the scientific yield. The Kepler pipeline performed aperture photometry for these primary targets to create light curves. However, hundreds of thousands of background sources were recorded in the TPFs and have never been systematically analyzed. This work uses the Linearized Field Deblending (LFD) method, a Point Spread Function (PSF) photometry algorithm, to extract light curves. We use Gaia DR3 as input catalog to extract \(606,900\) light curves from long-cadence TPFs. \(406,548\) are new light curves of background sources, while the rest are Kepler's targets. These light curves have comparable quality as those computed by the Kepler pipeline, with CDPP values \(<100\) ppm for sources \(G<16\). The light curve files are available as high-level science products at MAST. Files include PSF and aperture photometry, and extraction metrics. Additionally, we improve the background and PSF modeling in the LFD method. The LFD method is implemented in the \texttt{Python} library \texttt{psfmachine}. We demonstrate the advantages of this new dataset with two examples; deblending of contaminated false positive Kepler Object of Interest identifying the origin of the transit signal; and the changes in estimated transit depth of planets using PSF photometry which improves dilution when compared to aperture photometry. This new nearly unbiased catalog enables further studies in planet search, occurrence rates, and other time-domain studies.
The use of Gaussian processes (GPs) as models for astronomical time series datasets has recently become almost ubiquitous, given their ease of use and flexibility. GPs excel in particular at ...marginalization over the stellar signal in cases where the variability due to starspots rotating in and out of view is treated as a nuisance, such as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest since it is not obvious how the parameters of the GP model are related to the physical properties of interest, such as the size, contrast, and latitudinal distribution of the spots. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is degenerate, ill-posed, and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many light curves. In this paper, we derive a closed-form expression for the mean and covariance of a Gaussian process model that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more stellar light curves, including the typical radii and the mean and variance of the latitude distribution of starspots. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from both light curves and radial velocity (RV) measurements, as well as for robustly modeling correlated noise in both transiting and RV exoplanet searches. Our implementation is efficient, user-friendly, and open source, available as the Python package starry-process.
In this note we present the starry_process code, which implements an interpretable Gaussian process (GP) for modeling variability in stellar light curves. As dark starspots rotate in and out of view, ...the total flux received from a distant star will change over time. Unresolved flux time series therefore encode information about the spatial structure of features on the stellar surface. The starry_process software package allows one to easily model the flux variability due to starspots, whether one is interested in understanding the properties of these spots or marginalizing over the stellar variability when it is treated as a nuisance signal. The main difference between the GP implemented here and typical GPs used to model stellar variability is the explicit dependence of our GP on physical properties of the star, such as its period, inclination, and limb darkening coefficients, and on properties of the spots, such as their radius and latitude distributions. This code is the Python implementation of the interpretable GP algorithm developed in Luger, Foreman-Mackey, and Hedges (2021).
Pandora is an upcoming NASA SmallSat mission that will observe transiting exoplanets to study their atmospheres and the variability of their host stars. Efficient mission planning is critical for ...maximizing the science achieved with the year-long primary mission. To this end, we have developed a scheduler based on a metaheuristic algorithm that is focused on tackling the unique challenges of time-constrained observing missions, like Pandora. Our scheduling algorithm combines a minimum transit requirement metric, which ensures we meet observational requirements, with a `quality' metric that considers three factors to determine the scientific quality of each observation window around an exoplanet transit (defined as a visit). These three factors are: observing efficiency during a visit, the amount of the transit captured by the telescope during a visit, and how much of the transit captured is contaminated by a coincidental passing of the observatory through the South Atlantic Anomaly. The importance of each of these factors can be adjusted based on the needs or preferences of the science team. Utilizing this schedule optimizer, we develop and compare a few schedules with differing factor weights for the Pandora SmallSat mission, illustrating trade-offs that should be considered between the three quality factors. We also find that under all scenarios probed, Pandora will not only be able to achieve its observational requirements using the planets on the notional target list but will do so with significant time remaining for ancillary science.
NASA's Kepler mission observed background regions across its field of view for more than three consecutive years using custom designed super apertures (EXBA masks). Since these apertures were ...designed to capture a region of the sky rather than single targets, the Kepler Science Data Processing pipeline produced Target Pixel Files, but did not produce light curves for the sources within these background regions. In this work we produce light curves for \(9,327\) sources observed in the EXBA masks. These light curves are generated using aperture photometry estimated from the instrument's Pixel Response Function (PRF) profile computed from Kepler's full-frame images. The PRF models enable the creation of apertures that follow the characteristic shapes of the PSF in the image and the computation of flux completeness and contamination metrics. The light curves are available at MAST as a High Level Science Product (kbonus-apexba). Alongside this dataset, we present kepler-apertures, a Python library to compute PRF models and use them to perform aperture photometry on Kepler-like data. Using light curves from the EXBA masks we found an exoplanet candidate around Gaia EDR3 2077240046296834304 consistent with a large planet companion with a \(0.81 R_J\) radius. Additionally, we report a catalog of 69 eclipsing binaries. We encourage the community to exploit this new dataset to perform in depth time domain analysis, such as eclipsing binaries demographic and others.
The Nancy Grace Roman Space Telescope (Roman) is NASA's next astrophysics flagship mission, expected to launch in late 2026. As one of Roman's core community science surveys, the Galactic Bulge Time ...Domain Survey (GBTDS) will collect photometric and astrometric data for over 100 million stars in the Galactic bulge to search for microlensing planets. To assess the potential with which Roman can detect exoplanets via transit, we developed and conducted pixel-level simulations of transiting planets in the GBTDS. From these simulations, we predict that Roman will find between \(\sim\)60,000 and \(\sim\)200,000 transiting planets, over an order of magnitude more planets than are currently known. While the majority of these planets will be giants (\(R_p>4R_\oplus\)) on close-in orbits (\(a<0.3\) au), the yield also includes between \(\sim\)7,000 and \(\sim\)12,000 small planets (\(R_p<4 R_\oplus\)). The yield for small planets depends sensitively on the observing cadence and season duration, with variations on the order of \(\sim\)10-20% for modest changes in either parameter, but is generally insensitive to the trade between surveyed area and cadence given constant slew/settle times. These predictions depend sensitively on the Milky Way's metallicity distribution function, highlighting an opportunity to significantly advance our understanding of exoplanet demographics, particularly across stellar populations and Galactic environments.
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