arXiv subjects

Allison Payne

Publications and source records attributed to Allison Payne.

4 recordsLinked to original sources

arXiv2025

Multi-bandpass Photometry for Exoplanet Atmosphere Reconnaissance (MPEAR) with the Habitable Worlds Observatory (HWO) -- I. Differentiating Earth from Neptunes During Discovery

As the architecture for the Habitable Worlds Observatory (HWO) is being developed, it is crucial to optimize the observing strategies for a survey to detect and characterize Earth-like planets around Sun-like stars. Efficient target identification and characterization will help drive mission requirements that can be matched to the planned observations. Current HWO concepts allow simultaneous multi-bandpass observations with the coronagraph instrument, critical for performing a qualitative planetary reconnaissance to optimize observing time for deriving orbital constraints and prioritize characterization of promising targets. We describe a new algorithm designed to determine the best combination of broadband photometric observations for extracting maximum information from the first visit. It identifies degeneracies in the orbital configurations, fluxes, and noise, and determines optimal secondary photometry bands to reduce these. We demonstrate its application by comparing an Earth seen at quadrature with a cold and a warm Neptune at inclined orbits and varying phases, with comparable flux in the discovery bandpass centered at 500 nm (20\% bandwidth). Using the noise and exposure time calculator that we developed for the HWO coronagraph instrument, we find that the baseline $S/N=7$ (corresponding to 3.2 hours observing time for a planet at 10pc) is only sufficient to marginally differentiate the Earth from a cold Neptune-like planet assuming two parallel bandpasses (550 nm + 850 nm). However, increasing to $S/N=15$ (7 hours observing time) and using three parallel bandpasses (360 nm + 500 nm + 1.11 micron) would differentiate the Earth from either a warm or cold Neptune.

astro-ph.EP↗

arXiv2025

A comprehensive spectroscopic reference of the solar system and its application to exoplanet direct imaging

We present a calibrated database of reflectance spectra for the solar system planets (i.e., Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune) and for Titan, spanning from the ultraviolet to the near infrared. We considered data collected over 60 years of planetary observations, employing a broad range of geometries and facilities (spacecraft and ground-based observatories). To correct for differences in observational geometries and data quality, we adopted a two-step calibration process that standardized each spectrum to the planet's geometric albedo values and corrected for planetary heterogeneity and calibration effects. The calibrated datasets were then combined across wavelengths, leading to a reference composite reflectance spectrum for each planet. As a test of this spectral library for exoplanetary research, we simulated direct imaging observations of the Proxima Centauri and HD 219134 systems as solar system analogs, as well as the solar system at a distance of 10 parsecs. We also explored the detection limitations of direct imaging instruments imposed by the inner and outer working angles for Earth and Jupiter-like exoplanets as a function of system distance. Additionally, we used the visible light portion of the results to produce realistic color reconstructions of each planet. Standardizing reflectance spectra in this work improves our baseline for interpreting new reflected light observations of exoplanets through comparative planetology. This spectral library can then serve as a calibrated and validated reference in the modeling and preparation for the characterization of exoplanet atmospheres with future direct imaging missions and for astronomical studies of the solar system.

astro-ph.EP↗

arXiv2024

The pale blue dot: using the Planetary Spectrum Generator to simulate signals from hyper realistic exo-Earths

The atmospheres and surfaces of planets show tremendous amount of spatial variation, which has a direct effect on the spectrum of the object, even if this may not be spatially resolved. Here, we apply hyper realistic radiative simulations of Earth as an exoplanet comprising thousands of simulations and study the unresolved spectrum. The GlobES module on the Planetary Spectrum Generator was used, and we parameterized the atmosphere as described in the modern earth retrospective analysis for research and applications, MERRA2, database. The simulations were made into high spatial resolution images and compared to space based observations from the DSCOVR EPIC, at L1, and Himawari8, geostationary, satellites, confirming spatial variations and the spectral intensities of the simulations. The DSCOVR EPIC camera only functions in narrow wavelength bands, but strong agreement is demonstrated. It is shown that aerosols and small particles play an important role in defining Earths reflectance spectra, contributing significantly to its characteristic blue color. Subsequently, a comprehensive noise model is employed to constrain the exposure time required to detect O2, O3 and H2O as a function of varying ground and cloud cover for several concept observatories, including the habitable worlds observatory. Cloud coverage enhances the detectability of planets in reflected light, with important consequences for the design of the future HWO. The HWO concept would require between 3 to 10 times longer to observe the studied features than LUVOIR A but performs better than the HabEx without a starshade. The codes, routines, and the noise models are made publicly available.

astro-ph.EP↗

arXiv2020

Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors

Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarker immediately after treatment from contrast-enhanced MRI. The NPV has variable accuracy, and the use of contrast agent prevents continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model. A deep convolutional neural network was trained on noncontrast multiparametric MR images using the NPV biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue. A novel volume-conserving registration algorithm yielded a voxel-wise correlation between treatment and follow-up NPV, providing a rigorous validation of the biomarker. The learned noncontrast multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, substantially outperforming the current clinical standard (DICE coefficient = 0.53). Noncontrast multiparametric MR imaging integrated with a deep convolutional neural network provides a more accurate prediction of MRgFUS treatment outcome than current contrast-based techniques.

eess.IV↗