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orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

Techniques to retrieve the atmospheric properties of exoplanets via direct observation of their reflected light have often been limited in scope due to computational constraints imposed by the forward-model calculations. We have developed a new set of techniques which significantly decreases the time required to perform a retrieval while maintaining accurate results. We constructed a grid of 1.4 million pre-computed geometric albedo spectra valued at discrete sets of parameter points. Spectra from this grid are used to produce models for a fast and efficient nested sampling routine called PSGnest. Beyond the upfront time to construct a spectral grid, the amount of time to complete a full retrieval using PSGnest is on the order of seconds to minutes using a personal computer. An extensive evaluation of the error induced from interpolating intermediate spectra from the grid indicates that this bias is insignificant compared to other retrieval error sources, with an average coefficient of determination between interpolated and true spectra of 0.998. We apply these new retrieval techniques to help constrain the optimal bandpass centers for retrieving various atmospheric and bulk parameters from a LuvEx-type mission observing several planetary archetypes. We show that spectral observations made using a 20\% bandpass centered at 0.73 microns can be used alongside our new techniques to make detections of $H_2O$ and $O_2$ without the need to increase observing time beyond what is necessary for a signal-to-noise ratio of 10. The methods introduced here will enable robust studies of the capabilities of future observatories to characterize exoplanets., Comment: 32 pages, 17 figures. Accepted for publication in The Astronomical Journal

We present a new generation of substellar atmosphere and evolution models, appropriate for application to studies of L, T, and Y-type brown dwarfs and self-luminous extrasolar planets. The atmosphere models describe the expected temperature-pressure profiles and emergent spectra of atmospheres in radiative-convective equilibrium with effective temperatures and gravities within the ranges $200\le T_{\rm eff}\le2400\,\rm K$ and $2.5\le \log g \le 5.5$. These ranges encompass masses from about 0.5 to 85 Jupiter masses for a set of metallicities ($[{\rm M/H}] = -0.5$ to $+0.5$), C/O ratios (from 0.5 to 1.5 times that of solar), and ages. The evolution tables describe the cooling of these substellar objects through time. These models expand the diversity of model atmospheres currently available, notably to cooler effective temperatures and greater ranges in C/O. Notable improvements from past such models include updated opacities and atmospheric chemistry. Here we describe our modeling approach and present our initial tranche of models for cloudless, chemical equilibrium atmospheres. We compare the modeled spectra, photometry, and evolution to various datasets., Comment: 27 pages, 16 figures, accepted for Astrophysical Journal. Models available at https://doi.org/10.5281/zenodo.5063476

Simulations of exoplanet albedo profiles are key to planning and interpreting future direct imaging observations. In this paper we demonstrate the use of the Planetary Spectrum Generator to produce simulations of reflected light exoplanet spectra. We use PSG to examine multiple issues relevant to all models of directly imaged exoplanet spectra and to produce sample spectra of the bright, nearby exoplanet $\upsilon$ Andromedae d, a potential direct imaging target for next-generation facilities. We introduce a new, fast, and accurate subsampling technique that enables calculations of disk-integrated spectra one order of magnitude faster than Chebyshev-Gauss sampling for moderate- to high-resolution sampling. Using this method and a first-principles-derived atmosphere for $\upsilon$ And d, we simulate phase-dependent spectra for a variety of different potential atmospheric configurations. The simulated spectra for $\upsilon$ And d include versions with different haze and cloud properties. Based on our combined analysis of this planet's orbital parameters, phase-and illumination-appropriate model spectra, and realistic instrument noise parameters, we find that $\upsilon$ And d is a potentially favorable direct imaging and spectroscopy target for the Coronagraph Instrument (CGI) on the Nancy Grace Roman Space Telescope. When a noise model corresponding to the Roman CGI SPC spectroscopy mode is included, PSG predicts the time required to reach a signal-to-noise ratio of 10 of the simulated spectra in both the central wavelength bin of the Roman CGI SPC spectroscopy mode (R=50 spectrum) and of the Band 1 HLC imaging mode is approximately 400 and less than 40 hr, respectively. We also discuss potential pathways to extricating information about the planet and its atmosphere with future observations and find that Roman observations may be able to bound the interior temperature of the planet., Comment: 23 pages, 14 figures

In anticipation of future flagship missions focused on the goal of achieving direct imaging of rocky exoplanets, we have developed a database of models to help the community examine the potential spectral characteristics of a broad range of rocky planet atmospheres. Using the publicly available Planetary Spectrum Generator (PSG), we have computed a grid of 141,600 rocky exoplanet geometric albedo spectra across a 7-dimensional parameter space. Using this grid, we have performed a color-color analysis seeking to identify the most useful near-ultraviolet and red or near-infrared photometric followup channels to combine with a green-optical (discovery) spectral channel. We found that a combination of filters at 0.4 um, 0.58 um, and (approx) 0.8um were able to distinguish between atmospheres with moderate-to-high concentrations of four different dominant absorbing constituents, given at least 10 hours of observation on a star at 10 parsec with a 15-meter-class space telescope; however, more moderate abundances similar to those of Solar System rocky bodies would be more challenging to detect. We recommend that future missions seeking to characterize directly imaged rocky exoplanets by colors alone further consider multi-band photometry as a first discriminator for planetary characteristics., Comment: 15 pages, 10 figures. Accepted by AJ as of 9/2/2020

The characterization of rocky, Earth-like planets is an important goal for future large ground- and space-based telescopes. In support of developing an efficient observational strategy, we have applied Bayesian statistical inference to interpret the albedo spectrum of cloudy true-Earth analogs that include a diverse spread in their atmospheric water vapor mixing ratios. We focus on detecting water-bearing worlds by characterizing their atmospheric water vapor content via the strong 0.94$\,\mu$m H$_2$O absorption feature, with several observational configurations. Water vapor is an essential signpost when assessing planetary habitability, and determining its presence is important in vetting whether planets are suitable for hosting life. We find that R=140 spectroscopy of the absorption feature combined with a same-phase green optical photometric point at $0.525-0.575\,\mu$m is capable of distinguishing worlds with less than $0.1\times$ Earth-like water vapor levels from worlds with $1\times$ Earth-like levels or greater at a signal-to-noise ratio of 5 or better with $2\sigma$ confidence. This configuration can differentiate between $0.01\times$ and $0.1\times$ Earth-like levels when the signal-to-noise ratio is 10 or better at the same confidence. However, strong constraints on the water vapor mixing ratio remained elusive with this configuration even at signal-to-noise of 15. We find that adding the same-phase optical photometric point does not significantly help characterize the H$_2$O mixing ratio, but does enable an upper limit on atmospheric ozone levels. Finally, we find that a 0.94$\,\mu$m photometric point, instead of spectroscopy, combined with the green-optical point, fails to produce meaningful information about atmospheric water content., Comment: Accepted to AJ

Atmospheric characterization of directly imaged planets has thus far been limited to ground-based observations of young, self-luminous, Jovian planets. Near-term space- and ground- based facilities like \emph{WFIRST} and ELTs will be able to directly image mature Jovian planets in reflected light, a critical step in support of future facilities that aim to directly image terrestrial planets in reflected light (e.g. HabEx, LUVOIR). These future facilities are considering the use of photometry to classify planets. Here, we investigate the intricacies of using colors to classify gas-giant planets by analyzing a grid of 9,120 theoretical reflected light spectra spread across different metallicities, pressure-temperature profiles, cloud properties, and phase angles. We determine how correlated these planet parameters are with the colors in the \emph{WFIRST} photometric bins and other photometric bins proposed in the literature. Then we outline under what conditions giant planet populations can be classified using several supervised multivariate classification algorithms. We find that giant planets imaged in reflected light can be classified by metallicity with an accuracy of $>$90\% if they are \emph{a prior} known to not have significant cloud coverage in the visible part of the atmosphere, and at least 3 filter observations are available. If the presence of clouds is not known \emph{a priori}, directly imaged planets can be more accurately classified by their cloud properties, as oppposed to metallicity or temperature. Furthermore, we are able to distinguish between cloudy and cloud-free populations with $>$90\% accuracy with 3 filter observations. Our statistical pipeline is available on GitHub and can be extended to optimize science yield of future mission concepts., Comment: 16 pages, 11 figures, accepted AJ