RAPOC : the Rosseland and Planck opacity converter. A user-friendly and fast opacity program for Python

RAPOC (Rosseland and Planck Opacity Converter) is a Python 3 code that calculates Rosseland and Planck mean opacities (RPMs) from wavelength-dependent opacities for a given temperature, pressure, and wavelength range. In addition to being user-friendly and rapid, RAPOC can interpolate between discrete data points, making it flexible and widely applicable to the astrophysical and Earth-sciences fields, as well as in engineering. For the input data, RAPOC can use ExoMol and DACE data, or any user-defined data, provided that it is in a readable format. In this paper, we present the RAPOC code and compare its calculated Rosseland and Planck mean opacities with other values found in the literature. The RAPOC code is open-source and available on Pypi and GitHub.Comment: 15 pages, 6 figures, 3 tables; Accepted for Publication in Exp. Astro

Detecting molecules in Ariel low resolution transmission spectra

The Ariel Space Mission aims to observe a diverse sample of exoplanet atmospheres across a wide wavelength range of 0.5 to 7.8 microns. The observations are organized into four Tiers, with Tier 1 being a reconnaissance survey. This Tier is designed to achieve a sufficient signal-to-noise ratio (S/N) at low spectral resolution in order to identify featureless spectra or detect key molecular species without necessarily constraining their abundances with high confidence. We introduce a P-statistic that uses the abundance posteriors from a spectral retrieval to infer the probability of a molecule’s presence in a given planet’s atmosphere in Tier 1. We find that this method predicts probabilities that correlate well with the input abundances, indicating considerable predictive power when retrieval models have comparable or higher complexity compared to the data. However, we also demonstrate that the P-statistic loses representativity when the retrieval model has lower complexity, expressed as the inclusion of fewer than the expected molecules. The reliability and predictive power of the P-statistic are assessed on a simulated population of exoplanets with H2-He dominated atmospheres, and forecasting biases are studied and found not to adversely affect the classification of the survey

Predicting the optical performance of the Ariel Telescope using PAOS

The Ariel Space Mission is the M4 mission in ESA's Cosmic Vision program and will observe a large and diverse sample of exoplanetary atmospheres in the visible to the near-infrared range of the electromagnetic spectrum. Assessing the impact of diffraction, aberrations, and related systematics on the Ariel optical performance before having a system-level measurement is paramount to ensuring that the optical quality, complexity, costs, and risks are not too high. Several codes offer Physical Optics Propagation (POP) calculations, although generally, they are not easily customizable, e.g., for Monte Carlo simulations, are not free access and publicly available, or have technical limitations such as not providing support for refractive elements. PAOS, the Physical Ariel Optics Simulator, is an end-to-end Physical Optics Propagation (POP) model of the Ariel telescope and subsystems. PAOS implements Fresnel diffraction in the near and far fields to simulate the propagation of the complex electromagnetic wavefront through the Ariel optical chain and deliver the realistic PSFs vs. lambda at the intermediate and focal planes. PAOS is written with a full Python 3 stack and comes with an installer, documented examples, and an exhaustive guide. PAOS is meant to be easy to use, generic and versatile for POP simulations of optical systems other than Ariel’s, thanks to its generic input system and built-in GUI providing a seamless user interface and simulations

ExoRad 2.0: The generic point source radiometric model

ExoRad 2.0 is a generic radiometric simulator compatible with any instrument for point source photometry or spectroscopy. Given the descriptions of an observational target and the instrumentation, ExoRad 2.0 estimates several performance metrics for each photometric channel and spectral bin. These include the total optical efficiency, the measured signal from the target, the saturation times, the read noise, the photon noise, the dark current noise, the zodiacal emission, the instrument-self emission and the sky foreground emission. ExoRad 2.0 is written in Python and it is compatible with Python 3.8 and higher. The software is released under the BSD 3-Clause license, and it is available on PyPi, so it can be installed as pip install exorad. Alternatively, the software can be installed from the source code available on GitHub. Before each run, ExoRad 2.0 checks for updates and notifies the user if a new version is available. ExoRad 2.0 has an extensive documentation, available on readthedocs, including a quick-start guide, a tutorial, and a detailed description of the software functionalities. The documentation is continuously updated along with the code. The software source code, available on GitHub, also includes a set of examples of the simulation inputs (for instruments and targets) to run the software and reproduce the results reported in the documentation. The software has been extensively validated against the Ariel radiometric model ArielRad (Mugnai et al., 2020), the time domain simulator ExoSim (Sarkar et al., 2021) and custom simulations performed by the Ariel consortium. ExoRad 2.0 is now used not only by the Ariel consortium but also by other missions, such as the balloon-borne NASA EXCITE mission (Nagler et al., 2022), the space telescope Twinkle (Stotesbury et al., 2022), and an adaptation for the James Webb Space Telescope (Gardner et al., 2006) is under preparation. Such JWST adaptation has been tested against the JWST Exposure Time Calculator (Pontoppidan et al., 2016) and returned consistent results, providing a validation of the code against a working system. Although the code has been validated and used mostly for space and airborne-based telescopes, we foresee no practical limitation to adaptation for ground-based system

Alfnoor: A Retrieval Simulation of the Ariel Target List

In this work, we present Alfnoor, a dedicated tool optimised for population studies of exoplanet atmospheres. Alfnoor combines the latest version of the retrieval algorithm TauREx 3, with the instrument noise simulator ArielRad and enables the simultaneous retrieval analysis of a large sample of exo-atmospheres. We applied this tool to the Ariel list of planetary candidates and focus on hydrogen dominated, cloudy atmospheres observed in transit with the Tier-2 mode (medium Ariel resolution). As a first experiment, we randomised the abundances - ranging from 10$^{-7}$ to 10$^{-2}$ - of the trace gases, which include H$_2$O, CH$_4$, CO, CO$_2$ and NH$_3$. This exercise allowed to estimate the detection limits for Ariel Tier-2 and Tier-3 modes when clouds are present. In a second experiment, we imposed an arbitrary trend between a chemical species and the effective temperature of the planet. A last experiment was run requiring molecular abundances being dictated by equilibrium chemistry at a certain temperature. Our results demonstrate the ability of Ariel Tier-2 and Tier-3 surveys to reveal trends between the chemistry and associated planetary parameters. Future work will focus on eclipse data, on atmospheres heavier than hydrogen and will be applied also to other observatories.Comment: 34 pages, 24 figures, Accepted in A

Detecting molecules in Ariel low resolution transmission spectra

The Ariel Space Mission aims to observe a diverse sample of exoplanet atmospheres across a wide wavelength range of 0.5 to 7.8 microns. The observations are organized into four Tiers, with Tier 1 being a reconnaissance survey. This Tier is designed to achieve a sufficient signal-to-noise ratio (S/N) at low spectral resolution in order to identify featureless spectra or detect key molecular species without necessarily constraining their abundances with high confidence. We introduce a P-statistic that uses the abundance posteriors from a spectral retrieval to infer the probability of a molecule’s presence in a given planet’s atmosphere in Tier 1. We find that this method predicts probabilities that correlate well with the input abundances, indicating considerable predictive power when retrieval models have comparable or higher complexity compared to the data. However, we also demonstrate that the P-statistic loses representativity when the retrieval model has lower complexity, expressed as the inclusion of fewer than the expected molecules. The reliability and predictive power of the P-statistic are assessed on a simulated population of exoplanets with H2 -He dominated atmospheres, and forecasting biases are studied and found not to adversely affect the classification of the survey

Alfnoor: Assessing the information content of Ariel's low-resolution spectra with planetary population studies

The Ariel Space Telescope will provide a large and diverse sample of exoplanet spectra, performing spectroscopic observations of about 1000 exoplanets in the wavelength range 0.5–7.8 μm. In this paper, we investigate the information content of Ariel’s Reconnaissance Survey low-resolution transmission spectra. Among the goals of the Ariel Reconnaissance Survey is also to identify planets without molecular features in their atmosphere. In this work, (1) we present a strategy that will allow us to select candidate planets to be reobserved in Ariel’s higher-resolution tier, (2) we propose a metric to preliminary classify exoplanets by their atmospheric composition without performing an atmospheric retrieval, and (3) we introduce the possibility to find other methods to better exploit the data scientific content

A survey of exoplanet phase curves with Ariel

The ESA-Ariel mission will include a tier dedicated to exoplanet phase curves corresponding to ∼ 10 % of the science time. We present here the current observing strategy for studying exoplanet phase curves with Ariel. We define science questions, requirements and a list of potential targets. We also estimate the precision of phase curve reconstruction and atmospheric retrieval using simulated phase curves. Based on this work, we found that full-orbit phase variations for 35-40 exoplanets could be observed during the 3.5-yr mission. This statistical sample would provide key constraints on atmospheric dynamics, composition, thermal structure and clouds of warm exoplanets, complementary to the scientific yield from spectroscopic transits/eclipses measurements