Showing papers by "Ahmed Al-Refaie published in 2022"

Five Key Exoplanet Questions Answered via the Analysis of 25 Hot-Jupiter Atmospheres in Eclipse

Abstract: Population studies of exoplanets are key to unlocking their statistical properties. So far, the inferred properties have been mostly limited to planetary, orbital, and stellar parameters extracted from, e.g., Kepler, radial velocity, and Gaia data. More recently an increasing number of exoplanet atmospheres have been observed in detail from space and the ground. Generally, however, these atmospheric studies have focused on individual planets, with the exception of a couple of works that have detected the presence of water vapor and clouds in populations of gaseous planets via transmission spectroscopy. Here, using a suite of retrieval tools, we analyze spectroscopic and photometric data of 25 hot Jupiters, obtained with the Hubble and Spitzer Space Telescopes via the eclipse technique. By applying the tools uniformly across the entire set of 25 planets, we extract robust trends in the thermal structure and chemical properties of hot Jupiters not obtained in past studies. With the recent launch of the James Webb Space Telescope and the upcoming missions Twinkle and Ariel, population-based studies of exoplanet atmospheres, such as the one presented here, will be a key approach to understanding planet characteristics, formation, and evolution in our galaxy.

Exploring the Ability of HST WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres Through a Homogeneous Transmission Survey of 70 Gaseous Planets

Abstract: We present the analysis of the atmospheres of 70 gaseous extrasolar planets via transit spectroscopy with Hubble’s Wide Field Camera 3 (WFC3). For over half of these, we statistically detect spectral modulation which our retrievals attribute to molecular species. Among these, we use Bayesian Hierarchical Modelling to search for chemical trends with bulk parameters. We use the extracted water abundance to infer the atmospheric metallicity and compare it to the planet’s mass. We also run chemical equilibrium retrievals, fitting for the atmospheric metallicity directly. However, although previous studies have found evidence of a mass-metallicity trend, we find no such relation within our data. For the hotter planets within our sample, we find evidence for thermal dissociation of dihydrogen and water via the H− opacity. We suggest that the general lack of trends seen across this population study could be due to i) the insufficient spectral coverage offered by HST WFC3 G141, ii) the lack of a simple trend across the whole population, iii) the essentially random nature of the target selection for this study or iv) a combination of all the above. We set out how we can learn from this vast dataset going forward in an attempt to ensure comparative planetology can be undertaken in the future with facilities such as JWST, Twinkle and Ariel. We conclude that a wider simultaneous spectral coverage is required as well as a more structured approach to target selection.

To Sample or Not To Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalising Flows

Abstract: Current endeavours in exoplanet characterisation rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of the technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation becomes more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in Machine Learning provide optimisation-based Variational Inference as an alternative approach to perform approximate Bayesian Posterior Inference. In this investigation we combined Normalising Flow-based neural network with our newly developed differentiable forward model, Diff- τ , to perform Bayesian Inference in the context of atmospheric retrieval. Using examples from real and simulated spectroscopic data, we demonstrated the superiority of our proposed framework: 1) Training Our neural network only requires a single observation; 2) It produces high-fidelity posterior distributions similar to sampling-based retrieval and; 3) It requires 75% less forward model computation to converge. 4.) We performed, for the first time, Bayesian model selection on our trained neural network. Our proposed framework contribute towards the latest development of a neural-powered atmospheric retrieval. Its flexibility and speed hold the potential to complement sampling-based approaches in large and complex data sets in the future.

FRECKLL: Full and Reduced Exoplanet Chemical Kinetics distiLLed

Abstract: We introduce a new chemical kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) to evolve large chemical networks efficiently. FRECKLL employs ‘distillation’ in computing the reaction rates, which minimizes the error bounds to the minimum allowed by double precision values ( (cid:15) ≤ 10 − 15 ). FRECKLL requires less than 5 minutes to evolve the full Venot2020 network in a 130 layers atmosphere and 30 seconds to evolve the Venot2020 reduced scheme. Pack-aged with FRECKLL is a TauREx 3.1 plugin for usage in forward modelling and retrievals. We present TauREx retrievals performed on a simulated HD189733 JWST spectra using the full and reduced Venot2020 chemical networks and demonstrate the viability of total disequilibrium chemistry retrievals and the ability for JWST to detect disequilibrium processes.

ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes

Abstract: The study of extra-solar planets, or simply, exoplanets, planets outside our own Solar System, is fundamentally a grand quest to understand our place in the Universe. Discoveries in the last two decades have re-defined our understanding of planets, and helped us comprehend the uniqueness of our very own Earth. In recent years the focus has shifted from planet detection to planet characterisation, where key planetary properties are inferred from telescope observations using Monte Carlo-based methods. However, the efficiency of sampling-based methodologies is put under strain by the high-resolution observational data from next generation telescopes, such as the James Webb Space Telescope and the Ariel Space Mission. We are delighted to announce the acceptance of the Ariel ML Data Challenge 2022 as part of the NeurIPS competition track. The goal of this challenge is to identify a reliable and scalable method to perform planetary characterisation. Depending on the chosen track, participants are tasked to provide either quartile estimates or the approximate distribution of key planetary properties. To this end, a synthetic spectroscopic dataset has been generated from the official simulators for the ESA Ariel Space Mission. The aims of the competition are three-fold. 1) To offer a challenging application for comparing and advancing conditional density estimation methods. 2) To provide a valuable contribution towards reliable and efficient analysis of spectroscopic data, enabling astronomers to build a better picture of planetary demographics, and 3) To promote the interaction between ML and exoplanetary science. The competition is open from 15th June and will run until early October, participants of all skill levels are more than welcomed!

Twinkle: a small satellite spectroscopy mission for the next phase of exoplanet science

Abstract: With a focus on off-the-shelf components, Twinkle is the first in a series of cost competitive small satellites managed and financed by Blue Skies Space Ltd. The satellite is based on a high-heritage Airbus platform that will carry a 0.45 m telescope and a spectrometer which will provide simultaneous wavelength coverage from 0.5–4.5 μm. The spacecraft prime is Airbus Stevenage while the telescope is being developed by Airbus Toulouse and the spectrometer by ABB Canada. Scheduled to begin scientific operations in 2025, Twinkle will sit in a thermally-stable, sun-synchronous, low-Earth orbit. The mission has a designed operation lifetime of at least seven years and, during the first three years of operation, will conduct two large-scale survey programmes: one focused on Solar System objects and the other dedicated to extrasolar targets. Here we present an overview of the architecture of the mission, refinements in the design approach, and some of the key science themes of the extrasolar survey.