arXiv subjects

Giovanna Tinetti

Publications and source records attributed to Giovanna Tinetti.

At least 37 records · Page 2Linked to original sources

arXiv2022

Original Research By Young Twinkle Students (ORBYTS): Ephemeris Refinement of Transiting Exoplanets III

We report photometric follow-up observations of thirteen exoplanets (HATS-1 b, HATS2 b, HATS-3 b, HAT-P-18 b, HAT-P-27 b, HAT-P-30 b, HAT-P-55 b, KELT-4A b, WASP-25 b, WASP-42 b, WASP-57 b, WASP-61 b and WASP-123 b), as part of the Original Research By Young Twinkle Students (ORBYTS) programme. All these planets are potentially viable targets for atmospheric characterisation and our data, which were taken using the LCOGT network of ground-based telescopes, will be combined with observations from other users of ExoClock to ensure that the transit times of these planets continue to be well-known, far into the future.

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arXiv2022

The Ariel Target List: The Impact of TESS and the Potential for Characterising Multiple Planets Within a System

The ESA Ariel mission has been adopted for launch in 2029 and will conduct a survey of around one thousand exoplanetary atmospheres during its primary mission life. By providing homogeneous datasets, with a high SNR and wide wavelength coverage, Ariel will unveil the atmospheric demographics of these far-away worlds, helping to constrain planet formation and evolution processes on a galactic scale. Ariel seeks to undertake a statistical survey of a diverse population of planets and, therefore, the sample of planets from which this selection can be made is of the utmost importance. While many suitable targets have already been found, hundreds more will be discovered before the mission is operational. Previous studies have used predictions of exoplanet detections to forecast the available planet population by the launch date of Ariel, with the most recent noting that the Transiting Exoplanet Survey Satellite (TESS) alone should provide over a thousand potential targets. In this work, we consider the planet candidates found to date by TESS to show that, with the addition of already confirmed planets, Ariel will already have a more than sufficient sample to choose its target list from once these candidates are validated. We showcase the breadth of this population as well as exploring, for the first time, the ability of Ariel to characterise multiple planets within a single system. Comparative planetology of worlds orbiting the same star, as well as across the wider population, will undoubtedly revolutionise our understanding of planet formation and evolution.

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arXiv2022

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

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!

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arXiv2022

Five key exoplanet questions answered via the analysis of 25 hot Jupiter atmospheres in eclipse

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 which 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 analyse 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 JWST 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.

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arXiv2022

The transmission spectrum of WASP-17 b from the optical to the near-infrared wavelengths: combining STIS, WFC3 and IRAC datasets

We present the transmission spectrum of the inflated hot-Jupiter WASP-17 b, observed with the STIS and WFC3 instruments aboard the Hubble Space Telescope, allowing for a continuous wavelength coverage from ~0.4 to ~1.7 um. Observations taken with IRAC channel 1 and 2 on the Spitzer Space Telescope are also included, adding photometric measurements at 3.6 and 4.5 um. HST spectral data was analysed with Iraclis, a pipeline specialised in the reduction of STIS and WFC3 transit and eclipse observations. Spitzer photometric observations were reduced with the TLCD-LSTM method, utilising recurrent neural networks. The outcome of our reduction produces incompatible results between STIS visit 1 and visit 2, which leads us to consider two scenarios for G430L. Additionally, by modelling the WFC3 data alone, we can extract atmospheric information without having to deal with the contrasting STIS datasets. We run separate retrievals on the three spectral scenarios with the aid of TauREx 3, a fully Bayesian retrieval framework. We find that, independently of the data considered, the exoplanet atmosphere displays strong water signatures and potentially, the presence of aluminium oxide (AlO) and titanium hydride (TiH). A retrieval that includes an extreme photospheric activity of the host star is the preferred model, but we recognise that such a scenario is unlikely for an F6-type star. Due to the incompleteness of all STIS spectral lightcurves, only further observations with this instrument would allow us to properly constrain the atmospheric limb of WASP-17 b, before JWST or Ariel will come online.

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arXiv2022

Cross-sections for heavy atmospheres: H$_2$O continuum

Most of the exoplanets detected up to now transit in front of their host stars, allowing for the generation of transmission spectra; the study of exoplanet atmospheres relies heavily upon accurate analysis of these spectra. Recent discoveries mean that the study of atmospheric signals from low-mass, temperate worlds are becoming increasingly common. The observed transit depth in these planets is small and more difficult to analyze. Analysis of simulated transmission spectra for two small, temperate planets (GJ 1214 b and K2-18 b) is presented, giving evidence for significant differences in simulated transit depth when the water vapor continuum is accounted for when compared to models omitting it. These models use cross-sections from the CAVIAR lab experiment for the water self-continuum up to 10,000 cm$^{-1}$; these cross-sections exhibit an inverse relationship with temperature, hence lower-temperature atmospheres are the most significantly impacted. Including the water continuum strongly affects transit depths, increasing values by up to 80 ppm, with the differences for both planets being detectable with the future space missions Ariel and JWST. It is imperative that models of exoplanet spectra move toward adaptive cross-sections, increasingly optimized for H$_2$O-rich atmospheres. This necessitates including absorption contribution from the water vapor continuum into atmospheric simulations.

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arXiv2022

Cross-sections for heavy atmospheres: H$_2$O self-broadening

The discovery of super-Earth and mini-Neptune exoplanets means that atmospheric signals from low-mass, temperate exoplanets are being increasingly studied. The signal acquired as the planet transits its host star, known as the transit depth, is smaller for these planets and, as such, more difficult to analyze. The launch of the space telescopes James Webb (JWST) & Ariel will give rise to an explosion in the quality and quantity of spectroscopic data available for an unprecedented number of exoplanets in our galaxy. Accurately extracting the information content, thereby permitting atmospheric science, of such data-sets will require robust models and techniques. We present here the analysis of simulated transmission spectra for water-rich atmospheres, giving evidence for non-negligible differences in simulated transit depths when self-broadening of H$_2$O is correctly accounted for, compared with the currently typically accepted standard of using H$_2$ and He-broadened cross-sections. Our case-study analysis is carried out on two super-Earths, focusing on water-based atmospheres, ranging from H$_2$-rich to H$_2$O-rich. The transit depth is considerably affected, increasing values by up to 60 ppm, which is shown to be detectable with JWST and Ariel. The differences are most pronounced for the lighter (i.e. $mu$ $\sim$ 4) atmospheres. Our work illustrates that it is imperative that the field of exoplanet spectroscopy moves toward adapted cross-sections, increasingly optimized for high-mu atmospheres for studies of super-Earths and mini-Neptunes.

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arXiv2021

Tracing the formation history of giant planets in protoplanetary disks with Carbon, Oxygen, Nitrogen and Sulphur

The composition of giant planets is imprinted by their migration history and the compositional structure of their hosting disks. Studies in recent literature investigate how the abundances of C and O can constrain the formation pathways of giant planets forming within few tens of au from the star. New ALMA observations, however, suggest planet-forming regions possibly extending to hundreds of au. We explore the implications of these wider formation environments through n-body simulations of growing and migrating giant planets embedded in planetesimal disks, coupled with a compositional model of the protoplanetary disk where volatiles are inherited from the molecular cloud and refractories are calibrated against extrasolar and Solar System data. We find that the C/O ratio provides limited insight on the formation pathways of giant planets that undergo large-scale migration. This limitation can be overcome thanks to nitrogen and sulphur. Jointly using the C/N, N/O and C/O ratios breaks any degeneracy in the formation and migration tracks of giant planets. The use of elemental ratios normalized to the respective stellar ratios supplies additional information on the nature of giant planets, thanks to the relative volatility of O, C and N in disks. When the planetary metallicity is dominated by the accretion of solids C/N* $>$ C/O* $>$ N/O* (* denoting this normalized scale), otherwise N/O* $>$ C/O* $>$ C/N*. The S/N ratio provides an additional independent probe into the metallicity of giant planets and their accretion of solids.

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arXiv2021

A comparison of chemical models of exoplanet atmospheres enabled by TauREx 3.1

Thermochemical equilibrium is one of the most commonly used assumptions in current exoplanet retrievals. As the James Webb Space Telescope (JWST) and Ariel launch draw near, assessing the underlying biases and assumptions made when applying self-consistent chemistry into spectral retrievals is crucial. Here we use the flexibility of TauREx 3.1 to cross-compare three state of the art chemical equilibrium codes: ACE, FastChem and GGchem. We simulate JWST spectra for ACE, FastChem, GGchem and GGchem+condensation containing only C, H, O, N elements and spectra for FastChem, GGchem and GGchem+condensation with a more extensive range of elements giving seven simulated JWST spectra in total and then cross-retrieve giving a total of 49 retrievals. Our analysis demonstrates that like-for-like, all chemical codes retrieve the correct parameters to <1% of the truth. However, retrievals, where the contained elements do not match the truth, parameters such as metallicity deviate by 20% while maintaining extremely low uncertainties <1% giving false confidence. This point is of major importance for future analyses on JWST and Ariel, highlighting that self-consistent chemical scheme that do not employ the proper assumptions (missing species, fixed elemental ratios, condensation) are at risk of confidently biasing interpretations. Free chemistry retrievals employing parametric descriptions of the chemical profiles can provide alternative unbiased explorations.

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arXiv2021

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 $0.5 \to 7.8 \; μm$. 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 to select candidate planets to be reobserved in a ARIEL's higher resolution Tier; (2) we propose a metric to preliminary classify exoplanets by their atmospheric composition without performing an atmospheric retrieval; (3) we introduce the possibility to find other methods to better exploit the data scientific content.

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arXiv2021

Disentangling Atmospheric Compositions of K2-18 b with Next Generation Facilities

Recent analysis of the planet K2-18b has shown the presence of water vapour in its atmosphere. While the H2O detection is significant, the Hubble Space Telescope (HST) WFC3 spectrum suggests three possible solutions of very different nature which can equally match the data. The three solutions are a primary cloudy atmosphere with traces of water vapour (cloudy sub-Neptune), a secondary atmosphere with a substantial amount (up to 50% Volume Mixing Ratio) of H2O (icy/water world) and/or an undetectable gas such as N2 (super-Earth). Additionally, the atmospheric pressure and the possible presence of a liquid/solid surface cannot be investigated with currently available observations. In this paper we used the best fit parameters from Tsiaras et al. (2019) to build James Webb Space Telescope (JWST) and Ariel simulations of the three scenarios. We have investigated 18 retrieval cases, which encompass the three scenarios and different observational strategies with the two observatories. Retrieval results show that twenty combined transits should be enough for the Ariel mission to disentangle the three scenarios, while JWST would require only two transits if combining NIRISS and NIRSpec data. This makes K2-18b an ideal target for atmospheric follow-ups by both facilities and highlights the capabilities of the next generation of space-based infrared observatories to provide a complete picture of low mass planets.

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arXiv2021

TauREx III: A fast, dynamic and extendable framework for retrievals

TauREx 3 is the next generation of the TauREx exoplanet atmospheric retrieval framework for Windows, Mac, and Linux. It is a complete rewrite with a full Python stack that makes it easy-to-use, high-performance, dynamic, and flexible. The new main TauREx program is built with modularity in mind, allowing the user to augment its functionalities with custom code and efficiently perform retrievals on custom parameters. We achieve this result by dynamic determination of fitting parameters, whereby TauREx 3 can detect new parameters for retrieval from user code through a simple interface. TauREx 3 can act as a library with a simple 'import taurex' command, providing a rich set of classes and functions related to atmospheric modelling. A 10x speedup in forward model computations is achieved as compared to the previous version with a sixfold reduction in retrieval times while maintaining robust results. TauREx 3 is intended as a standalone, all-in-one package for retrievals while the TauREx 3 Python library can build or augment a user's custom data pipeline easily.

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arXiv2021

Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals

Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being 'black boxes'. It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us - among other things - of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that for different molecules, the wavelength ranges to which the DNN's predictions are most sensitive, indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions.

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arXiv2021

An exploration of model degeneracies with a unified phase curve retrieval analysis: The light and dark sides of WASP-43 b

The analysis of exoplanetary atmospheres often relies upon the observation of transit or eclipse events. While very powerful, these snapshots provide mainly 1-dimensional information on the planet structure and do not easily allow precise latitude-longitude characterisations. The phase curve technique, which consists of measuring the planet emission throughout its entire orbit, can break this limitation and provide useful 2-dimensional thermal and chemical constraints on the atmosphere. As of today however, computing performances have limited our ability to perform unified retrieval studies on the full set of observed spectra from phase curve observations at the same time. Here, we present a new phase curve model that enables fast, unified retrieval capabilities. We apply our technique to the combined phase curve data from the Hubble and Spitzer space telescopes of the hot-Jupiter WASP-43 b. We tested different scenarios and discussed the dependence of our solution to different assumptions in the model. Our more comprehensive approach suggests that multiple interpretation of this dataset are possible but our more complex model is consistent with the presence of thermal inversions and a metal rich atmosphere, contrasting with previous data analyses, although this likely depends on the Spitzer data reduction. The detailed constraints extracted here demonstrate the importance of developing and understanding advanced phase curve techniques, which we believe will unlock access to a richer picture of exoplanet atmospheres.

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arXiv2021

Ariel: Enabling planetary science across light-years

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution.

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arXiv2021

Detectability of Rocky-Vapour Atmospheres on Super-Earths with Ariel

Ariel will mark the dawn of a new era as the first large-scale survey characterising exoplanetary atmospheres with science objectives to address fundamental questions about planetary composition, evolution and formation. In this study, we explore the detectability of atmospheres vaporised from magma oceans on dry, rocky Super-Earths orbiting very close to their host stars. The detection of such atmospheres would provide a definitive piece of evidence for rocky planets but are challenging measurements with currently available instruments due to their small spectral signatures. However, some of the hottest planets are believed to have atmospheres composed of vaporised rock, such as Na and SiO, with spectral signatures bright enough to be detected through eclipse observations with planned space-based telescopes. In this study, we find that rocky super-Earths with a irradiation temperature of 3000 K and a distance from Earth of up to 20 pc, as well as planets hotter than 3500 K and closer than 50 pc, have SiO features which are potentially detectable in eclipse spectra observed with Ariel.

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arXiv2021

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.

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arXiv2020

Earth as a Proxy Exoplanet: Deconstructing and Reconstructing Spectrophotometric Light Curves

Point source spectrophotometric ("single-point") light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significant for determining habitability on exoplanets. Given that Earth is the only celestial body confirmed to harbor life, treating it as a proxy exoplanet by analyzing time-resolved spectral images provides a benchmark in the search for habitable exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) provides such an opportunity, with observations of ~5000 full-disk sunlit Earth images each year at ten wavelengths with high temporal frequency. We disk-integrate these spectral images to create single-point light curves and decompose them into principal components (PCs). Using machine learning techniques to relate the PCs to six preselected spatial features, we find that the first and fourth PCs of the single-point light curves, contributing ~83.23% of the light curve variability, contain information about low and high clouds, respectively. Surface information relevant to the contrast between land and ocean reflectance is contained in the second PC, while individual land sub-types are not easily distinguishable (<0.1% total light curve variation). We build an Earth model by systematically altering the spatial features to derive causal relationships to the PCs. This model can serve as a baseline for analyzing Earth-like exoplanets and guide wavelength selection and sampling strategies for future observations.

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