arXiv subjects

Mario Morvan

Publications and source records attributed to Mario Morvan.

At least 19 recordsLinked to original sources

arXiv2023

Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimised Retrieval Framework

The chromatic contamination that arises from photospheric heterogeneities e.g. spots and faculae on the host star presents a significant noise source for exoplanet transmission spectra. If this contamination is not corrected for, it can introduce substantial bias in our analysis of the planetary atmosphere. We utilise two stellar models of differing complexity, StARPA and ASteRA, to explore the biases introduced by stellar contamination in retrieval under differing degrees of stellar activity. We use the retrieval framework TauREx3 and a grid of 27 synthetic, spot-contaminated transmission spectra to investigate potential biases and to determine how complex our stellar models must be in order to accurately extract the planetary parameters from transmission spectra. The input observation is generated using the more complex model (StARPA), in which the spot latitude is an additional, fixable parameter. This observation is then fed into a combined stellar-planetary retrieval which contains a simplified stellar model (ASteRA). Our results confirm that the inclusion of stellar activity parameters in retrieval minimises bias under all activity regimes considered. ASteRA performs very well under low to moderate activity conditions, retrieving the planetary parameters with a high degree of accuracy. For the most active cases, characterised by larger, higher temperature contrast spots, some minor residual bias remains due to ASteRA neglecting the interplay between the spot and the limb darkening effect. As a result of this, we find larger errors in retrieved planetary parameters for central spots (0 degrees) and those found close to the limb (60 degrees) than those at intermediate latitudes (30 degrees).

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arXiv2022

Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer

Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model -- called Denoising Time Series Transformer (DTST) -- and show that it excels at removing the noise and outliers in datasets of time series when trained with a masked objective, even when no clean targets are available. Moreover, the use of self-attention enables rich and illustrative queries into the learned representations. We present experiments on real stellar light curves from the Transiting Exoplanet Space Satellite (TESS), showing advantages of our approach compared to traditional denoising techniques.

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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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arXiv2021

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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arXiv2021

ARES V: No Evidence For Molecular Absorption in the HST WFC3 Spectrum of GJ 1132 b

We present a study on the spatially scanned spectroscopic observations of the transit of GJ 1132 b, a warm ($\sim$500 K) Super-Earth (1.13 R$_\oplus$) that was obtained with the G141 grism (1.125 - 1.650 $\mu$m) of the Wide Field Camera 3 (WFC3) onboard the Hubble Space Telescope. We used the publicly available Iraclis pipeline to extract the planetary transmission spectra from the five visits and produce a precise transmission spectrum. We analysed the spectrum using the TauREx3 atmospheric retrieval code with which we show that the measurements do not contain molecular signatures in the investigated wavelength range and are best-fit with a flat-line model. Our results suggest that the planet does not have a clear primordial, hydrogen-dominated atmosphere. Instead, GJ 1132 b could have a cloudy hydrogen-dominated envelope, a very enriched secondary atmosphere, be airless, or have a tenuous atmosphere that has not been detected. Due to the narrow wavelength coverage of WFC3, these scenarios cannot be distinguished yet but the James Webb Space Telescope may be capable of detecting atmospheric features, although several observations may be required to provide useful constraints.

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arXiv2020

ExoClock Project: An open platform for monitoring the ephemerides of Ariel targets with contributions from the public

The Ariel mission will observe spectroscopically around 1000 exoplanets to further characterise their atmospheres. For the mission to be as efficient as possible, a good knowledge of the planets' ephemerides is needed before its launch in 2028. While ephemerides for some planets are being refined on a per-case basis, an organised effort to collectively verify or update them when necessary does not exist. In this study, we introduce the ExoClock project, an open, integrated and interactive platform with the purpose of producing a confirmed list of ephemerides for the planets that will be observed by Ariel. The project has been developed in a manner to make the best use of all available resources: observations reported in the literature, observations from space instruments and, mainly, observations from ground-based telescopes, including both professional and amateur observatories. To facilitate inexperienced observers and at the same time achieve homogeneity in the results, we created data collection and validation protocols, educational material and easy to use interfaces, open to everyone. ExoClock was launched in September 2019 and now counts over 140 participants from more than 15 countries around the world. In this release, we report the results of observations obtained until the 15h of April 2020 for 119 Ariel candidate targets. In total, 632 observations were used to either verify or update the ephemerides of 83 planets. Additionally, we developed the Exoplanet Characterisation Catalogue (ECC), a catalogue built in a consistent way to assist the ephemeris refinement process. So far, the collaborative open framework of the ExoClock project has proven to be highly efficient in coordinating scientific efforts involving diverse audiences. Therefore, we believe that it is a paradigm that can be applied in the future for other research purposes, too.

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arXiv2020

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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arXiv2020

Hubble WFC3 Spectroscopy of the Habitable-zone Super-Earth LHS 1140 b

Atmospheric characterisation of temperate, rocky planets is the holy grail of exoplanet studies. These worlds are at the limits of our capabilities with current instrumentation in transmission spectroscopy and challenge our state-of-the-art statistical techniques. Here we present the transmission spectrum of the temperate Super-Earth LHS 1140b using the Hubble Space Telescope (HST). The Wide Field Camera 3 (WFC3) G141 grism data of this habitable zone (T$_{\rm{eq}}$ = 235 K) Super-Earth (R = 1.7 $R_\oplus$), shows tentative evidence of water. However, the signal-to-noise ratio, and thus the significance of the detection, is low and stellar contamination models can cause modulation over the spectral band probed. We attempt to correct for contamination using these models and find that, while many still lead to evidence for water, some could provide reasonable fits to the data without the need for molecular absorption although most of these cause also features in the visible ground-based data which are nonphysical. Future observations with the James Webb Space Telescope (JWST) would be capable of confirming, or refuting, this atmospheric detection.

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arXiv2020

ARES IV: Probing the atmospheres of the two warm small planets HD 106315 c and HD 3167 c with the HST/WFC3 camera

We present an atmospheric characterization study of two medium sized planets bracketing the radius of Neptune: HD 106315 c (R$_{\rm{P}}$=4.98 $\pm$ 0.23 R$_{\oplus}$) and HD 3167 c (R$_{\rm{P}}$=2.740$_{-0.100}^{+0.106}$ R$_{\oplus}$). We analyse spatially scanned spectroscopic observations obtained with the G141 grism (1.125 - 1.650 $\mu$m) of the Wide Field Camera 3 (WFC3) onboard the Hubble Space Telescope. We use the publicly available Iraclis pipeline and TauREx3 atmospheric retrieval code and we detect water vapor in the atmosphere of both planets with an abundance of $\log_{10}[\mathrm{H_2O}]=-2.1^{+0.7}_{-1.3}$ ($\sim$5.68$\sigma$) and $\log_{10}[\mathrm{H_2O}]=-4.1^{+0.9}_{-0.9}$ ($\sim$3.17$\sigma$) for HD 106315 c and HD 3167 c, respectively. The transmission spectrum of HD 106315 c shows also a possible evidence of ammonia absorption ($\log_{10}[\mathrm {NH_3}]=-4.3^{+0.7}_{-2.0}$, $\sim$1.97$\sigma$ -even if it is not significant-), whilst carbon dioxide absorption features may be present in the atmosphere of HD 3167 c in the $\sim$1.1-1.6~$\mu$m wavelength range ($\log_{10}[\mathrm{CO_{2}}]= -2.4^{+0.7}_{-1.0}$, $\sim$3.28$\sigma$). However the CO$_2$ detection appears significant, it must be considered carefully and put into perspective. Indeed, CO$_2$ presence is not explained by 1D equilibrium chemistry models, and it could be due to possible systematics. The additional contribution of clouds, CO and CH$_4$ are discussed. HD 106315 c and HD 3167 c will be interesting targets for upcoming telescopes such as the James Webb Space Telescope (JWST) and the Atmospheric Remote-Sensing Infrared Exoplanet Large-Survey (Ariel).

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arXiv2020

PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch

We present a new open source python package, based on PyLightcurve and PyTorch, tailored for efficient computation and automatic differentiation of exoplanetary transits. The classes and functions implemented are fully vectorised, natively GPU-compatible and differentiable with respect to the stellar and planetary parameters. This makes PyLightcurve-torch suitable for traditional forward computation of transits, but also extends the range of possible applications with inference and optimisation algorithms requiring access to the gradients of the physical model. This endeavour is aimed at fostering the use of deep learning in exoplanets research, motivated by an ever increasing amount of stellar light curves data and various incentives for the improvement of detection and characterisation techniques.

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arXiv2020

Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency's upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future. Finally, we present the solutions obtained by the top-5 winning teams, provide their code and discuss their implications. Successful solutions either construct highly non-linear (w.r.t. the raw data) models with minimal preprocessing -deep neural networks and ensemble methods- or amount to obtaining meaningful statistics from the light curves, constructing linear models on which yields comparably good predictive performance.

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arXiv2020

KELT-11 b: Abundances of water and constraints on carbon-bearing molecules from the Hubble transmission spectrum

In the past decade, the analysis of exoplanet atmospheric spectra has revealed the presence of water vapour in almost all the planets observed, with the exception of a fraction of overcast planets. Indeed, water vapour presents a large absorption signature in the wavelength coverage of the Hubble Space Telescope's (HST) Wide Field Camera 3 (WFC3), which is the main space-based observatory for atmospheric studies of exoplanets, making its detection very robust. However, while carbon-bearing species such as methane, carbon monoxide and carbon dioxide are also predicted from current chemical models, their direct detection and abundance characterisation has remained a challenge. Here we analyse the transmission spectrum of the puffy, clear hot-Jupiter KELT-11 b from the HST WFC3 camera. We find that the spectrum is consistent with the presence of water vapor and an additional absorption at longer wavelengths than 1.5um, which could well be explained by a mix of carbon bearing molecules. CO2, when included is systematically detected. One of the main difficulties to constrain the abundance of those molecules is their weak signatures across the HST WFC3 wavelength coverage, particularly when compared to those of water. Through a comprehensive retrieval analysis, we attempt to explain the main degeneracies present in this dataset and explore some of the recurrent challenges that are occurring in retrieval studies (e.g: the impact of model selection, the use of free vs self-consistent chemistry and the combination of instrument observations). Our results make this planet an exceptional example of chemical laboratory where to test current physical and chemical models of hot-Jupiters' atmospheres.

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arXiv2020

On The Compatibility of Ground-based and Space-based Data: WASP-96 b, An Example

The study of exoplanetary atmospheres relies on detecting minute changes in the transit depth at different wavelengths. To date, a number of ground and space based instruments have been used to obtain transmission spectra of exoplanets in different spectral band. One common practice is to combine observations from different instruments in order to achieve a broader wavelength coverage. We present here two inconsistent observations of WASP-96 b, one by Hubble Space Telescope (HST) and the other by the Very Large Telescope (VLT). We present two key findings in our investigation: 1.) a strong water signature is detected via the HST WFC3 observations. 2.) A notable offset in transit depth (> 1100 ppm) can be seen when the ground-based and space-based observations are combined together. The discrepancy raises the question of whether observations from different instruments could indeed be combined together. We attempt to align the observations by including an additional parameter in our retrieval studies but are unable to definitively ascertain that the aligned observations are indeed compatible. The case of WASP-96 b signals that compatibility of instruments should not be assumed. While wavelength overlaps between instruments can help, it should be noted that combining datasets remains a risky business. The difficulty in combining observations also strengthens the need for next generation instruments which will possess broader spectral coverage.

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arXiv2020

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

We report follow-up observations of four transiting exoplanets, TRES-2b, HAT-P-22b, HAT-P-36b and XO-2b, as part of the Original Research By Young Twinkle Students (ORBYTS) programme. These observations were taken using the Las Cumbres Observatory Global Telescope Network's (LCOGT) robotic 0.4 m telescopes and were analysed using the HOlomon Photometric Software (HOPS). Such observations are key for ensuring accurate transit times for upcoming telescopes, such as the James Webb Space Telescope (JWST), Twinkle and Ariel, which may seek to characterise the atmospheres of these planets. The data have been uploaded to ExoClock and a significant portion of this work has been completed by secondary school students in London.

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arXiv2020

ARES III: Unveiling the Two Faces of KELT-7 b with HST WFC3

We present the analysis of the hot-Jupiter KELT-7b using transmission and emission spectroscopy from the Hubble Space Telescope (HST), both taken with the Wide Field Camera 3 (WFC3). Our study uncovers a rich transmission spectrum which is consistent with a cloud-free atmosphere and suggests the presence of H2O and H-. In contrast, the extracted emission spectrum does not contain strong absorption features and, although it is not consistent with a simple blackbody, it can be explained by a varying temperature-pressure profile, collision induced absorption (CIA) and H-. KELT-7 b had also been studied with other space-based instruments and we explore the effects of introducing these additional datasets. Further observations with Hubble, or the next generation of space-based telescopes, are needed to allow for the optical opacity source in transmission to be confirmed and for molecular features to be disentangled in emission.

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arXiv2020

ARES II: Characterising the Hot Jupiters WASP-127 b, WASP-79 b and WASP-62 b with HST

This paper presents the atmospheric characterisation of three large, gaseous planets: WASP-127b, WASP-79b and WASP-62b. We analysed spectroscopic data obtained with the G141 grism (1.088 - 1.68 $\mu$m) of the Wide Field Camera 3 (WFC3) onboard the Hubble Space Telescope (HST) using the Iraclis pipeline and the TauREx3 retrieval code, both of which are publicly available. For WASP-127 b, which is the least dense planet discovered so far and is located in the short-period Neptune desert, our retrieval results found strong water absorption corresponding to an abundance of log(H$_2$O) = -2.71$^{+0.78}_{-1.05}$, and absorption compatible with an iron hydride abundance of log(FeH)=$-5.25^{+0.88}_{-1.10}$, with an extended cloudy atmosphere. We also detected water vapour in the atmospheres of WASP-79 b and WASP-62 b, with best-fit models indicating the presence of iron hydride, too. We used the Atmospheric Detectability Index (ADI) as well as Bayesian log evidence to quantify the strength of the detection and compared our results to the hot Jupiter population study by Tsiaras et al. 2018. While all the planets studied here are suitable targets for characterisation with upcoming facilities such as the James Webb Space Telescope (JWST) and Ariel, WASP-127 b is of particular interest due to its low density, and a thorough atmospheric study would develop our understanding of planet formation and migration.

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arXiv2020

ARES I: WASP-76 b, A Tale of Two HST Spectra

We analyse the transmission and emission spectra of the ultra-hot Jupiter WASP-76b, observed with the G141 grism of the Hubble Space Telescope's Wide Field Camera 3 (WFC3). We reduce and fit the raw data for each observation using the open-source software Iraclis before performing a fully Bayesian retrieval using the publicly available analysis suite TauRex 3. Previous studies of the WFC3 transmission spectra of WASP-76 b found hints of titanium oxide (TiO) and vanadium oxide (VO) or non-grey clouds. Accounting for a fainter stellar companion to WASP-76, we reanalyse this data and show that removing the effects of this background star changes the slope of the spectrum, resulting in these visible absorbers no longer being detected, eliminating the need for a non-grey cloud model to adequately fit the data but maintaining the strong water feature previously seen. However, our analysis of the emission spectrum suggests the presence of TiO and an atmospheric thermal inversion, along with a significant amount of water. Given the brightness of the host star and the size of the atmospheric features, WASP-76 b is an excellent target for further characterisation with HST, or with future facilities, to better understand the nature of its atmosphere, to confirm the presence of TiO and to search for other optical absorbers.

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arXiv2020

Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks

The precise derivation of transit depths from transit light curves is a key component for measuring exoplanet transit spectra, and henceforth for the study of exoplanet atmospheres. However, it is still deeply affected by various kinds of systematic errors and noise. In this paper we propose a new detrending method by reconstructing the stellar flux baseline during transit time. We train a probabilistic Long Short-Term Memory (LSTM) network to predict the next data point of the light curve during the out-of-transit, and use this model to reconstruct a transit-free light curve - i.e. including only the systematics - during the in-transit. By making no assumption about the instrument, and using only the transit ephemeris, this provides a general way to correct the systematics and perform a subsequent transit fit. The name of the proposed model is TLCD-LSTM, standing for Transit Light Curve Detrending LSTM. Here we present the first results on data from six transit observations of HD 189733b with the IRAC camera on board the Spitzer Space Telescope, and discuss some of its possible further applications.

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