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Safe operation of machine learning models requires architectures that explicitly delimit their operational ranges. We evaluate the ability of anomaly detection algorithms to provide indicators correlated with degraded model performance. By placing acceptance thresholds over such indicators, hard boundaries are formed that define the model's coverage. As a use case, we consider the extraction of exoplanetary spectra from transit light curves, specifically within the context of ESA's upcoming Ariel mission. Isolation Forests are shown to effectively identify contexts where prediction models are likely to fail. Coverage/error trade-offs are evaluated under conditions of data and concept drift. The best performance is seen when Isolation Forests model projections of the prediction model's explainability SHAP values., Comment: To appear in "Proceedings of SPAICE 2024: 1st ESA/IAA conference on AI in and for Space". Conference page at https://spaice.esa.int/

With the JWST offering higher resolution data in space-based transmission spectroscopy, understanding the capabilities of our current atmospheric retrieval pipelines is essential. These new data cover wider wavelength ranges and at much higher spectral resolution than previous instruments have been able to offer. Therefore, it is often appealing to bin spectra to fewer points, better constrained in their transit depth, before using them as inputs for atmospheric retrievals. As such, we produce a simulation replicating the observations of WASP-39b by the Near Infrared Spectrograph (NIRSpec) instrument on board JWST using the PRISM dispersion element. Then, we assess the accuracy and consistency of retrievals while varying both the resolution and the average photometric error of this simulated spectrum. We repeat this analysis on three different simulation setups where each includes an opaque cloud layer at a different height in the atmosphere. In agreement with previous studies, we find that a much greater resolution is needed in the case of a high cloud deck since features are already heavily muted by the presence of the clouds. In the other two cases, there are large 'safe zones' in the parameter space where accurate estimations are made. If these maps can be generalized, they could be used to inform future observations on how long to observe a given target in order to achieve the most accurate retrieval results. We also find that the resolution required to fully resolve the degeneracies between the parameters contributing to the spectra is much greater than that needed to constrain the marginalized posterior distributions for each parameter individually., Comment: Published in Monthly Notices of the Royal Astronomical Society, 27 pages, 30 figures

This work introduces an approach to enhancing the computational efficiency of 3D atmospheric simulations by integrating a machine-learned surrogate model into the OASIS global circulation model (GCM). Traditional GCMs, which are based on repeatedly numerically integrating physical equations governing atmospheric processes across a series of time-steps, are time-intensive, leading to compromises in spatial and temporal resolution of simulations. This research improves upon this limitation, enabling higher resolution simulations within practical timeframes. Speeding up 3D simulations holds significant implications in multiple domains. Firstly, it facilitates the integration of 3D models into exoplanet inference pipelines, allowing for robust characterisation of exoplanets from a previously unseen wealth of data anticipated from JWST and post-JWST instruments. Secondly, acceleration of 3D models will enable higher resolution atmospheric simulations of Earth and Solar System planets, enabling more detailed insights into their atmospheric physics and chemistry. Our method replaces the radiative transfer module in OASIS with a recurrent neural network-based model trained on simulation inputs and outputs. Radiative transfer is typically one of the slowest components of a GCM, thus providing the largest scope for overall model speed-up. The surrogate model was trained and tested on the specific test case of the Venusian atmosphere, to benchmark the utility of this approach in the case of non-terrestrial atmospheres. This approach yields promising results, with the surrogate-integrated GCM demonstrating above 99.0% accuracy and 147 factor GPU speed-up of the entire simulation compared to using the matched original GCM under Venus-like conditions., Comment: 20 pages, 14 figures

We present a population study of 20 exoplanets, ranging from Neptune-like to inflated hot-Jupiter planets, observed during transit with the STIS and WFC3 instruments aboard the Hubble Space Telescope. To obtain spectral information from the near-UV to the near-infrared, we reanalysed sixteen WFC3 and over fifty STIS archival data sets with our dedicated HST pipeline. We also include twenty-four WFC3 data sets previously reduced with the same software. Across our target sample we observe significant divergence among multiple observations conducted with the same STIS grating at various epochs, whilst we do not detect variations in the WFC3 data sets. These results are suggestive of stellar contamination, which we have investigated further using known Bayesian tools and other tailored metrics, facilitating a more objective assessment of stellar activity intensity within each system. Our findings reveal that stellar activity contaminates up to half of the studied exoplanet atmospheres, albeit at varying extents. Accounting for stellar activity can significantly alter planetary atmospheric parameters like molecular abundances (up to 6 orders of magnitude) and temperature (up to 145 %), contrasting with the results of analyses that neglect activity. Our results emphasise the importance of considering the effects of stellar contamination in exoplanet transit studies; this issue is particularly true for data sets obtained with facilities that do not cover the optical and/or UV spectral range where the activity is expected to be more impactful but also more easily detectable. Our results also provide a catalogue of potentially active stars for further investigation and monitoring., Comment: 69 pages, 19 figures, 24 tables, accepted for publication to ApJS

Conventional atmospheric retrieval codes are designed to extract information, such as chemical abundances, thermal structures and cloud properties, from fully "reduced" spectra obtained during transit or eclipse. Reduced spectra, however, are assembled by fitting a series of simplified light-curves to time series observations, wavelength-by-wavelength. Thus, spectra are a post-processed summary statistics of the original data, which by definition does not encode all the available information (i.e., astrophysical signal, model covariance, instrumental noise). Here, we explore an alternative inversion strategy where the atmospheric retrieval is performed on the light-curve directly -- i.e., closer to the data. This method is implemented in ExPLOR (EXoplanet Panchromatic Light-curve Observation and Retrieval), a novel atmospheric retrieval code inheriting from the TauREx project. By explicitly considering time in the model, ExPLOR naturally handles transit, eclipse, phase-curve and other complex geometries for transiting exoplanets. In this paper, we have validated this new technique by inverting simulated panchromatic light-curves. The model was tested on realistic simulations of a WASP-43b like exoplanet as observed with the James Webb Space Telescope (JWST) and Ariel telescope. By comparing our panchromatic light-curve approach against conventional spectral retrievals on mock scenarios, we have identified key breaking points in information and noise propagation when employing past literature techniques. Throughout the paper, we discuss the importance of developing "closer-to-data" approaches such as the method presented in this work, and highlight the inevitable increase in model complexity and computing requirements associated with the recent JWST revolution., Comment: Accepted to AJ, 22 pages, 11 figures

We present a comprehensive analysis of the Hubble Space Telescope observations of the atmosphere of WASP-121 b, a ultra-hot Jupiter. After reducing the transit, eclipse, and phase-curve observations with a uniform methodology and addressing the biases from instrument systematics, sophisticated atmospheric retrievals are used to extract robust constraints on the thermal structure, chemistry, and cloud properties of the atmosphere. Our analysis shows that the observations are consistent with a strong thermal inversion beginning at ~0.1 bar on the dayside, solar to subsolar metallicity Z (i.e., -0.77 < log(Z) < 0.05), and super-solar C/O ratio (i.e., 0.59 < C/O < 0.87). More importantly, utilizing the high signal-to-noise ratio and repeated observations of the planet, we identify the following unambiguous time-varying signals in the data: i) a shift of the putative hotspot offset between the two phase-curves and ii) varying spectral signatures in the transits and eclipses. By simulating the global dynamics of WASP-121 b atmosphere at high-resolution, we show that the identified signals are consistent with quasi-periodic weather patterns, hence atmospheric variability, with signatures at the level probed by the observations (~5% to ~10%) that change on a timescale of ~5 planet days; in the simulations, the weather patterns arise from the formation and movement of storms and fronts, causing hot (as well as cold) patches of atmosphere to deform, separate, and mix in time., Comment: Accepted for publication in ApJS. 43 pages, 31 figures, 2 animations (available online at the journal)

We present an atmospheric analysis of LTT 9779 b, a rare planet situated in the hot Neptune desert, that has been observed with HST WFC3 G102 and G141. The combined transmission spectrum, which covers 0.8 - 1.6 $\mu$m, shows a gradual increase in transit depth with wavelength. Our preferred atmospheric model shows evidence for H$_{\rm 2}$O, CO$_{\rm 2}$ and FeH with a significance of 3.1 $\sigma$, 2.4 $\sigma$ and 2.1 $\sigma$, respectively. In an attempt to constrain the rate of atmospheric escape for this planet, we search for the 1.083 $\mu$m Helium line in the G102 data but find no evidence of excess absorption that would indicate an escaping atmosphere using this tracer. We refine the orbital ephemerides of LTT 9779 b using our HST data and observations from TESS, searching for evidence of orbital decay or apsidal precession, which is not found. The phase-curve observation of LTT 9779 b with JWST NIRISS should provide deeper insights into the atmosphere of this planet and the expected atmospheric escape might be detected with further observations concentrated on other tracers such as Lyman $\alpha$., Comment: Accepted for publication in AJ

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., Comment: Accepted for publication in ApJS

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., Comment: ICML 2022 Workshop: Machine Learning for Astrophysics

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!, Comment: 13 pages, Accepted in the Conference on Neural Information Processing Systems 2022 (NeurIPS 2022), Competition hosted on https://www.ariel-datachallenge.space/

This is an exciting era for exo-planetary exploration. The recently launched JWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are set to bring fresh insights to the convoluted processes of planetary formation and evolution and its connections to atmospheric compositions. However, with new opportunities come new challenges. The field of exoplanet atmospheres is already struggling with the incoming volume and quality of data, and machine learning (ML) techniques lands itself as a promising alternative. Developing techniques of this kind is an inter-disciplinary task, one that requires domain knowledge of the field, access to relevant tools and expert insights on the capability and limitations of current ML models. These stringent requirements have so far limited the developments of ML in the field to a few isolated initiatives. In this paper, We present the Atmospheric Big Challenge Database (ABC Database), a carefully designed, organised and publicly available database dedicated to the study of the inverse problem in the context of exoplanetary studies. We have generated 105,887 forward models and 26,109 complementary posterior distributions generated with Nested Sampling algorithm. Alongside with the database, this paper provides a jargon-free introduction to non-field experts interested to dive into the intricacy of atmospheric studies. This database forms the basis for a multitude of research directions, including, but not limited to, developing rapid inference techniques, benchmarking model performance and mitigating data drifts. A successful application of this database is demonstrated in the NeurIPS Ariel ML Data Challenge 2022., Comment: 21 pages, Accepted in RASTI, challenge accepted in NeurIPS 2022. Database link: https://doi.org/10.5281/zenodo.6770103

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-Tau, 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., Comment: 16 pages, 4 figures, Published in ApJ

In high-stakes applications of data-driven decision making like healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. Firstly, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Secondly, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this paper, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when $\Xt$, a degraded version of $\Xb$ with missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the \textit{conservative strategy} where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy we need to estimate posterior distribution $p(\Xb|\Xt)$, we use variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAE) which are designed to capture the underlying structure of features with missing values.

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., Comment: 17 pages, 11 figures. Accepted for publication in AJ

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., Comment: 32 pages, 22 figures. Submitted to AJ. Accepted July 20, 2021

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., Comment: Accepted for publication in AJ on 30th October 2020

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)., Comment: Accepted for publication in AJ

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., Comment: 20 pages, 7 figures, 2 tables, Submitted to The Astrophysics Journal (ApJ)

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., Comment: 18 pages, 13 figures. Submitted to ApJ, currently under review

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., Comment: Published in the Research Notes of the American Astronomical Society (RNAAS)

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., Comment: 19 pages, 11 figures, accepted in AJ on June 23, 2020

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., Comment: Accepted for publication in AJ

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., Comment: Accepted for publication in AJ

We report follow-up observations of transiting exoplanets that have either large uncertainties (>10 minutes) in their transit times or have not been observed for over three years. A fully robotic ground-based telescope network, observations from citizen astronomers and data from TESS have been used to study eight planets, refining their ephemeris and orbital data. Such follow-up observations are key for ensuring accurate transit times for upcoming ground and space-based telescopes which may seek to characterise the atmospheres of these planets. We find deviations from the expected transit time for all planets, with transits occurring outside the 1 sigma uncertainties for seven planets. Using the newly acquired observations, we subsequently refine their periods and reduce the current predicted ephemeris uncertainties to 0.28 - 4.01 minutes. A significant portion of this work has been completed by students at two high schools in London as part of the Original Research By Young Twinkle Students (ORBYTS) programme., Comment: Accepted for publication in MNRAS

Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections., Comment: 16 Pages, 6 Figures, 3 Tables, Presented in ECML-PKDD 2019

Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade, it is therefore paramount to upgrade current models and improve their reliability, completeness and numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light-curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the results obtained are more robust against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from HST/WFC3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to identify potential discrepancies between the data-sets., Comment: 14 pages, 15 figures,Submitted to ApJ