Deep Learning, Shallow Dips: Transit light curves have never been so trendy

At the crossroad between photometry and time-domain astronomy, light curves are invaluable data objects to study distant events and sources of light even when they can not be spatially resolved. In particular, the field of exoplanet sciences has tremendously benefited from acquired stellar light curves to detect and characterise a majority of the outer worlds that we know today. Yet, their analysis is challenged by the astrophysical and instrumental noise often diluting the signals of interest. For instance, the detection of shallow dips caused by transiting exoplanets in stellar light curves typically require a precision of the order of 1 ppm to 100 ppm in units of stellar flux, and their very study directly depends upon our capacity to correct for instrumental and stellar trends. The increasing number of light curves acquired from space and ground-based telescopes—of the order of billions—opens up the possibility for global, efficient, automated processing algorithms to replace individual, parametric and hard-coded ones. Luckily, the field of deep learning is also progressing fast, revolutionising time series problems and applications. This reinforces the incentive to develop data-driven approaches hand-in-hand with existing scientific models and expertise. With the study of exoplanetary transits in focus, I developed automated approaches to learn and correct for the time-correlated noise in and across light curves. In particular, I present (i) a deep recurrent model trained via a forecasting objective to detrend individual transit light curves (e.g. from the Spitzer space telescope); (ii) the power of a Transformer-based model leveraging whole datasets of light curves (e.g. from large transit surveys) to learn the trend via a masked objective; (iii) a hybrid and flexible framework to combine neural networks with transit physics

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.Comment: 12 pages, 10 figures, 4 tables, accepted for publication in The Astronomical Journa

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.Comment: 7 pages, 3 figures; submission status updated, fig 2 caption adde

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.Comment: 24 pages, 14 figures, Accepted in A

The Transmission Spectrum of WASP-17 b From the Optical to the Near-infrared Wavelengths: Combining STIS, WFC3, and IRAC Data Sets

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 μm. Observations taken with IRAC channels 1 and 2 on the Spitzer Space Telescope are also included, adding photometric measurements at 3.6 and 4.5 μm. HST spectral data were analyzed with Iraclis, a pipeline specialized in the reduction of STIS and WFC3 transit and eclipse observations. Spitzer photometric observations were reduced with the TLCD-LSTM method, utilizing 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 modeling the WFC3 data alone, we can extract atmospheric information without having to deal with the contrasting STIS data sets. 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 aluminum oxide and titanium hydride. A retrieval that includes an extreme photospheric activity of the host star is the preferred model, but we recognize that such a scenario is unlikely for an F6-type star. Due to the incompleteness of all STIS spectral light curves, only further observations with this instrument would allow us to properly constrain the atmospheric limb of WASP-17 b, before the James Webb Space Telescope or Ariel will come online

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).Comment: 34 pages, 20 figures, accepted for publication in Ap

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.Comment: Accepted for publication in AJ on 30th October 202

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

Complex Molecules in the L1157 Molecular Outflow

We report the detection of complex organic molecules in the young protostellar outflow L1157. We identify lines from HCOOCH3, CH3CN, HCOOH and C2H5OH at the position of the B1 shock in the blueshifted lobe, making it the first time that complex species have been detected towards a molecular outflow powered by a young low-mass protostar. The time scales associated with the warm outflow gas (< 2,000 yr) are too short for the complex molecules to have formed in the gas phase after the shock-induced sputtering of the grain mantles. It is more likely that the complex species formed in the surface of grains and were then ejected from the grain mantles by the shock. The formation of complex molecules in the grains of low-mass star forming regions must be relatively efficient, and our results show the importance of considering the impact of outflows when studying complex molecules around protostars. The relative abundance with respect to methanol of most of the detected complex molecules is similar to that of hot cores and molecular clouds in the galactic center region, which suggests that the mantle composition of the dust in the L1157 dark cloud is similar to dust in those regions.Comment: 8 pages, 2 tables, 2 figure