Detecting and characterizing exoplanet atmospheres remains challenging because atmospheric signals can be comparable to residual noise and instrumental/astrophysical systematics. Spectral features span from a few ppm for small planets up to $\sim 10<^>3$ ppm for warm/hot giants, while high-quality James Webb Space Telescope (JWST) time-series spectroscopy typically reaches $\sim 10$-50 ppm (occasionally $\sim 100$-200 ppm in the presence of stellar variability or stronger systematics), making correlated noise across temporal and spectral dimensions a key limitation. With JWST delivering an increasing volume of high-precision transmission spectra, and Ariel set to extend this to a homogeneous survey of $\sim 10<^>3$ exoplanet atmospheres, robust benchmarking resources with known ground truth are essential to develop and validate data-driven (including ML-based) detrending approaches. As a major step towards this goal, we use ExoSim2 and TauREx to generate one of the most comprehensive public data sets based on the current payload design of the ESA Ariel mission, specifically intended to benchmark detrending algorithms. We also provide a deep neural network baseline for time-series reduction, and use it to highlight the limitations of ML based detrendng methods, i.e. the risks posed by data set shift when observed distributions diverge from those of the training set, a scenario likely to arise in real observations. This data set is featured in the Ariel Data Challenge 2024 on Kaggle and has been field-tested for robustness and simulation fidelity. By making these resources publicly available, we aim to support the community in developing, comparing, and stress-testing scalable and reliable methods for exoplanet transmission spectroscopy.

The EXoplanet Climate Infrared TElescope (EXCITE) is a balloon-borne mission dedicated to measuring spectroscopic phase curves of hot Jupiter-type exoplanets. Phase curve measurements can be used to characterize an exoplanet's longitude-dependent atmospheric composition and energy circulation patterns. EXCITE carries a 0.5 m primary mirror and a moderate resolution diffraction-limited spectrograph with spectral coverage from 0.8 to 3.5 μm. EXCITE is designed to fly from a long-duration balloon. EXCITE will observe through the peak of a target's spectral energy distribution and through spectral signatures of hydrogen and carbon-containing molecules. In this paper, we present the science goals of EXCITE, detail the as-built instrument, and discuss its performance during a 2024 engineering flight from Fort Sumner, New Mexico.

Abstract Creating a new observatory or optical system includes the development of an alignment plan , the procedure for assembling parts and subsystems into a fully functioning observatory. Part of developing the alignment plan is choosing which metrology method is the best fit for each step, ideally with an emphasis on reducing cost and complexity. In this paper, we show how lateral shearing interferometry can be used for quick, low-cost verification of diffraction-limited performance of an optical system using a so-called horizontal line test . This horizontal line test does not require numerical methods and is relatively simple to implement, making it a low-complexity and flexible metrological method to include as part of an alignment plan. We discuss the optical theory behind this method, and provide specific examples of aberration testing with both simulated and laboratory demonstrations. We also discuss how linear shearing interferometry can be applied to observatory alignment plans, using the NASA EXoplanet Climate Infrared TElescope spectrograph as an example of real-life implementation. Overall, the lateral shearing plate horizontal line test is particularly useful in situations where logistics constrain available resources, such as deployment in a remote location like Antarctica.

Context. Long-period transiting planets are essential edge-cases for understanding exoplanet formation and evolution. Their detection is challenging due to the infrequent nature of their transits, making every observation of these objects very valuable. Aims. Our goal is to confirm and further characterise the long-period planetary candidate TOI-4409 b (initially identified by TESS) through additional observations using the Antarctica-based ASTEP telescope, supplemented with CHAT, OMES, LCOGT, and PEST light-curve data and radial-velocity (RV) measurements collected with the HARPS, FEROS, and PFS instruments. Methods. We jointly analysed the photometric data from eight TESS, four ASTEP, one CHAT, two OMES, four LCOGT, and one PEST light curve(s), and RV data from the HARPS, FEROS, and PFS instruments, covering observations from 2018 to 2024. Results. TOI-4409 b is a bona fide puffy warm super-Neptune exoplanet with a planet radius of 7.44 ± 0.19 R⊕ and a mass of 0.047 ± 0.017 MJ (3σ upper limit <0.095 MJ) orbiting a low-mass main-sequence star (effective temperature of 4928 ± 48 K and a stellar radius of 0.720 ± 0.018 R⊙) with a period of 92.49179 ± 0.00010 days. The integration of various observational methods and consistent transit signals across multiple instruments confirms the planetary nature of TOI-4409 b. The combined transit, imaging, chromaticity, and RV evidence supports the confirmation of the planet. Conclusions. With a radius of 7.44 ± 0.19 R⊕ and a low bulk density of 0.20 ± 0.07 g cm−3, TOI-4409 b occupies a sparsely populated region of parameter space among warm, puffy super-Neptunes on long-period orbits. The detection of candidate transit timing variations with an ~10-minute semi-amplitude hints at the presence of an additional body in the system. The favourable host-star brightness and extended transit duration (~7 hours) make TOI-4409 b a promising target for atmospheric characterisation with JWST and Ariel.

Since the launch of JWST, observations of exoplanetary atmospheres have seen a revolution in data quality. Given that atmospheric parameter inferences depend heavily on the underlying data, a re-evaluation of current methodologies is warranted to assess the reliability of these results. We investigate the impact of variations in input spectra on atmospheric retrievals for the hot Jupiter WASP-39 b using JWST transit data. Specifically, we analyse the reliability of parameter estimations from random perturbations of the underlying spectrum and their sensitivity to three transmission spectra derived from the same observational data. Using the NIRSpec PRISM observation from a single transit of WASP-39 b, we perform retrievals with the TauREx framework. As a baseline, we use a spectrum derived with the Eureka! data reduction pipeline. To evaluate retrieval reliability, we analyse posterior distributions under deviations from this spectrum. We simulate random noise by performing retrievals on scattered instances of this spectrum and compare them with retrievals based on existing spectra reduced from the same raw observation. Our analysis identifies three types of posterior distributions: (1) Stable, Gaussian distributions for species constrained across the entire spectrum (e.g., H2O, CO2); (2) Uniform posteriors with upper bounds for weakly constrained species (e.g., CO, CH4); and (3) Unstable, heavy-tailed posteriors for species constrained by minor spectrum features (e.g., SO2, C2H2). We find that other parameters, such as the planetary radius and p-T profile, are stable under spectral perturbations. Posterior distributions differ for retrievals on independently reduced transmission spectra from the same raw data, complicating interpretation, particularly for skewed distributions. Based on this, we advocate for careful assessment and selection of credible interval sizes to reflect this.

Abstract Knowledge of the constituents of the Martian surface and their distributions over the planet informs us about Mars’ geomorphological formation and evolutionary history. In this research, an unsupervised end-to-end unmixing model using autoencoders is proposed, that can produce physically plausible abundance maps of the surface mineralology from hyperspectral imaging data. 1 Introduction Mars’ formationary and evolutionary history can be inferred by studying the mineralogical constituents of the Martian surface and their distributions. Knowledge of the specific minerals present and their abundances constrain the possible histories of the planet’s surface (Liu et al., 2016). Hyperspectral imaging of Mars from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM; Murchie et al., 2007) provides unprecedented insight into the distribution of surface mineralogy. Usually, minerals are identified from spectral absorption features and comparison to spectral libraries (see Viviano-Beck et al., 2014). This can be incredibly time consuming, and relies upon detailed a priori knowledge of the surface chemistry and geology. In order to obtain constituent mineralogical spectra (endmembers) and their fractional abundances one must perform spectral unmixing. Machine learning methods have been used extensively for Earth based hyperspectral imaging analysis, but only a few works to our knowledge have attempted to produce end-to-end unmixing models: Guo et al. (2015); Palsson et al. (2018, 2017); Zhang et al. (2018). Data obtained from hyperspectral imaging of Mars is essentially of the same format as that obtained from Earth, so similar kinds of analysis can be performed. 2 Method Signal decomposition techniques, namely principal component analysis (PCA) and independent component analysis (ICA) are used in order to investigate the presence - and the approximate number - of distinct, clearly detectable signals (component spectra) within CRISM imaging data. Clustering algorithms are then used to obtain an alternative estimate of the number of distinct identifiable mineralogical components present within the spectra. The intuition is that the optimal number of clusters identified should match the number of components determined through the use of signal decomposition techniques. Autoencoders (a type of neural network consisting of an encoder and decoder function that are trained to reproduce their input to their output) are then trained on the spectra from the image. They learn an approximation of the LMM whereby the weights of the decoder correspond to the endmember spectra and the activations of the encoder are the fractional abundances of the endmembers for a given spectrum (Palsson et al., 2018, 2017). The number of endmembers in the learned approximation to the LMM are then set as the estimates of the number of component signals from PCA and clustering. Figure 1 shows a schematic of this. Learned endmembers are extracted from the networks and abundance maps are produced by projecting the activation of each neuron in the decoder to the pixel location of the input spectrum for all of the spectra in the image. These techniques all get around the requirement of detailed a priori knowledge by making no assumptions about the state of the surface. To our knowledge, this is the first work to address the use of unsupervised neural networks for an end-to-end spectral unmixing of Martian hyperspectral images. Figure 1: Schematic view of an autoencoder used for spectral unmixing. 3 Results and Conclusion The signal decomposition techniques provide strong evidence for the presence of detectable mineralogical signals within the data, and the number of signal components estimated from PCA are consistent with the estimates from clustering. PCA, ICA, and the abundance maps produced by the trained autoencoders all appear to convincingly replicate features of the surface topography. Figure 2 shows an example of this, comparing the quicklook image for phyllosilicates (a false colour RGB image constructed from wavelengths using spectral parameters; Viviano-Beck et al., 2014) to one of the produced abundance maps. This provides strong evidence that these methods have learnt mineralogically significant spectra, and that the autoencoders have learnt a physically plausible approximation to the LMM. This is despite the fact that at no spatial information is provided at any point to any of these methods – their results are purely based on spectral information. Hence, when combined with methods to estimate the number of endmembers present, one can construct an unsupervised end-to-end linear unmixing model using autoencoders, that learns to identify physically plausible mineralogical endmembers. Once trained, this pipeline will allow the large number of CRISM data sets to be characterised and abundances mapped over the surface of the planet. This method is also extendable to any kind of planetary hyperspectral imaging, allowing simple mineralogical mapping on an unprecedented scale. Figure 2: Quicklook image for phyllosilicates (a false colour RGB image made using spectral parameters; Viviano-Beck et al., 2014) alongside one of the produced abundance maps. References Guo, R., et al. (2015). Hyperspectral image unmixing using autoencoder cascade. In 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), volume 2015-June, pages 1–4. IEEE. Liu, Y., et al. (2016). End-member identi- fication and spectral mixture analysis of CRISM hy- perspectral data: A case study on southwest Melas Chasma, Mars. Journal of Geophysical Research: Planets, 121(10):2004–2036. Murchie, S., et al. (2007). Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on Mars Reconnaissance Orbiter (MRO). Journal of Geophysical Re- search, 112(E5):E05S03. Palsson, B., et al. (2018). Hyperspectral Unmixing Using a Neural Network Autoencoder. IEEE Access, 6:25646–25656. Palsson, F., et al. (2017). Neural network hyperspec- tral unmixing with spectral information divergence objective. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 755–758. IEEE. Viviano-Beck, et al. (2014). Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars. Journal of Geophysical Research E: Planets, 119(6):1403–1431. Zhang, X., et al. (2018). Hyperspectral Unmixing via Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 15(11):1755–1759.

The EXoplanet Climate Infrared TElescope (EXCITE) is an instrument designed to measure spectroscopic phase curves of extrasolar hot Jupiters from a long duration balloon platform. EXCITE will fly a moderate resolution spectrometer housed inside of a cryogenic receiver actively cooled by two linear pulse tube cryocoolers. Here we provide the current status of the design and performance of the cryogenic receiver, its heat rejection mechanism, and associated control electronics. A recirculating methanol fluid loop rejects heat from the cryocoolers and transports it to sky-facing radiator panels mounted to the gondola. The cryocoolers are controlled by drive electronics with active vibration reduction functionality to minimize the impact of vibrations on pointing stability. We discuss the thermal and vibrational performance of the cryogenic receiver during ground-based pointing tests in its 2023 field campaign in Ft. Sumner, NM and present its current status as EXCITE prepares for its 2024 test flight campaign.

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 time frames. Speeding up 3D simulations holds significant implications in multiple domains. First, it facilitates the integration of 3D models into exoplanet inference pipelines, allowing for robust characterization 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 per cent accuracy and factor of 147 speed-up of the entire simulation executed on one graphics processing unit (GPU) compared to using the matched original GCM under Venus-like conditions.

AbstractTauREx 3.1 is the next version of the open-source python retrieval framework TauREx 3[1], which is backward-compatible with the previous version but offers a swathe of improvements and optimizations to the overall architecture. This version upgrade includes support for k-tables, non-uniform priors, and H- opacities. The most major inclusion in this version of TauREx is the plugin system, which allows any developer to imbue TauREx 3 with new features without touching the main codebase. Plugins are installable additions that TauREx 3 will detect and automatically include in its Bayesian retrieval pipeline. They can be installed from PyPI through pip install or directly from a git repository. They can consist of collections of new chemistries, temperature profiles, contribution functions, stellar and planetary models, non-uniform prior functions, opacity formats, and forward models, to name a few. Anyone can host and develop them, and they are designed so that all are interoperable with each other. Presented are the CUDA and OpenCL plugins, which provide GPU accelerated forward models. Fastchem[2], GGchem[3], and disequilibrium[4] chemical plugins and petitRADTRANS[5] and phase curve forward model plugins. Presented are Forward models and retrievals that exploit the plugins IntroductionExoplanetary atmospheres are multi-faceted physical phenomena that touch many scientific fields. Chemical modeling, cloud physics, fluid dynamics, orbital mechanics and molecular spectroscopy are some of the physical processes that need to be appropriately handled in a retrieval framework to characterize these complex environments correctly. With the upcoming JWST and Ariel telescope expected to bring a higher density of information, it is vital that the many codes and contributions of a wide variety of fields can be exploited for characterization. PluginsTauREx 3 allowed for the inclusion of custom codes in the pipeline. TauREx 3.1 provides a means for which a developer can develop and distribute their custom codes through plugins. Plugins exploit the python packaging system to allow developers to make their codes installable and easily used in retrievals without modification of the main TauREx 3 codebase. The installation is an essential aspect as this allows many FORTRAN and C++ codes to be automatically compiled or binaries distributed. TauREx will then automatically detect these plugins and integrate them into its retrieval pipeline. Plugins can provide replacements for all components in the TauREx 3 framework. For example, if a user wishes to use the GGchem chemistry code in atmospheric retrievals, they can simply write pip install taurex_ggchem, which will automatically download a precompiled GGchem library and its data files and install it alongside a new TauREx chemistry component. A user can then immediately include GGchem for both forward models and retrievals in input files or python scripts. A single plugin can consist of any number of new replacement atmospheric components and can make use of FORTRAN, C++, and python codes/libraries. TauREx-CUDA/OpenCLTauREx-CUDA and TauREx-OpenCL are plugins that, when installed, provide replacement forward models that take advantage of heterogeneous computing. These replacement forward models allow the optical depth calculation to execute on hardware accelerators such as Nvidia, AMD, and Intel GPUs to be easily exploited for a 25--50x speed up in both forward models and retrievals. TauREx-GGchem/Fastchem/ACE/DisequilibriumThe four plugins TauREx-ACE, TauREx-Fastchem, and TauREx-GGchem, are installable components that provide the ACE[6], Fastchem[2] and GGchem[3] equilibrium codes for use in both forward modeling and retrievals. These can be easily installed and used in Windows, Mac, and Linux through PyPi and provide their full capabilities for both forward modeling (Figure 1) and retrievals (Figure 2). Also included is a new plugin for a photochemical kinetic solver[4] that provides disequilibrium chemistry with retrieval capabilities (Figure 3). Figure 1. Plots of simulated JWST transmission spectra from different chemistry schemes. Figure 2. Posteriors of ACE (blue), Fastchem (red), GGchem (green) and GGchem with condensation (orange) Figure 3. Active molecular profiles using a photochemical kinetic solver[4] with retrieval sampling uncertainties. Forward Model pluginsPlugins can include entirely new forward models. The TauREx-petitRADTRANS plugin that utilizes petitRADTRANS[5] code to compute the transmission and emission spectra. It demonstrates the interoperable nature of plugins. Running petitRADTRANS under the TauREx 3.1 framework gives it the ability to use all available chemistries, including ACE, Fastchem, GGchem, and disequilibrium codes, as well as retrievals utilizing nested sampling with non-uniform priors. The TauREx-phase plugin implements a phase curve forward model for retrievals and can exploit all plugins, including the equilibrium chemistry and CUDA, for self-consistent accelerated optimizations. SummaryThe plugin system in TauREx 3.1 aims to simplify the inclusion of new atmospheric parameters and external codes. TauREx 3.1 is available at http://github.com/ucl-exoplanets/TauREx3_public or through PyPi. Each plugin is also available through both in the ucl-exoplanets GitHub page and PyPi. References[1] Ahmed F. Al-Refaie, Quentin Changeat, Ingo P. Waldmann, and Giovanna Tinetti. Taurex III: A fast, dynamic and extendable framework for retrievals, 2019.[2] Joachim W Stock, Daniel Kitzmann, A Beate C Patzer, and Erwin Sedlmayr. FastChem: A computer program for efficient complex chemical equilibrium calculations in the neutral/ionized gas phase with applications to stellar and planetary atmospheres .Monthly Notices of the Royal Astronomical Society,479(1):865–874, 06 2018.[3] Woitke, P., Helling, Ch., Hunter, G. H., Millard, J. D., Turner, G. E.,Worters, M., Blecic, J., and Stock, J. W. Equilibrium chemistry down to 100 k - impact of silicates and phyllosilicates on the carbon to oxygen ratio..A&A, 614:A1, 2018.[4] Venot, O., Hebrard, E., Agundez, M., Dobrijevic, M., Selsis, F., Hersant,F., Iro, N., and Bounaceur, R. A chemical model for the atmosphere of hot jupiters. A&A, 546:A43, 2012.[5] Molliere, P., Wardenier, J. P., van Boekel, R., Henning, Th., Molaverdikhani, K., and Snellen, I. A. G. petitRADTRANS - a python radiative transfer package for exoplanet characterization and retrieval.A&A, 627:A67, 2019.[6] M. Agundez, O. Venot, N. Iro, F. Selsis, F. Hersant, E. Hebrard, and M. Do-brijevic. The impact of atmospheric circulation on the chemistry of the hotJupiter HD 209458b.A&A, 548:A73, Dec 2012.

This work introduces an approach to enhance the computational efficiency of 3D Global Circulation Model (GCM) simulations by integrating a machine-learned surrogate model into the OASIS GCM[1]. 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 post-JWST instruments[2-3]. 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.This work builds upon previous efforts in both exoplanet science and Earth climate science to accelerate 3D atmospheric models. Prior work in exoplanet science primarily relies on extrapolating 1D models to approximate certain 3D atmospheric variations[4-6], which often introduces both known and unknown biases. Earth climate science, benefiting from a wealth of high-resolution observational measurements, has had a different set of methods employed, namely machine-learned surrogate models trained on such observations[7-8]. This work employs machine-learned surrogate model techniques, benchmarked in Earth climate science[8], to be used in general planetary climate models.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 and lowest resolution 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 10 times CPU speed-up compared to the original GCM.In conclusion, this work presents a method to accelerate 3D GCM simulations, offering a pathway to more efficient and detailed modelling of planetary atmospheres. References:[1] Mendonca J. & Buchhave L., ‘Modelling the 3D Climate of Venus with OASIS’, 2020, Volume 496, Pages 3512-3530, Monthly Notices of the Royal Astronomical Society[2] Gardner, J. P., “The James Webb Space Telescope”, Space Science Reviews, vol. 123, no. 4, pp. 485–606, 2006. doi:10.1007/s11214-006-8315-7.[3] Tinetti, G., “Ariel: Enabling planetary science across light-years”, arXiv e-prints, 2021. doi:10.48550/arXiv.2104.04824.[4] Q. Changeat and A. Al-Refaie, “TauREx3 PhaseCurve: A 1.5D Model for Phase-curve Description,” Astrophys J, vol. 898, no. 2, p. 155, Aug. 2020, doi: 10.3847/1538-4357/ab9b82.[5] K. L. Chubb and M. Min, “Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b,” Jun. 2022, doi: 10.1051/0004-6361/202142800.[6] P. G. J. Irwin et al., “2.5D retrieval of atmospheric properties from exoplanet phase curves: Application toWASP-43b observations,” Mon Not R Astron Soc, vol. 493, no. 1, pp. 106–125, Mar. 2020, doi: 10.1093/mnras/staa238.[7] Yao, Y., Zhong, X., Zheng, Y., & Wang, Z. 2023, Journal of Advances in Modeling Earth Systems, 15, e2022MS003445, doi: 10.1029/2022MS003445[8] Ukkonen, P. 2022, Journal of Advances in Modeling Earth Systems, 14, e2021MS002875, doi: 10.1029/2021MS002875

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.

The EXoplanet Climate Infrared TElescope (EXCITE) experiment is a balloon-borne, purpose-designed mission to measure spectroscopic phase curves of short-period extrasolar giant planets (EGPs, or “hot Jupiters”). Here, we present EXCITE’s principal science instrument: a high-throughput, single-object spectrograph operating in the 0.8-2.5 µm and 2.5-4.0 µm bands with R≥50. Our compact design achieves diffraction-limited, on-axis performance with just three powered optics: two off-axis parabolic mirrors and a CaF2 prism. We discuss the optical and mechanical design, the expected optical performance of the spectrograph, and summarize the tolerances needed to achieve that performance. We also discuss plans for establishing alignment of the optics and verifying the optical performance.

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.

High precision sub-arcsecond pointing stability has become a capability widely utilized in the balloon-borne community, in particular for high resolution optical systems. However, many of these applications are also pushing the state-of-the-art with regards to detector technology, many forms of which require some level of cryogenic cooling and active dissipative cooling systems to achieve target performance specifications. Built on the success of the Super-pressure Balloon-borne Imaging Telescope (SuperBIT) experiment, we present the results of improved technologies and design methodologies applied to the EXoplanet Infrared TElescope (EXCITE), which uses active cryogenic systems to achieve detector performance while requiring pointing stability at the 100 milliarcsecond level. Results from EXCITE's recent balloon-borne campaign are presented within the context of Super-pressure Balloon (SPB) and Long Duration Balloon (LDB) applications.

We would like to present 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 um) 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-127b, 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-79b and WASP-62b, 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-127b is of particular interest due to its low density, and a thorough atmospheric study would develop our understanding of planet formation and migration.

In recent years, the study of exoplanetary atmospheres has increasingly relied on detailed disequilibrium chemistry models to understand atmospheric composition and dynamics. These models are computationally intensive, often requiring significant time and resources. We introduce CHEXANET, a novel U-Net-based neural network architecture designed to efficiently simulate disequilibrium chemistry in exoplanetary atmospheres, with network design driven by data exploration.We have developed a machine learning framework that uses neural networks to predict the steady-state abundances of key chemical species in exoplanetary atmospheres. Our method involves training the neural networks on a comprehensive dataset generated from traditional disequilibrium chemistry models. Once trained, these networks can rapidly approximate the outcomes of the full models, substantially reducing computation time.Our method was validated across a range of exoplanetary atmospheres, ranging from warm Neptunes to hot-Jupiters and an expansive range of chemistries. Our results demonstrate that the neural network models can achieve high accuracy in reproducing the steady-state solutions of complex chemical systems. Specifically, it significantly enhances computational efficiency, reducing the prediction time for atmospheric disequilibrium states to just one second per atmosphere on a standard personal computer—over a hundred times faster than traditional methods. Our approach accelerates the simulation process and makes it feasible to conduct extensive parameter studies and real-time atmospheric analysis.A noteworthy part of the project is involving data-driven decisions in neural network design. We incorporated knowledge gained from preliminary data analysis, which included statistical analysis, principal component analysis, random forest analysis, and feature importance analysis. The examination stated above shows that involving initial parameters such as C/O ratio, temperature, metallicity, planet mass, and radius should significantly boost the predictive capabilities of simple U-net architecture. Figure 1 and 2 show the Mean Absolute Error of network prediction in correlation with initial parameters for two different networks, Model A, a simple U-net model, and Model E, a network incorporating initial parameters. A simple data-driven decision substantially reduced a bias observed in Model A. The future work will include a deeper description of data analysis and its influence on network behaviour to explain the Model's internal processes.Our contribution highlights the potential of artificial intelligence in enhancing the capabilities of planetary science, providing a robust tool for future research in the characterization of exoplanets. Figure 1: Pair plots illustrating the distribution of initial exoplanetary parameters: C/O ratio, Temperature (K), Metallicity, and Planet Mass (MJ), colour-coded by the Mean Absolute Error (MAE) for Model A. Notable is the cluster formation and a peak in MAE around a C/O ratio of 1.0 in the Temperature vs. C/O ratio plot, indicating a possible region of increased predictive difficulty. Moreover, the error for the higher C/O ratio decreases, as seen in the upper right corner of the plot. Temperature vs. Planet Mass plot reveals a non-linear pattern. Figure 2: Pair plots illustrating the distribution of initial exoplanetary parameters: C/O ratio, Temperature (K), Metallicity, and Planet Mass (MJ), colour-coded by the Mean Absolute Error (MAE) for Model E. Although the prediction performance has improved compared to Figure 1, it is still noticeable that there is a cluster formation and a peak in MAE around a C/O ratio of 1.0 in the Temperature vs. C/O ratio plot, which suggests a possible area of increased predictive difficulty.

The precise derivation of transit depths from stellar light curves is a key component in the construction of exoplanet transit spectra, and thereby for the characterization of exoplanet atmospheres. However, it is still deeply affected by various kinds of complex systematic errors and noises taking their source from host stars’ or instruments’ variability. On the other hand, as the volume of exoplanetary data is quickly increasing, a new way is being opened up for using machine learning as part of the data processing pipeline. By training a recurrent neural network to model the temporal dependencies in stellar light curves, our results on both real on simulated light curves highlight that it is possible to:Model accurately the compound of trends and periodic effects with few or no assumptions about the instrument, star, or planetary signals Improve the understanding of each instrument’s systematic behaviour Optimise a deep detrending model jointly with a transit fit Leverage the cross-light curves and cross-instruments information Such an approach therefore paves the way for a global, flexible and efficient noise-correction pipeline which will be of paramount importance to make the most of exoplanets observations and provide high precision spectra to subsequent atmospheric retrieval pipelines.

The H2020 Europlanet-2020 programme, which ended on Aug 31st, 2019, included an activity called VESPA (Virtual European Solar and Planetary Access), which focused on adapting Virtual Observatory (VO) techniques to handle Planetary Science data [1] [2]. The outcome of this activity is a contributive data distribution system where data services are located and maintained in research institutes, declared in a registry, and accessed by several clients based on a specific access protocol. During Europlanet-2020, 52 data services were installed, including the complete ESA Planetary Science Archive, and the outcome of several EU funded projects. Data are described using the EPN-TAP protocol, which parameters describe acquisition and observing conditions as well as data characteristics (physical quantity, data type, etc). A main search portal has been developed to optimize the user experience, which queries all services together. Compliance with VO standards ensures that existing tools can be used as well, either to access or visualize the data. In addition, a bridge linking the VO and Geographic Information Systems (GIS) has been installed to address formats and tools used to study planetary surfaces; several large data infrastructures were also installed or upgraded (SSHADE for lab spectroscopy, PVOL for amateurs images, AMDA for plasma-related data).In the framework of the starting Europlanet-2024 programme, the VESPA activity will complete this system even further: 30-50 new data services will be installed, focusing on derived data, and experimental data produced in other Work Packages of Europlanet-2024; connections between PDS4 and EPN-TAP dictionaries will make PDS metadata searchable from the VESPA portal and vice versa; Solar System data present in astronomical VO catalogues will be made accessible, e.g. from the VizieR database. The search system will be connected with more powerful display and analysing tools: a run-on-demand platform will be installed, as well as Machine Learning capacities to process the available content. Finally, long-term sustainability will be improved by setting VESPA hubs to assist data providers in maintaining their services, and by using the new EU-funded European Open Science Cloud (EOSC). In addition to favoring data exploitation, VESPA will provide a handy and economical solution to Open Science challenges in the field.The Europlanet 2020 & 2024 Research Infrastructure project have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 654208 & 871149.[1] Erard et al 2018, Planet. Space Sci. 150, 65-85. 10.1016/j.pss.2017.05.013. ArXiv 1705.09727 [2] Erard et al. 2020, Data Science Journal 19, 22. doi: 10.5334/dsj-2020-022.