arXiv subjects

Emilie Panek

Publications and source records attributed to Emilie Panek.

8 recordsLinked to original sources

arXiv2026

ASTER -- Agentic Science Toolkit for Exoplanet Research

The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing atmospheric composition of transiting exoplanets. The analyses of these data require the combination of archival queries, literature search, the use of radiative transfer models, and Bayesian retrieval frameworks, each demanding specialized expertise. Modern large language models enable the coordinated execution of complex, multi-step tasks by AI agents with tool integration, structured prompts, and iterative reasoning. In this study we present ASTER, an Agentic Science Toolkit for Exoplanet Research. ASTER is an orchestration framework that brings LLM capability to the exoplanetary community by enabling LLM-driven interaction with integrated domain-specific tools, workflow planning and management, and support for common data analysis tasks. Currently ASTER incorporates tools for downloading planetary parameters and observational datasets from the NASA Exoplanet Archive, as well as the generation of transit spectra from the TauREx radiative transfer model, and the completion of Bayesian retrieval of planetary parameters with TauREx. Beyond tool integration, the agent assists users by proposing alternative modeling approaches, reporting potential issues and suggesting solutions, and interpretations. We demonstrate ASTER's workflow through a complete case study of WASP-39b, performing multiple retrievals using observational data available on the archive. The agent efficiently transitions between datasets, generates appropriate forward model spectra and performs retrievals. ASTER provides a unified platform for the characterization of exoplanet atmospheres. Ongoing development and community contributions will continue expanding ASTER's capabilities toward broader applications in exoplanet research.

astro-ph.EP↗

arXiv2026

Balancing Variety and Sample Size: Optimal Parameter Sampling for Ariel Target Selection

Targeted astrophysical surveys are limited by the amount of telescope time available, which makes it impossible to observe every single object of interest. In order to maximize the scientific return, we need a well thought strategy for selecting the observational targets, in our case exoplanets. This study evaluates various strategies for selecting exoplanet targets within limited observation windows, focusing specifically on the selection of exoplanet targets for Tier 2 transit spectroscopy with ESA's upcoming Ariel mission. We define three distinct selection criteria -- sample size, variance, and leverage -- and translate them into objective functions compatible with modern optimization algorithms. Specifically, we test five heuristics for maximizing sample leverage: leverage greedy, simulated annealing, K-means clustering, regular classes, and quantile classes. The performance of these methods is demonstrated through three practical exercises across one, two, and three parameters of diversity. Each criterion represents a unique trade-off between sample size, diversity, and total observation time. While a time-greedy approach maximizes the quantity of planets, it fails to capture diversity. Conversely, variance-greedy selection prioritizes diversity but introduces significant drawbacks: it oversamples rare cases and undersamples typical planets, ultimately reducing the total number of targets observed. Leverage-based selections emerge as the most effective middle ground, successfully balancing sample diversity with a robust sample size. This work supports the broader community effort to ensure that Ariel delivers the most diverse and scientifically valuable sample of exoplanet atmospheres within mission limits.

astro-ph.EP↗

arXiv2026

Hunting for "Oddballs" with Machine Learning: Detecting Anomalous Exoplanets Using a Deep-Learned Low-Dimensional Representation of Transit Spectra with Autoencoders

This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the Atmospheric Big Challenge (ABC) database, a publicly available dataset with over 100,000 simulated exoplanet spectra, to construct an anomaly detection scenario by defining CO2-rich atmospheres as anomalies and CO2-poor atmospheres as the normal class. We benchmarked four different anomaly detection strategies: Autoencoder Reconstruction Loss, One-Class Support Vector Machine (1 class-SVM), K-means Clustering, and Local Outlier Factor (LOF). Each method was evaluated in both the original spectral space and the autoencoder's latent space using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) metrics. To test the performance of the different methods under realistic conditions, we introduced Gaussian noise levels ranging from 10 to 50 ppm. Our results indicate that anomaly detection is consistently more effective when performed within the latent space across all noise levels. Specifically, K-means clustering in the latent space emerged as a stable and high-performing method. We demonstrate that this anomaly detection approach is robust to noise levels up to 30 ppm (consistent with realistic space-based observations) and remains viable even at 50 ppm when leveraging latent space representations. On the other hand, the performance of the anomaly detection methods applied directly in the raw spectral space degrades significantly with increasing the level of noise. This suggests that autoencoder-driven dimensionality reduction offers a robust methodology for flagging chemically anomalous targets in large-scale surveys where exhaustive retrievals are computationally prohibitive.

astro-ph.EP↗

arXiv2025

Re-analysis of 10 Hot-Jupiter Atmospheres with disequilibrium chemistry retrieval

Constraining the chemical structure of exoplanetary atmospheres is pivotal for interpreting spectroscopic data and understanding planetary evolution. Traditional retrieval methods often assume thermochemical equilibrium or free profiles, which may fail to capture disequilibrium processes like photodissociation and vertical mixing. This study leverages the TauREx 3.1 retrieval framework coupled with FRECKLL, a disequilibrium chemistry model, to address these challenges. The study aims to (1) assess the impact of disequilibrium chemistry on constraining metallicity and C/O ratios; (2) evaluate the role of refractory species (TiO and VO) in spectral retrievals; (3) explore consistency between transit and eclipse observations for temperature and chemical profiles; and (4) determine the effects of retrieval priors and data reduction methods. Ten hot-Jupiter atmospheres were reanalyzed using Hubble Space Telescope (HST) WFC3 data in eclipse and transit. The TauREx-FRECKLL model incorporated disequilibrium chemistry calculations with a Bayesian framework to infer atmospheric properties. The disequilibrium approach significantly altered retrieved metallicity and C/O ratios compared to equilibrium models, impacting planet formation insights. Retrievals reconciled transit and eclipse temperature profiles in deeper atmospheric layers but not in upper layers. Results were highly dependent on spectral resolution and retrieval priors, emphasizing limitations of HST data and the need for broader spectral coverage from instruments like JWST. This study demonstrates the feasibility and importance of incorporating disequilibrium chemistry in atmospheric retrievals, highlighting its potential for advancing our understanding of exoplanetary atmospheres with next-generation telescopes.

astro-ph.EP↗

arXiv2024

Breaking degeneracies in exoplanetary parameters through self-consistent atmosphere-interior modelling

Context: A new generation of instruments (e.g., JWST, ELTs, PLATO and Ariel) is providing atmospheric spectra and mass/radius measurements for large exoplanet populations, challenging planetary models used to interpret these findings. Aims: We develop a new model, the Heat Atmosphere Density Evolution Solver (HADES), by coupling an atmosphere and interior model self-consistently and comparing its results to observed data. Methods: Atmospheric calculations are performed under radiative-convective equilibrium, while the interior relies on recent ab initio equations of state. We ensure continuity in the thermal, gravity, and molecular mass profiles between models. Results: The model is applied to the known exoplanet database to characterize intrinsic thermal properties. We find that intrinsic temperatures (T$_{int}$) of 200-400 K, increasing with equilibrium temperature, are needed to explain radius inflation in hot Jupiters. Additionally, we perform atmosphere-interior retrievals using observed spectra and measured parameters for WASP-39 b and 51 Eridani b. For WASP-39 b, spectroscopic data breaks degeneracies in metallicity and Tint, deriving high values: Z = 14.79$^{+1.80}_{-1.91}$ x Solar and T$_{int} = 297.39^{+8.95}_{-16.9}$ K. For 51 Eridani b, we show the importance of using self-consistent models with radius as a constrained parameter, deriving a planet mass M$_{p} = 3.13^{+0.05}_{-0.04}$ M$_{J}$ and a core mass M$_{core} = 31.86^{+0.32}_{-0.18}$ M$_{E}$, suggesting formation via core accretion with a "hot start." Conclusions: Self-consistent atmosphere-interior models can efficiently break degeneracies in the structure of transiting and directly imaged exoplanets, offering new insights into exoplanet formation and evolution.

astro-ph.EP↗

arXiv2024

Data availability and requirements relevant for the Ariel space mission and other exoplanet atmosphere applications

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption and other continuum data, optical properties of aerosols and surfaces, atmospheric chemistry, UV photodissociation and photoabsorption cross-sections, and standards in the description and format of such data. These data aspects are discussed by addressing the following questions for each topic, based on the experience of the "data-provider" and "data-user" communities: (1) what are the types and sources of currently available data, (2) what work is currently in progress, and (3) what are the current and anticipated data needs. We present a GitHub platform for Ariel-related data, with the goal to provide a go-to place for both data-users and data-providers, for the users to make requests for their data needs and for the data-providers to link to their available data. Our aim throughout the paper is to provide practical information on existing sources of data whether in databases, theoretical, or literature sources.

astro-ph.IM↗

arXiv2024

ARES VI: Viability of one-dimensional retrieval models for transmission spectroscopy characterization of exo-atmospheres in the era of JWST and Ariel

Observed exoplanet transit spectra are usually retrieved using 1D models to determine atmospheric composition while planetary atmospheres are 3D. With the JWST and future space telescopes such as Ariel, we will be able to obtain increasingly accurate transit spectra. The 3D effects on the spectra will be visible, and we can expect biases in the 1D extractions. In order to elucidate these biases, we have built theoretical observations of transit spectra, from 3D atmospheric modeling through transit modeling to instrument modeling. 3D effects are observed to be strongly nonlinear from the coldest to the hottest planets. These effects also depend on the planet's metallicity and gravity. Considering equilibrium chemistry, 3D effects are observed through very strong variations in certain features of the molecule or very small variations over the whole spectrum. We conclude that we cannot rely on the uncertainty of retrievals at all pressures, and that we must be cautious about the results of retrievals at the top of the atmosphere. However the results are still fairly close to the truth at mid-altitudes (those probed). We also need to be careful with the chemical models used for planetary atmosphere. If the chemistry of one molecule is not correctly described, this will bias all the others, and the retrieved temperature as well. Finally, although fitting a wider wavelength range and higher resolution has been shown to increase retrieval accuracy, we show that this could depend on the wavelength range chosen, due to the accuracy on modeling the different features. In any case, 1D retrievals are still correct for the detection of molecules, even in the event of an erroneous abundance retrieval.

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arXiv2023

A re-analysis of equilibrium chemistry in five hot Jupiters

Studying chemistry and chemical composition is fundamental to go back to formation history of planetary systems. We propose here to have another look at five targets to better determine their composition and the chemical mechanisms that take place in their atmospheres. We present a re-analysis of five Hot Jupiters, combining multiple instruments and using Bayesian retrieval methods. We compare different combinations of molecules present in the simulated atmosphere, different chemistry types as well as different clouds parametrization. As a consequence of recent studies questioning the detection of Na and K in the atmosphere of HD 209458b as being potentially contaminated by stellar lines when present, we study the impact on other retrieval parameters of misinterpreting the presence of these alkali species. We use spatially scanned observations from the grisms G102 and G141 of the WFC3 on HST, with a wavelength coverage of $\sim$0.8 to $\sim$1.7 microns. We analyse these data with the publicly available Iraclis pipeline. We added to our datasets STIS observations to increase our wavelength coverage from $\sim$0.4 to $\sim$1.7 microns. We then performed a Bayesian retrieval analysis with the open-source TauREx using a nested sampling algorithm. We explore the influence of including Na and K on the retrieval of the molecules from the atmosphere. Our data re-analysis and Bayesian retrieval are consistent with previous studies but we find small differences in the retrieved parameters. After all, Na and K has no significant impact on the properties of the planet atmospheres. Therefore, we present here our new best-fit models, taking into account molecular abundances varying freely and equilibrium chemistry. This work is a preparation for a future addition of more sophisticated representation of chemistry taking into account disequilibrium effects such as vertical mixing and photochemistry.

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