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Daniel Angerhausen

Publications and source records attributed to Daniel Angerhausen.

At least 19 recordsLinked to original sources

A Machine Learning Based Search for Lunar Anomalies

The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale. Here, we aim to test the abilities of the Beta-Variational Autoencoder (VAE) created by Lesnikowski et al. (2024), an unsupervised learning model which identifies anomalous features across the Moon's surface, locating not only scientifically useful geologic formations such as rockfall deposits, fresh impact craters, irregular mare patches, or volcanic pits/collapsed lava tubes, but also artificial objects such as landed spacecraft. This investigation further gauged the model's ability to locate anomalous surface features, successfully recovering two places of interest (Plaskett Crater and Paracelsus C Crater) and numerous landed technological assets at a statistically significant rate.

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The Dyson Minds 2025 Workshop: SETI around Black Holes

The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds" -- large-scale post-biological intelligences powered by energy harvested from supermassive black holes (SMBHs). Building on the ideas of F. J. Dyson (1960, 1966) and I. J. Good (1966), participants explored the physical, engineering, behavioral, and observational consequences of civilizations embodied as machinery operating near the universe's most powerful energy sources. The workshop aimed to develop new observational strategies capable of detecting signatures of such systems. Despite the highly cross-disciplinary scope, discussions centered on how a Dyson Mind might be constructed, how it might behave, and how those factors would shape strategies for the search for extraterrestrial intelligence. Key themes included the thermodynamic, mechanical, and stability limits of Dyson swarms; the trade-offs between power availability and communication latency in distributed minds; and how observability changes depending on whether Dyson Minds act as coherent entities or as loosely coordinated collectives. Across these topics, the consensus was that details of architecture and behavior strongly influence observational signatures. A major recommendation was to apply anomaly-detection methods to archival datasets, including those from WISE, JWST, and the Event Horizon Telescope, to identify unusual sources potentially overlooked by standard reduction pipelines. By integrating insights from multiple disciplines, the meeting advanced concrete, observation-focused strategies for future technosignature searches around SMBHs.

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Probing thermal gradients of habitable-zone rocky planets as an anti-indicator of a global surface ocean using mid-infrared direct imaging

Future direct-imaging missions such as the Large Interferometer for Exoplanets (LIFE) aim to observe thermal emission from potentially habitable planets to characterize their surface environments and search for signs of life. Previous studies of directly imaged Earth-like planets have mainly examined the signatures of atmospheric composition, often using 1D models, while the effect of horizontal temperature gradients has received limited attention. Because a pronounced horizontal temperature gradient may signal the absence of a global ocean, we investigate its detectability through thermal-emission direct imaging. Adopting Teegarden's Star b (zero-albedo equilibrium temperature ~280 K) as a benchmark, we compute 3D atmospheric structures with and without a global ocean using the ROCKE-3D general circulation model and simulate geometry-dependent thermal emission spectra. We show that the temperature gradients that disfavor a global-ocean scenario manifest in both orbital phase variation and spectral shape of the snapshot spectra. The phase variation is more readily detectable: one-day integrations with LIFE at two orbital phases would reveal flux variations in no-ocean cases with 1-10 bar atmospheres, depending on background atmospheric composition. Shapshot spectra provide complementary diagnostics, including the running brightness temperature of the continuum and detailed absorption band shapes, but detecting these features requires longer integration times. These 3D effects, if neglected, can bias interpretations based on 1D models, and could also lead to misidentification of the H$_2$O band beyond 10 $\mu$m. We also assess the detectability of these 3D effects on other nearby planets. Our results highlight the need to incorporate 3D atmospheric structures when characterizing rocky exoplanets, both to constrain surface conditions and to avoid misinterpretation of spectral data.

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Observing spatial and temporal variations in the atmospheric chemistry of rocky exoplanets: prospects for mid-infrared spectroscopy

Future telescopes such as the Large Interferometer For Exoplanets (LIFE) will enable mid-infrared characterisation of the atmospheres of nearby rocky exoplanets. Whilst 4D spatial and temporal variations of Earth as an exoplanet are below spectroscopic detection limits, such variability is planet-specific. We investigate LIFE's ability to detect 4D variability in the atmospheres of tidally locked exoplanets. We create daily synthetic LIFE observations of Proxima Centauri b in a 1:1 and an eccentric 3:2 spin-orbit resonance (SOR), using LIFEsim on spectra from daily 3D climate-chemistry model output of an aquaplanet with Earth-like composition. Hemispheric distributions of temperature, clouds, and chemical species determine spectral signatures and variability with orbital phase angle. Such variability dictates the extent to which parameters can be reliably inferred from snapshot spectra at arbitrary viewing geometries. In the 1:1 SOR, MIR spectra vary significantly with viewing geometry and indirectly probe atmospheric circulation. Nightside temperature inversions generate O3, CO2, and H2O emission features, though these lie below LIFE's detection threshold, and instead O3 features disappear at certain phase angles. In contrast, the 3:2 SOR yields a more homogeneous atmosphere with weaker phase variability but enhanced bolometric flux due to eccentric heating. Phase-resolved LIFE observations confidently distinguish between the SORs and capture seasonal O3 variability for golden targets like Proxima Centauri b. In case of abiotic O2/O3 build-up, the O3 variability presents a potential false positive scenario. Hence, LIFE can disentangle different spin-orbit states and resolve 4D atmospheric variability, enabling daily characterisation of the 4D physical and chemical state of nearby terrestrial worlds. Importantly, this characterisation requires phase-resolved rather than snapshot spectra.

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Detecting Atmospheric CO2 Trends as Population-Level Signatures for Long-Term Stable Water Oceans and Biotic Activity on Temperate Terrestrial Exoplanets

Identifying key observables is essential for enhancing our knowledge of exoplanet habitability and biospheres, as well as improving future mission capabilities. While currently challenging, future observatories such as the Large Interferometer for Exoplanets (LIFE) will enable atmospheric observations of a diverse sample of temperate terrestrial worlds. Using thermal emission spectra that represent conventional predictions of atmospheric CO2 variability across the Habitable Zone (HZ), we assess the ability of the LIFE mission - as a specific concept for a future space-based interferometer - to detect CO2 trends indicative of the carbonate-silicate (Cb-Si) weathering feedback, a well-known habitability marker and potential biological tracer. Therefore, we explore the feasibility of differentiating between CO2 trends in biotic and abiotic planet populations. We create synthetic exoplanet populations based on geochemistry-climate predictions and perform retrievals on simulated thermal emission observations. The results demonstrate the robust detection of population-level CO2 trends in both biotic and abiotic scenarios for population sizes as small as 30 Exo-Earth Candidates (EECs) and the lowest assessed spectrum quality in terms of signal-to-noise ratio, S/N = 10, and spectral resolution, R = 50. However, biased CO2 partial pressure constraints hinder accurate differentiation between biotic and abiotic trends. If these biases were corrected, accurate differentiation could be achieved for populations with $\geq$ 100 EECs. We conclude that LIFE can effectively enable population-level characterization of temperate terrestrial atmospheres and detect Cb-Si cycle driven CO2 trends as habitability indicators. Nevertheless, the identified biases underscore the importance of testing atmospheric characterization performance against the broad diversity expected for planetary populations.

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What if we find nothing? Bayesian analysis of the statistical information of null results in future exoplanet habitability and biosignature surveys

Future telescopes will survey temperate, terrestrial exoplanets to estimate the frequency of habitable ($\eta_{\text{Hab}}$) or inhabited ($\eta_{\text{Life}}$) planets. This study aims to determine the minimum number of planets ($N$) required to draw statistically significant conclusions, particularly in the case of a null result (i.e., no detections). Using a Bayesian framework, we analyzed surveys of up to $N=100$ planets to infer the frequency of a binary observable feature ($\eta_{\text{obs}}$) after null results. Posterior best fits and upper limits were derived for various survey sizes and compared with predicted yields from missions like the Large Interferometer for Exoplanets (LIFE) and the Habitable Worlds Observatory (HWO). Our findings indicate that $N=20-50$ ``perfect'' observations (100\% confidence in detecting or excluding the feature) yield conclusions relatively independent of priors. To achieve 99.9\% upper limits of $\eta_{\text{obs}} \leq 0.2/0.1$, approximately $N \simeq 40/80$ observations are needed. For ``imperfect'' observations, uncertainties in interpretation and sample biases become limiting factors. We show that LIFE and HWO aim for sufficiently large survey sizes to provide statistically meaningful estimates of habitable environments and life prevalence under these assumptions. However, robust conclusions require careful sample selection and high-confidence detection or exclusion of features in each observation.

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Photobombing for the Large Interferometer For Exoplanets (LIFE). A new criterion for target confusion and application to a MIR rotating nulling interferometer

One of the primary objectives in modern astronomy is to discover and study planets with characteristics similar to Earth. This pursuit involves analyzing the spectra of exoplanets and searching for biosignatures. Contamination of spectra by nearby objects (e.g., other planets and moons in the same system) is a significant concern and must be addressed for future exo-Earth searching missions. The aim is to estimate, for habitable planets, the probability of spectral contamination by other planets within the same star system. This investigation focuses on the Large Interferometer for Exoplanets (LIFE). Since the Rayleigh criterion is inapplicable to interferometers such as those proposed for LIFE, we present new criteria based on the principle of parsimony, which take into account two types of issues: contamination or blending of point sources, and cancellation of point sources due to destructive interference. We define a new spatial resolution metric associated with contamination or cancellation that generalizes to a broader family of observing instruments. In the current baseline design, LIFE is an X-array architecture nulling interferometer. Our investigation reveals that its transmission map introduces the potential for two point sources to appear as one, even if they do not appear in close proximity. We find that LIFE has a spatial resolution comparable to that of a traditional telescope with a diameter of $D = 600\,\text{m}$, observing at $\lambda = 4 \,\mu\text{m}$. Our survey of a star system population shows that, out of 73.4 expected habitable planets detected, 71.3 are not contaminated on average.

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Examining the Potential for Methyl Halide Accumulation and Detectability in Possible Hycean-Type Atmospheres

Some sub-Neptune planets may host habitable conditions; for example "Hycean" worlds with H2 envelopes over liquid water oceans can maintain potentially hospitable pressures and temperatures at their surface. Recent JWST observations of K2-18b and TOI-270d have shown that such worlds could be compelling targets for biosignature searches, given their extended scale heights and therefore large atmospheric signatures. Methylated biosignatures, a broad group of gases that can be generated by biological attachment of a CH3 group to an environmental substrate, have been proposed as candidate signs of life for Earth-like exoplanets. However, methyl halides (CH3 + halogen) have not yet been robustly examined with self-consistent photochemical and spectral models for planets with H2-dominated atmospheres. Here we demonstrate that methyl chloride (CH3Cl), predominantly produced by marine microbes, could be detected using JWST in tens of transits or fewer for Hycean planets, comparable to detection requirements for other potential atmospheric biosignatures. The threshold atmospheric mixing ratio for detectability is $\sim$10 ppm, which can accumulate with global fluxes comparable to moderately productive local environments on Earth.

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Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels

Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to atmospheric retrieval (e.g., nested sampling) are computationally expensive, a growing number of machine learning (ML) methods such as neural posterior estimation (NPE) have been proposed. We seek to make ML-based atmospheric retrieval (1) more reliable and accurate with verified results, and (2) more flexible with respect to the underlying neural networks and the choice of the assumed noise models. First, we adopt flow matching posterior estimation (FMPE) as a new ML approach to atmospheric retrieval. FMPE maintains many advantages of NPE, but provides greater architectural flexibility and scalability. Second, we use importance sampling (IS) to verify and correct ML results, and to compute an estimate of the Bayesian evidence. Third, we condition our ML models on the assumed noise level of a spectrum (i.e., error bars), thus making them adaptable to different noise models. Both our noise level-conditional FMPE and NPE models perform on par with nested sampling across a range of noise levels when tested on simulated data. FMPE trains about 3 times faster than NPE and yields higher IS efficiencies. IS successfully corrects inaccurate ML results, identifies model failures via low efficiencies, and provides accurate estimates of the Bayesian evidence. FMPE is a powerful alternative to NPE for fast, amortized, and parallelizable atmospheric retrieval. IS can verify results, thus helping to build confidence in ML-based approaches, while also facilitating model comparison via the evidence ratio. Noise level conditioning allows design studies for future instruments to be scaled up, for example, in terms of the range of signal-to-noise ratios.

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Artificial Greenhouse Gases as Exoplanet Technosignatures

Atmospheric pollutants such as CFCs and NO$_{2}$ have been proposed as potential remotely detectable atmospheric technosignature gases. Here we investigate the potential for artificial greenhouse gases including CF$_{4}$, C$_{2}$F$_{6}$, C$_{3}$F$_{8}$, SF$_{6}$, and NF$_{3}$ to generate detectable atmospheric signatures. In contrast to passive incidental byproducts of industrial processes, artificial greenhouse gases would represent an intentional effort to change the climate of a planet with long-lived, low toxicity gases and would possess low false positive potential. An extraterrestrial civilization may be motivated to undertake such an effort to arrest a predicted snowball state on their home world or to terraform an otherwise uninhabitable terrestrial planet within their system. Because artificial greenhouse gases strongly absorb in the thermal mid-infrared window of temperate atmospheres, a terraformed planet will logically possess strong absorption features from these gases at mid-IR wavelengths ($\sim$8-12 $\mu$m), possibly accompanied by diagnostic features in the near-IR. As a proof of concept, we calculate the needed observation time to detect 1 [10](100) ppm of C$_{2}$F$_{6}$/C$_{3}$F$_{8}$/SF$_{6}$ on TRAPPIST-1f with JWST MIRI/LRS and NIRSpec. We find that a combination of 1[10](100) ppm each of C$_{2}$F$_{6}$, C$_{3}$F$_{8}$, and SF$_{6}$ can be detected with an S/N $\geq$ 5 in as few as 25[10](5) transits with MIRI/LRS. We further explore mid-infrared direct-imaging scenarios with the LIFE mission concept and find these gases are more detectable than standard biosignatures at these concentrations. Consequently, artificial greenhouse gases can be readily detected (or excluded) during normal planetary characterization observations with no additional overhead.

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Automated Discovery of Anomalous Features in Ultra-Large Planetary Remote Sensing Datasets using Variational Autoencoders

The NASA Lunar Reconnaissance Orbiter (LRO) has returned petabytes of lunar high spatial resolution surface imagery over the past decade, impractical for humans to fully review manually. Here we develop an automated method using a deep generative visual model that rapidly retrieves scientifically interesting examples of LRO surface imagery representing the first planetary image anomaly detector. We give quantitative experimental evidence that our method preferentially retrieves anomalous samples such as notable geological features and known human landing and spacecraft crash sites. Our method addresses a major capability gap in planetary science and presents a novel way to unlock insights hidden in ever-increasing remote sensing data archives, with numerous applications to other science domains. We publish our code and data along with this paper.

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Large Interferometer For Exoplanets (LIFE): XII. The Detectability of Capstone Biosignatures in the Mid-Infrared -- Sniffing Exoplanetary Laughing Gas and Methylated Halogens

This study aims to identify exemplary science cases for observing N$_2$O, CH$_3$Cl, and CH$_3$Br in exoplanet atmospheres at abundances consistent with biogenic production using a space-based mid-infrared nulling interferometric observatory, such as the LIFE (Large Interferometer For Exoplanets) mission concept. We use a set of scenarios derived from chemical kinetics models that simulate the atmospheric response of varied levels of biogenic production of N$_2$O, CH$_3$Cl and CH$_3$Br in O$_2$-rich terrestrial planet atmospheres to produce forward models for our LIFEsim observation simulator software. In addition we demonstrate the connection to retrievals for selected cases. We use the results to derive observation times needed for the detection of these scenarios and apply them to define science requirements for the mission. Our analysis shows that in order to detect relevant abundances with a mission like LIFE in it's current baseline setup, we require: (i) only a few days of observation time for certain very near-by "Golden Target" scenarios, which also motivate future studies of "spectral-temporal" observations (ii) $\sim$10 days in certain standard scenarios such as temperate, terrestrial planets around M star hosts at 5 pc, (iii) $\sim$50 - 100 days in the most challenging but still feasible cases, such as an Earth twin at 5pc. A few cases for very low fluxes around specific host stars are not detectable. In summary, abundances of these capstone biosignatures are detectable at plausible biological production fluxes for most cases examined and for a significant number of potential targets.

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Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling

Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computationally expensive, thus sparking an interest in solutions based on machine learning (ML). In this ongoing work, we first explore flow matching posterior estimation (FMPE) as a new ML-based method for AR and find that, in our case, it is more accurate than neural posterior estimation (NPE), but less accurate than nested sampling. We then combine both FMPE and NPE with importance sampling, in which case both methods outperform nested sampling in terms of accuracy and simulation efficiency. Going forward, our analysis suggests that simulation-based inference with likelihood-based importance sampling provides a framework for accurate and efficient AR that may become a valuable tool not only for the analysis of observational data from existing telescopes, but also for the development of new missions and instruments.

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Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks

Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps $P$ to $T$. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.

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PyATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres

Cloud computing offers an opportunity to run compute-resource intensive climate models at scale by parallelising model runs such that datasets useful to the exoplanet community can be produced efficiently. To better understand the statistical distributions and properties of potentially habitable planetary atmospheres we implemented a parallelised climate modelling tool to scan a range of hypothetical atmospheres.Starting with a modern day Earth atmosphere, we iteratively and incrementally simulated a range of atmospheres to infer the landscape of the multi-parameter space, such as the abundances of biological mediated gases (\ce{O2}, \ce{CO2}, \ce{H2O}, \ce{CH4}, \ce{H2}, and \ce{N2}) that would yield `steady state' planetary atmospheres on Earth-like planets around solar-type stars. Our current datasets comprises of \numatmospheres simulated models of exoplanet atmospheres and is available publicly on the NASA Exoplanet Archive. Our scalable approach of analysing atmospheres could also help interpret future observations of planetary atmospheres by providing estimates of atmospheric gas fluxes and temperatures as a function of altitude. Such data could enable high-throughput first-order assessment of the potential habitability of exoplanetary surfaces and sepcan be a learning dataset for machine learning applications in the atmospheric and exoplanet science domain.

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Large Interferometer For Exoplanets (LIFE). X. Detectability of currently known exoplanets and synergies with future IR/O/UV reflected-starlight imaging missions

The next generation of space-based observatories will characterize the atmospheres of low-mass, temperate exoplanets with the direct-imaging technique. This will be a major step forward in our understanding of exoplanet diversity and the prevalence of potentially habitable conditions beyond the Earth. We compute a list of currently known exoplanets detectable with the mid-infrared Large Interferometer For Exoplanets (LIFE) in thermal emission. We also compute the list of known exoplanets accessible to a notional design of the Habitable Worlds Observatory (HWO), observing in reflected starlight. With a pre-existing method, we processed the NASA Exoplanet Archive and computed orbital realizations for each known exoplanet. We derived their mass, radius, equilibrium temperature, and planet-star angular separation. We used the LIFEsim simulator to compute the integration time ($t_{int}$) required to detect each planet with LIFE. A planet is considered detectable if a broadband signal-to-noise ratio $S/N$=7 is achieved over the spectral range $4-18.5μ$m in $t_{int}\leq$100 hours. We tested whether the planet is accessible to HWO in reflected starlight based on its notional inner and outer working angles, and minimum planet-to-star contrast. LIFE's reference configuration (four 2-m telescopes with 5% throughput and a nulling baseline between 10-100 m) can detect 212 known planets within 20 pc. Of these, 55 are also accessible to HWO in reflected starlight, offering a unique opportunity for synergies in atmospheric characterization. LIFE can also detect 32 known transiting exoplanets. Furthermore, 38 LIFE-detectable planets orbit in the habitable zone, of which 13 with $M_p<5M_\oplus$ and 8 with $5M_\oplus<M_p<10M_\oplus$. LIFE already has enough targets to perform ground-breaking analyses of low-mass, habitable-zone exoplanets, a fraction of which will also be accessible to other instruments.

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Imaging of exocomets with infrared interferometry

Active comets have been detected in several exoplanetary systems, although so far only indirectly, when the dust or gas in the extended coma has transited in front of the stellar disk. The large optical surface and relatively high temperature of an active cometary coma also makes it suitable to study with direct imaging, but the angular separation is generally too small to be reachable with present-day facilities. However, future imaging facilities with the ability to detect terrestrial planets in the habitable zones of nearby systems will also be sensitive to exocomets in such systems. Here we examine several aspects of exocomet imaging, particularly in the context of the Large Interferometer for Exoplanets (LIFE), which is a proposed space mission for infrared imaging and spectroscopy through nulling interferometry. We study what capabilities LIFE would have for acquiring imaging and spectroscopy of exocomets, based on simulations of the LIFE performance as well as statistical properties of exocomets that have recently been deduced from transit surveys. We find that for systems with extreme cometary activities such as beta Pictoris, sufficiently bright comets may be so abundant that they overcrowd the LIFE inner field of view. More nearby and moderately active systems such as epsilon Eridani or Fomalhaut may turn out to be optimal targets. If the exocomets have strong silicate emission features, such as in comet Hale-Bopp, it may become possible to study the mineralogy of individual exocometary bodies. We also discuss the possibility of exocomets as false positives for planets, with recent deep imaging of alpha Centauri as one hypothetical example. Such contaminants could be common, primarily among young debris disk stars, but should be rare among the main sequence population. We discuss strategies to mitigate the risk of any such false positives.

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Atmospheric retrievals for LIFE and other future space missions: the importance of mitigating systematic effects

Atmospheric retrieval studies are essential to determine the science requirements for future generation missions, such as the Large Interferometer for Exoplanets (LIFE). The use of heterogeneous absorption cross-sections might be the cause of systematic effects in retrievals, which could bias a correct characterization of the atmosphere. In this contribution we quantified the impact of differences in line list provenance, broadening coefficients, and line wing cut-offs in the retrieval of an Earth twin exoplanet orbiting a Sun-like star at 10 pc from the observer, as it would be observed with LIFE. We ran four different retrievals on the same input spectrum, by varying the opacity tables that the Bayesian retrieval framework was allowed to use. We found that the systematics introduced by the opacity tables could bias the correct estimation of the atmospheric pressure at the surface level, as well as an accurate retrieval of the abundance of some species in the atmosphere (such as CO$_2$ and N$_2$O). We argue that differences in the line wing cut-off might be the major source of errors. We highlight the need for more laboratory and modeling efforts, as well as inter-model comparisons of the main radiative transfer models and Bayesian retrieval frameworks. This is especially relevant in the context of LIFE and future generation missions, to identify issues and critical points for the community to jointly work together to prepare for the analysis of the upcoming observations.

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