Atmospheric Retrieval Research Articles

How do wavelength correlations affect transmission spectra? Application of a new fast and flexible 2D Gaussian process framework to transiting exoplanet spectroscopy

The use of Gaussian processes (GPs) is a common approach to account for correlated noise in exoplanet time series, particularly for transmission and emission spectroscopy. This analysis has typically been performed for each wavelength channel separately, with the retrieved uncertainties in the transmission spectrum assumed to be independent. However, the presence of noise correlated in wavelength could cause these uncertainties to be correlated, which could significantly affect the results of atmospheric retrievals. We present a method that uses a GP to model noise correlated in both wavelength and time simultaneously for the full spectroscopic dataset. To make this analysis computationally tractable, we introduce a new fast and flexible GP method that can analyse 2D datasets when the input points lie on a (potentially non-uniform) 2D grid - in our case a time by wavelength grid - and the kernel function has a Kronecker product structure. This simultaneously fits all light curves and enables the retrieval of the full covariance matrix of the transmission spectrum. Our new method can avoid the use of a `common-mode' correction, which is known to produce an offset to the transmission spectrum. Through testing on synthetic datasets, we demonstrate that our new approach can reliably recover atmospheric features contaminated by noise correlated in time and wavelength. In contrast, fitting each spectroscopic light curve separately performed poorly when wavelength-correlated noise was present. It frequently underestimated the uncertainty of the scattering slope and overestimated the uncertainty in the strength of sharp absorption peaks in transmission spectra. Two archival VLT/FORS2 transit observations of WASP-31b were used to compare these approaches on real observations. Our method strongly constrained the presence of wavelength-correlated noise in both datasets, and significantly different constraints on atmospheric features such as the scattering slope and strength of sodium and potassium features were recovered.

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Comparison of tropical cyclone thermal structures derived from ATMS and synthetic AMSU-A/MHS

The warm core of a tropical cyclone (TC) reflects its intensity and evolution. In the past, microwave sounding data have been widely used for detecting the TC warm core. However, the observed magnitude of the TC warm core can vary with instruments. This study utilizes the oversampling data from the Advanced Technology Microwave Sounder (ATMS) to reconstruct the brightness temperature data consistent with the data from Advanced Microwave Sounding Unit-A (AMSU-A), which is referred to as AMSU-like data. Through comparisons, it is found that brightness temperature from the original ATMS has more detailed structures, depicting well the TC eye region and cloud bands. Both ATMS and AMSU-like data are used as inputs to the Global Scene-Dependent Atmospheric Retrieval Testbed (GSDART) to retrieve the warm cores of multiple TCs. At 250 hPa, the warm-core intensity retrieved using ATMS data is about 1–2 K higher than that from AMSU-like data and the warm-core structure from ATMS is more detailed. It is recommended that the warm-core intensity retrieved from AMSU-A be corrected to the ATMS values for multi-sensor monitoring of TCs using our newly defined relationship.摘要热带气旋(TC)的暖心反映了其强度和变化. 微波探测数据被广泛用于探测TC暖心, 但观测到的 TC 暖心强度可能因仪器而异. 本研究利用先进技术微波探测仪 (ATMS) 的过采样数据重采样至与先进微波探测仪 (AMSU-A) 一致的亮温, 称为类-AMSU数据. 通过对比发现, ATMS的观测更加细致, 较好地刻画了TC眼区和云带. 使用ATMS和类-AMSU数据反演多个TC的暖心发现, 在250 hPa, 使用ATMS数据反演的暖心强度比类-AMSU高约1–2K, 并且其暖心结构更详细.

Earth as an Exoplanet. III. Using Empirical Thermal Emission Spectra as an Input for Atmospheric Retrieval of an Earth-twin Exoplanet

In this study, we treat Earth as an exoplanet and investigate our home planet by means of a potential future mid-infrared space mission called the Large Interferometer For Exoplanets (LIFE). We combine thermal spectra from an empirical data set of disk-integrated Earth observations with a noise model for LIFE to create mock observations. We apply a state-of-the-art atmospheric retrieval framework to characterize the planet, assess the potential for detecting the known bioindicators, and investigate the impact of viewing geometry and seasonality on the characterization. Our key findings reveal that we are observing a temperate habitable planet with significant abundances of CO2, H2O, O3, and CH4. Seasonal variations in the surface and equilibrium temperature, as well as in the Bond albedo, are detectable. Furthermore, the viewing geometry and the spatially and temporally unresolved nature of our observations only have a minor impact on the characterization. Additionally, Earth’s variable abundance profiles and patchy cloud coverage can bias retrieval results for the atmospheric structure and trace-gas abundances. Lastly, the limited extent of Earth’s seasonal variations in biosignature abundances makes the direct detection of its biosphere through atmospheric seasonality unlikely. Our results suggest that LIFE could correctly identify Earth as a planet where life could thrive, with detectable levels of bioindicators, a temperate climate, and surface conditions allowing liquid surface water. Even if atmospheric seasonality is not easily observed, our study demonstrates that next generation space missions can assess whether nearby temperate terrestrial exoplanets are habitable or even inhabited.

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

A Combined Ground-based and JWST Atmospheric Retrieval Analysis: Both IGRINS and NIRSpec Agree that the Atmosphere of WASP-77A b Is Metal-poor

Ground-based high-resolution and space-based low-resolution spectroscopy are the two main avenues through which transiting exoplanet atmospheres are studied. Both methods provide unique strengths and shortcomings, and combining the two can be a powerful probe into an exoplanet’s atmosphere. Within a joint atmospheric retrieval framework, we combined JWST NIRSpec/G395H secondary eclipse spectra and Gemini South/IGRINS pre- and post-eclipse thermal emission observations of the hot Jupiter WASP-77A b. Our inferences from the IGRINS and NIRSpec data sets are consistent with each other, and combining the two allows us to measure the gas abundances of H2O and CO, as well as the vertical thermal structure, with higher precision than either data set provided individually. We confirm WASP-77A b’s subsolar metallicity ([(C+O)/H] = −0.61 −0.09+0.10) and solar C/O ratio (C/O = 0.57 −0.06+0.06) . The two types of data are complementary, and our abundance inferences are mostly driven by the IGRINS data, while inference of the thermal structure is driven by the NIRSpec data. Our ability to draw inferences from the post-eclipse IGRINS data is highly sensitive to the number of singular values removed in the detrending process, potentially due to high and variable humidity. We also search for signatures for atmospheric dynamics in the IGRINS data and find that propagated ephemeris error can manifest as either an orbital eccentricity or a strong equatorial jet. Neither are detected when using more up-to-date ephemerides. However, we find moderate evidence of thermal inhomogeneity and measure a cooler nightside that presents itself in the later phases after secondary eclipse.

Understanding the Roles of Aerosols and Clouds in Environment, Meteorology and Climate with Advanced Lidar Remote Sensing Techniques

This Special Issue lists nine publications, covering the topics of advanced atmospheric lidar techniques, lidar retrievals, and lidar applications. The investigations listed here are diverse, but they are all focused on atmospheric lidars. Some urgent issues, for instance low blind zone detection and polarization detection at a near-infrared wavelength band, were discussed and explored. The results are helpful for extending atmospheric lidar applications. In terms of lidar retrievals, a planetary boundary layer height retrieval and an automatic lidar retrieval for aerosol optical properties were investigated in some of the publications, which can strengthen the atmospheric lidar capabilities. For lidar applications, a detailed analysis of the evolution of stratospheric aerosol and dust–cloud interactions was presented. In this Editorial, the articles published within this Special Issue are reviewed to highlight their innovative contributions and main research findings.

Measuring Tracers of Planet Formation in the Atmosphere of WASP-77A b: Substellar O/H and C/H Ratios, with a Stellar C/O Ratio and a Potentially Superstellar Ti/H Ratio

We present a comprehensive atmospheric retrieval study of the hot Jupiter WASP-77A b using eclipse observations from the Hubble Space Telescope (HST) and JWST. Using atmospheric retrievals, the spectral features of H2O, CO, and TiO are identified, with volume mixing ratios estimated at log10(VMR) = −4.40 −0.11+0.14 , −4.44 −0.28+0.34 , and −6.40 −0.23+0.22 , respectively. We derive the atmospheric C-to-O ratio—a key planetary formation tracer—to be C/O = 0.54 ± 0.12, which is consistent with both the stellar host value and previous studies of the planet’s atmosphere, suggesting a relatively close-in formation. Computing other elemental ratios (i.e., C/H, O/H, and Ti/H), we conclude that the general enrichment of the atmosphere (i.e., metallicity) is substellar, depleted in C and O, but that Ti appears slightly superstellar. A low C and O content could be obtained, in combination with a stellar C/O ratio if the planet formed outside of the CO2 snow line before migrating inwards. Meanwhile, a superstellar Ti/H could be obtained by late contamination from refractory rich planetesimals. While broadly in agreement with previous works, we do find some differences and discuss these while also highlighting the need for homogeneous analyses when comparative exoplanetology is conducted.

Confirmation of TiO absorption and tentative detection of MgH and CrH in the atmosphere of HAT-P-41b

Understanding the role of optical absorbers is critical for linking the properties of the dayside and terminator atmospheres of hot Jupiters. This study aims to identify the signatures of optical absorbers in the atmosphere of the hot Jupiter HAT-P-41b. We conducted five transit observations of this planet to obtain its optical transmission spectra using the Gran Telescopio Canarias (GTC). We performed atmospheric retrievals assuming free abundances of 12 chemical species. Our Bayesian model comparisons revealed strong evidence for TiO absorption (∆ ln 𝒵 = 21.02), modest evidence for CrH (∆ ln 𝒵 = 3.73), and weak evidence for MgH (∆ ln 𝒵 = 2.32). When we combined the GTC transmission spectrum with previously published Hubble Space Telescope and Spitzer data, the retrieval results and model inferences remained consistent. In conclusion, HAT-P-41b has a metal-rich atmosphere with no high-altitude clouds or hazes. Further observations of its dayside atmosphere should be made to confirm the hints of a thermal inversion in the upper atmosphere suggested by our results.

Is the Atmosphere of the Ultra-hot Jupiter WASP-121 b Variable?

We present a comprehensive analysis of the Hubble Space Telescope observations of the atmosphere of WASP-121 b, an ultra-hot Jupiter. After reducing the transit, eclipse, and phase-curve observations with a uniform methodology and addressing the biases from instrument systematics, sophisticated atmospheric retrievals are used to extract robust constraints on the thermal structure, chemistry, and cloud properties of the atmosphere. Our analysis shows that the observations are consistent with a strong thermal inversion beginning at ∼104 Pa on the dayside, solar to subsolar metallicity Z (i.e., −0.77<log(Z)<0.05 ), and supersolar C/O ratio (i.e., 0.59 < C/O < 0.87). More importantly, utilizing the high signal-to-noise ratio and repeated observations of the planet, we identify the following unambiguous time-varying signals in the data: (i) a shift of the putative hotspot offset between the two phase curves and (ii) varying spectral signatures in the transits and eclipses. By simulating the global dynamics of WASP-121 b's atmosphere at high resolution, we show that the identified signals are consistent with quasiperiodic weather patterns, hence atmospheric variability, with signatures at the level probed by the observations (∼5% to ∼10%) that change on a timescale of ∼5 planet days; in the simulations, the weather patterns arise from the formation and movement of storms and fronts, causing hot (as well as cold) patches of atmosphere to deform, separate, and mix in time.

β Pictoris b through the eyes of the upgraded CRIRES+

Context. High-resolution spectrographs fed by adaptive optics (AO) provide a unique opportunity to characterize directly imaged exoplanets. Observations with such instruments allow us to probe the atmospheric composition, spin rotation, and radial velocity of the planet, thereby helping to reveal information on its formation and migration history. The recent upgrade of the Cryogenic High-Resolution Infrared Echelle Spectrograph (CRIRES+) at the VLT makes it a highly suitable instrument for characterizing directly imaged exoplanets. Aims. In this work, we report on observations of β Pictoris b with CRIRES+ and use them to constrain the planets atmospheric properties and update the estimation of its spin rotation. Methods. The data were reduced using the open-source pycrires package. We subsequently forward-modeled the stellar, planetary, and systematic contribution to the data to detect molecules in the planet’s atmosphere. We also used atmospheric retrievals to provide new constraints on its atmosphere. Results. We confidently detected water and carbon monoxide in the atmosphere of β Pictoris b and retrieved a slightly sub-solar carbon-to-oxygen ratio, which is in agreement with previous results. The interpretation is hampered by our limited knowledge of the C/O ratio of the host star. We also obtained a much improved constraint on its spin rotation of 19.9 ± 1.0 kms−1, which gives a rotation period of 8.7 ± 0.8 h, assuming no obliquity. We find that there is a degeneracy between the metallicity and clouds, but this has minimal impact on the retrieved C/O, υ sin i, and radial velocity. Our results show that CRIRES+ is performing well and stands as a highly useful instrument for characterizing directly imaged planets.

Validation of Elemental and Isotopic Abundances in Late-M Spectral Types with the Benchmark HIP 55507 AB System

M dwarfs are common host stars to exoplanets but often lack atmospheric abundance measurements. Late-M dwarfs are also good analogs to the youngest substellar companions, which share similar T eff ∼ 2300–2800 K. We present atmospheric analyses for the M7.5 companion HIP 55507 B and its K6V primary star with Keck/KPIC high-resolution (R ∼ 35,000) K-band spectroscopy. First, by including KPIC relative radial velocities between the primary and secondary in the orbit fit, we improve the dynamical mass precision by 60% and find MB=88.0−3.2+3.4MJup , putting HIP 55507 B above the stellar–substellar boundary. We also find that HIP 55507 B orbits its K6V primary star with a=38−3+4 au and e = 0.40 ± 0.04. From atmospheric retrievals of HIP 55507 B, we measure [C/H] = 0.24 ± 0.13, [O/H] = 0.15 ± 0.13, and C/O = 0.67 ± 0.04. Moreover, we strongly detect 13CO (7.8σ significance) and tentatively detect H218O (3.7σ significance) in the companion’s atmosphere and measure 12CO/13CO=98−22+28 and H216O/H218O=240−80+145 after accounting for systematic errors. From a simplified retrieval analysis of HIP 55507 A, we measure 12CO/13CO=79−16+21 and C16O/C18O=288−70+125 for the primary star. These results demonstrate that HIP 55507 A and B have consistent 12C/13C and 16O/18O to the <1σ level, as expected for a chemically homogeneous binary system. Given the similar flux ratios and separations between HIP 55507 AB and systems with young substellar companions, our results open the door to systematically measuring 13CO and H218O abundances in the atmospheres of substellar or even planetary-mass companions with similar spectral types.

Harnessing machine learning for accurate treatment of overlapping opacity species in general circulation models

To understand high precision observations of exoplanets and brown dwarfs, we need detailed and complex general circulation models (GCMs) that incorporate hydrodynamics, chemistry, and radiation. For this study, we specifically examined the coupling between chemistry and radiation in GCMs and compared different methods for the mixing of opacities of different chemical species in the correlated-k assumption, when equilibrium chemistry cannot be assumed. We propose a fast machine learning method based on DeepSets (DS), which effectively combines individual correlated-k opacities (k-tables). We evaluated the DS method alongside other published methods such as adaptive equivalent extinction (AEE) and random overlap with rebinning and resorting (RORR). We integrated these mixing methods into our GCM (expeRT/MITgcm) and assessed their accuracy and performance for the example of the hot Jupiter HD 209458 b. Our findings indicate that the DS method is both accurate and efficient for GCM usage, whereas RORR is too slow. Additionally, we observed that the accuracy of AEE depends on its specific implementation and may introduce numerical issues in achieving radiative transfer solution convergence. We then applied the DS mixing method in a simplified chemical disequilibrium situation, where we modeled the rainout of TiO and VO, and confirmed that the rainout of TiO and VO would hinder the formation of a stratosphere. To further expedite the development of consistent disequilibrium chemistry calculations in GCMs, we provide documentation and code for coupling the DS mixing method with correlated-k radiative transfer solvers. The DS method has been extensively tested to be accurate enough for GCMs; however, other methods might be needed for accelerating atmospheric retrievals.

A Cloud Detection Algorithm Based on FY-4A/GIIRS Infrared Hyperspectral Observations

Cloud detection is an essential preprocessing step when using satellite-borne infrared hyperspectral sounders for data assimilation and atmospheric retrieval. In this study, we propose a cloud detection algorithm based solely on the sensitivity and detection characteristics of the FY-4A Geostationary Interferometric Infrared Sounder (GIIRS), rather than relying on other instruments. The algorithm consists of four steps: (1) combining observed radiation and clear radiance data simulated by the Community Radiative Transfer Model (CRTM) to identify clear fields of view (FOVs); (2) determining the number of clouds within adjacent 2 × 2 FOVs via a principal component analysis of observed radiation; (3) identifying whether there are large observed radiance differences between adjacent 2 × 2 FOVs to determine the mixture of clear skies and clouds; and (4) assigning adjacent 2 × 2 FOVs as a cloud cluster following the three steps above to select an appropriate classification threshold. The classification results within each cloud detection cluster were divided into the following categories: clear, partly cloudy, or overcast. The proposed cloud detection algorithm was tested using one month of GIIRS observations from May 2022 in this study. The cloud detection and classification results were compared with the FY-4A Advanced Geostationary Radiation Imager (AGRI)’s operational cloud mask products to evaluate their performance. The results showed that the algorithm’s performance is significantly influenced by the surface type. Among all-day observations, the highest recognition performance was achieved over the ocean, followed by land surfaces, with the lowest performance observed over deep inland water. The proposed algorithm demonstrated better clear sky recognition during the nighttime for ocean and land surfaces, while its performance was higher for partly cloudy and overcast conditions during the day. However, for inland water surfaces, the algorithm consistently exhibited a lower cloud recognition performance during both the day and night. Moreover, in contrast to the GIIRS’s Level 2 cloud mask (CLM) product, the proposed algorithm was able to identify partly cloudy conditions. The algorithm’s classification results departed slightly from those of the AGRI’s cloud mask product in areas with clear sky/cloud boundaries and minimal convective cloud coverage; this was attributed to the misclassification of clear sky as partly cloudy under a low-resolution situation. AGRI’s CLM products, temporally and spatially collocated to the GIIRS FOV, served as the reference value. The proportion of FOVs consistently classified as partly cloudy to the total number of partly cloudy FOVs was 40.6%. In comparison with the GIIRS’s L2 product, the proposed algorithm improved the identification performance by around 10%.

Inferring chemical disequilibrium biosignatures for Proterozoic Earth-like exoplanets

AbstractChemical disequilibrium quantified using the available free energy has previously been proposed as a potential biosignature. However, researchers remotely sensing exoplanet biosignatures have not yet investigated how observational uncertainties impact the ability to infer a life-generated available free energy. We pair an atmospheric retrieval tool to a thermodynamics model to assess the detectability of chemical disequilibrium signatures of Earth-like exoplanets, focusing on the Proterozoic eon when the atmospheric abundances of oxygen–methane disequilibrium pairs may have been relatively high. Retrieval model studies applied across a range of gas abundances revealed that order-of-magnitude constraints on the disequilibrium energy are achieved with simulated reflected-light observations for the high-abundance scenario and high signal-to-noise ratios (50), whereas weak constraints are found for moderate signal-to-noise ratios (20–30) and medium- to low-abundance cases. Furthermore, the disequilibrium-energy constraints are improved by using the modest thermal information encoded in water vapour opacities at optical and near-infrared wavelengths. These results highlight how remotely detecting chemical disequilibrium biosignatures can be a useful and metabolism-agnostic approach to biosignature detection.

BOWIE-ALIGN: A JWST comparative survey of aligned versus misaligned hot Jupiters to test the dependence of atmospheric composition on migration history

ABSTRACT A primary objective of exoplanet atmosphere characterization is to learn about planet formation and evolution, however, this is challenged by degeneracies. To determine whether differences in atmospheric composition can be reliably traced to differences in evolution, we are undertaking a transmission spectroscopy survey with JWST to compare the compositions of a sample of hot Jupiters that have different orbital alignments around F stars above the Kraft break. Under the assumption that aligned planets migrate through the inner disc, while misaligned planets migrate after disc dispersal, the act of migrating through the inner disc should cause a measurable difference in the C/O between aligned and misaligned planets. We expect the amplitude and sign of this difference to depend on the amount of planetesimal accretion and whether silicates accreted from the inner disc release their oxygen. Here, we identify all known exoplanets that are suitable for testing this hypothesis, describe our JWST survey, and use noise simulations and atmospheric retrievals to estimate our survey’s sensitivity. With the selected sample of four aligned and four misaligned hot Jupiters, we will be sensitive to the predicted differences in C/O between aligned and misaligned hot Jupiters for a wide range of model scenarios.

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

To Sample or Not to Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalizing Flows

Current endeavours in exoplanet characterization rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of said technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation has become more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in machine learning provide optimization-based variational inference as an alternative approach to perform approximate Bayesian posterior inference. In this investigation we developed a normalizing-flow-based neural network, combined with our newly developed differentiable forward model, Diff-τ, to perform Bayesian inference in the context of atmospheric retrievals. Using examples from real and simulated spectroscopic data, we demonstrate the advantages of our proposed framework: (1) training our neural network does not require a large precomputed training set and can be trained with only a single observation; (2) it produces high-fidelity posterior distributions in excellent agreement with sampling-based retrievals; (3) it requires up to 75% fewer forward model calls to converge to the same result; and (4) this approach allows formal Bayesian model selection. We discuss the computational efficiencies of Diff-τ in relation to TauREx3's nominal forward model and provide a “lessons learned” account of developing radiative transfer models in differentiable languages. Our proposed framework contributes toward the latest development of neural network–powered atmospheric retrieval. Its flexibility and significant reduction in forward model calls required for convergence holds the potential to be an important addition to the retrieval tool box for large and complex data sets along with sampling-based approaches.

FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning

Context. Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Since these methods require many model computations, a compromise must be made between the model’s complexity and its run time. Achieving this compromise leads to a simplification of many physical and chemical processes (e.g. parameterised temperature structure). Aims. Here, we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm for atmospheric retrievals for exoplanets. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer. Methods. We generated 100 synthetic observations using ARtful Modeling Code for exoplanet Science (ARCiS), which is an atmospheric modelling code with the flexibility to compute models across varying degrees of complexity and to perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generated a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and ran a retrieval with self-consistent models to showcase the possibilities opened up by SNPE. Results. We find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50 000 forward model evaluations. We find that SNPE can speed up retrievals between ∼2× and ≥10× depending on the computational load of the forward model, the dimensionality of the observation, and its signal-to-noise ratio (S/N). We have made the code publicly available for the community on Github.

Atmospheric Material Boundary Layer Height Retrieval and Analysis in Qingdao Based on Doppler Lidar

Atmospheric Material Boundary Layer Height Retrieval and Analysis in Qingdao Based on Doppler Lidar