Atmospheric Retrieval of Exoplanets

Madhusudhan, Nikku

doi:10.1007/978-3-319-55333-7_104

Cited by 70 publications

(71 citation statements)

References 109 publications

(211 reference statements)

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“…Therefore, the most stringent constraints on P-T profiles in exoplanetary atmospheres have been obtained for dayside thermal emission spectra of transiting hot Jupiters. A detailed review of observational inferences of atmospheric P-T profiles in exoplanets and their theoretical implications have been discussed in several recent works (167,163,162). Here we focus on the latest developments in this area and future directions.…”

Section: Temperature Structuresmentioning

confidence: 95%

Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects

Madhusudhan

2019

Annu. Rev. Astron. Astrophys.

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327

227

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Exoplanetary science is on the verge of an unprecedented revolution. The thousands of exoplanets discovered over the past decade have most recently been supplemented by discoveries of potentially habitable planets around nearby low-mass stars. Currently, the field is rapidly progressing towards detailed spectroscopic observations to characterise the atmospheres of these planets. While various surveys from space and ground are expected to detect numerous more exoplanets orbiting nearby stars, the imminent launch of JWST along with large groundbased facilities are expected to revolutionise exoplanetary spectroscopy. Such observations, combined with detailed theoretical models and inverse methods, provide valuable insights into a wide range of physical processes and chemical compositions in exoplanetary atmospheres. Depending on the planetary properties, the knowledge of atmospheric compositions can also place important constraints on planetary formation and migration mechanisms, geophysical processes and, ultimately, biosignatures. In the present review, we will discuss the modern and future landscape of this frontier area of exoplanetary atmospheres. We will start with a brief review of the area, emphasising the key insights gained from different observational methods and theoretical studies. This is followed by an in-depth discussion of the state-of-the-art, challenges, and future prospects in three forefront branches of the area. 2 N. Madhusudhan 6 N. Madhusudhan 28 N. Madhusudhan 42 N. Madhusudhan

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Section: Temperature Structuresmentioning

confidence: 95%

Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects

Madhusudhan

2019

Annu. Rev. Astron. Astrophys.

Self Cite

327

227

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“…Given a measured spectrum, atmospheric retrieval solves the inverse problem of inferring the properties of the object (see Madhusudhan 2018 for a recent review). Traditionally, atmospheric retrieval is performed using a simple forward model that is computed on the fly and used in tandem with a Bayesian method such as nested sampling or Markov Chain Monte Carlo, e.g., Line et al (2015).…”

Section: Combining the Use Of Model Grids With Supervised Machine Leamentioning

confidence: 99%

Supervised Machine Learning for Intercomparison of Model Grids of Brown Dwarfs: Application to GJ 570D and the Epsilon Indi B Binary System

Oreshenko

Kitzmann

Márquez-Neila

et al. 2019

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Self-consistent model grids of brown dwarfs involve complex physics and chemistry, and are often computed using proprietary computer codes, making it challenging to identify the reasons for discrepancies between model and data as well as between the models produced by different research groups. In the current study, we demonstrate a novel method for analyzing brown dwarf spectra, which combines the use of the Sonora, AMES-cond and HELIOS model grids with the supervised machine learning method of the random forest. Besides performing atmospheric retrieval, the random forest enables information content analysis of the three model grids as a natural outcome of the method, both individually on each grid and by comparing the grids against one another, via computing large suites of mock retrievals. Our analysis reveals that the different choices made in modeling the alkali line shapes hinder the use of the alkali lines as gravity indicators. Nevertheless, the spectrum longward of 1.2 µm encodes enough information on the surface gravity to allow its inference from retrieval. Temperature may be accurately and precisely inferred independent of the choice of model grid, but not the surface gravity. We apply random forest retrieval to three objects: the benchmark T7.5 brown dwarf GJ 570D; and Indi Ba (T1.5 brown dwarf) and Bb (T6 brown dwarf), which are part of a binary system and have measured dynamical masses. For GJ 570D, the inferred effective temperature and surface gravity are consistent with previous studies. For Indi Ba and Bb, the inferred surface gravities are broadly consistent with the values informed by the dynamical masses.

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“…To plot and save the radiance spectrum (along with the wavenumber grid) in a file use the riPlot and riSave functions, respectively. To convolve the radiance spectrum I with a spectral response function according to (12), a special method convolve has been implemented, e.g., radBox1 = radiance.convolve() uses the default "box" with a half width 1.0 cm −1 . Likewise, radGauss2 = radiance.convolve(2.0,'G') uses a Gaussian response function with HWHM 2.0 cm −1 .…”

Section: Radiance/intensitymentioning

confidence: 99%

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

et al. 2019

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Radiation is a key process in the atmosphere. Numerous radiative transfer codes have been developed spanning a large range of wavelengths, complexities, speeds, and accuracies. In the infrared and microwave, line-by-line codes are crucial esp. for modeling and analyzing high-resolution spectroscopic observations. Here we present Py4CAtS—PYthon scripts for Computational ATmospheric Spectroscopy, a Python re-implemen-tation of the Fortran Generic Atmospheric Radiation Line-by-line Code GARLIC, where computationally-intensive code sections use the Numeric/Scientific Python modules for highly optimized array processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts (and corresponding functions) to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. Py4CAtS can be used in three ways: in the (Unix/Windows/Mac) console/terminal, inside the (I)Python interpreter, or Jupyter notebook. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented. In conclusion, Py4CAtS provides a versatile environment for “interactive” (and batch) line-by-line radiative transfer modeling.

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Atmospheric Retrieval of Exoplanets

Cited by 70 publications

References 109 publications

Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects

Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects

Supervised Machine Learning for Intercomparison of Model Grids of Brown Dwarfs: Application to GJ 570D and the Epsilon Indi B Binary System

Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

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