arXiv subjects

Chloe Fisher

Publications and source records attributed to Chloe Fisher.

24 records · Page 2Linked to original sources

arXiv2021

HELIOS-K 2.0 Opacity Calculator and Open-source Opacity Database for Exoplanetary Atmospheres

Computing and using opacities is a key part of modeling and interpreting data of exoplanetary atmospheres. Since the underlying spectroscopic line lists are constantly expanding and currently include up to ~ 10^10 - 10^11 transition lines, the opacity calculator codes need to become more powerful. Here we present major upgrades to the HELIOS-K GPU-accelerated opacity calculator and describe the necessary steps to process large line lists within a reasonable amount of time. Besides performance improvements, we include more capabilities and present a toolbox for handling different atomic and molecular data sets: from downloading and pre-processing the data to performing the opacity calculations in a user-friendly way. HELIOS-K supports line lists from ExoMol, HITRAN, HITEMP, NIST, Kurucz and VALD3. By matching the resolution of 0.1 cm^-1 and cutting length of 25 cm^-1 used by the ExoCross code for timing performance (251 seconds excluding data read-in time), HELIOS-K can process the ExoMol BT2 water line list in 12.5 seconds. Using a resolution of 0.01 cm^-1, it takes 45 seconds - equivalent to about 10^7 lines per second. As a wavenumber resolution of 0.01 cm^-1 suffices for most exoplanetary atmosphere spectroscopic calculations, we adopt this resolution in calculating opacity functions for several hundred atomic and molecular species, and make them freely available on the open-access DACE database. For the opacity calculations of the database, we use a cutting length of 100 cm^-1 for molecules and no cutting length for atoms. Our opacities are available for downloading from https://dace.unige.ch/opacityDatabase and may be visualized using https://dace.unige.ch/opacity.

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arXiv2020

Information content of JWST-NIRSPEC transmission spectra of warm Neptunes

Warm Neptunes offer a rich opportunity for understanding exo-atmospheric chemistry. With the upcoming James Webb Space Telescope (JWST), there is a need to elucidate the balance between investments in telescope time versus scientific yield. We use the supervised machine learning method of the random forest to perform an information content analysis on a 11-parameter model of transmission spectra from the various NIRSpec modes. The three bluest medium-resolution NIRSpec modes (0.7 - 1.27 microns, 0.97 - 1.84 microns, 1.66 - 3.07 microns) are insensitive to the presence of CO. The reddest medium-resolution mode (2.87 - 5.10 microns) is sensitive to all of the molecules assumed in our model: CO, CO2, CH4, C2H2, H2O, HCN and NH3. It competes effectively with the three bluest modes on the information encoded on cloud abundance and particle size. It is also competitive with the low-resolution prism mode (0.6 - 5.3 microns) on the inference of every parameter except for the temperature and ammonia abundance. We recommend astronomers to use the reddest medium-resolution NIRSpec mode for studying the atmospheric chemistry of 800-1200 K warm Neptunes; its corresponding high-resolution counterpart offers diminishing returns. We compare our findings to previous JWST information content analyses that favor the blue orders, and suggest that the reliance on chemical equilibrium could lead to biased outcomes if this assumption does not apply. A simple, pressure-independent diagnostic for identifying chemical disequilibrium is proposed based on measuring the abundances of H2O, CO and CO2.

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arXiv2020

Interpreting High-Resolution Spectroscopy of Exoplanets Using Cross-Correlations and Supervised Machine Learning

We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine learning technique, to overcome challenges associated with high-resolution data. A series of cross-correlation functions are concatenated to give a "CCF-sequence" for each model atmosphere, which reduces the dimensionality by a factor of ~100. The random forest, trained on our grid of ~65,000 models, provides a likelihood-free method of retrieval. The pre-computed grid spans 31 values of both temperature and metallicity, and incorporates a realistic noise model. We apply our method to HARPS-N observations of the ultra-hot Jupiter KELT-9b, and obtain a metallicity consistent with solar (logM = $-0.2\pm0.2$). Our retrieved transit chord temperature (T = $6000^{+0}_{-200}$K) is unreliable as the ion cross-correlations lie outside of the training set, which we interpret as being indicative of missing physics in our atmospheric model. We compare our method to traditional nested-sampling, as well as other machine learning techniques, such as Bayesian neural networks. We demonstrate that the likelihood-free aspect of the random forest makes it more robust than nested-sampling to different error distributions, and that the Bayesian neural network we tested is unable to reproduce complex posteriors. We also address the claim in Cobb et al. (2019) that our random forest retrieval technique can be over-confident but incorrect. We show that this is an artefact of the training set, rather than the machine learning method, and that the posteriors agree with those obtained using nested-sampling.

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arXiv2019

How Much Information Does the Sodium Doublet Encode? Retrieval Analysis of Non-LTE Sodium Lines at Low and High Spectral Resolutions

Motivated by both ground- and space-based detections of the sodium doublet in the transmission spectra of exoplanetary atmospheres, we revisit the theory and interpretation of sodium lines in non-local thermodynamic equilibrium (NLTE), where collisions are not efficient enough to maintain a Boltzmann distribution for the excited and ground states of the sodium atom. We consider non-Boltzmann distributions that account for the ineffectiveness of collisions. We analyze the sodium doublet in transmission spectra measured at low (HAT-P-1b, HAT-P-12b, HD 189733b, WASP-6b, WASP-17b and WASP-39b) and high (WASP-49b) spectral resolutions. Nested-sampling retrievals performed on low-resolution optical/visible transmission spectra are unable to break the normalization degeneracy if the spectral continuum is associated with Rayleigh scattering by small cloud particles. Using mock retrievals, we demonstrate that un-normalized ground-based, high-resolution spectra centered on the sodium doublet alone are unable to precisely inform us about the pressure levels probed by the transit chord and hence to identify the region (i.e., thermosphere, exosphere) of the atmosphere being probed. Retrievals performed on the HARPS transmission spectrum of WASP-49b support this conclusion. Generally, we are unable to distinguish between LTE versus NLTE interpretations of the sodium doublet based on the computed Bayesian evidence with the implication that LTE interpretations tend to under-estimate the temperature probed by the transit chord. With the current low-resolution data, the sodium line shapes are consistent with Voigt profiles without the need for sub-Lorentzian wings. The retrieved sodium abundances are consistent with being sub-solar to solar.

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arXiv2018

Retrieval analysis of 38 WFC3 transmission spectra and resolution of the normalisation degeneracy

A comprehensive analysis of 38 previously published Wide Field Camera 3 (WFC3) transmission spectra is performed using a hierarchy of nested-sampling retrievals: with versus without clouds, grey versus non-grey clouds, isothermal versus non-isothermal transit chords and with water, hydrogen cyanide and/or ammonia. We revisit the "normalisation degeneracy": the relative abundances of molecules are degenerate at the order-of-magnitude level with the absolute normalisation of the transmission spectrum. Using a suite of mock retrievals, we demonstrate that the normalisation degeneracy may be partially broken using WFC3 data alone, even in the absence of optical/visible data and without appealing to the presence of patchy clouds, although lower limits to the mixing ratios may be prior-dominated depending on the measurement uncertainties. With James Webb Space Telescope-like spectral resolutions, the normalisation degeneracy may be completely broken from infrared spectra alone. We find no trend in the retrieved water abundances across nearly two orders of magnitude in exoplanet mass and a factor of 5 in retrieved temperature (about 500 to 2500 K). We further show that there is a general lack of strong Bayesian evidence to support interpretations of non-grey over grey clouds (only for WASP-69b and WASP-76b) and non-isothermal over isothermal atmospheres (no objects). 35 out of 38 WFC3 transmission spectra are well-fitted by an isothermal transit chord with grey clouds and water only, while 8 are adequately explained by flat lines. Generally, the cloud composition is unconstrained.

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arXiv2018

Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres

The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model. Known as atmospheric retrieval, it is a technique that originates from the Earth and planetary sciences. Such methods are very time-consuming and by necessity there is a compromise between physical and chemical realism versus computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods. Here, we report an adaptation of the random forest method of supervised machine learning, trained on a pre-computed grid of atmospheric models, which retrieves full posterior distributions of the abundances of molecules and the cloud opacity. The use of a pre-computed grid allows a large part of the computational burden to be shifted offline. We demonstrate our technique on a transmission spectrum of the hot gas-giant exoplanet WASP-12b using a five-parameter model (temperature, a constant cloud opacity and the volume mixing ratios or relative abundance by number of water, ammonia and hydrogen cyanide). We obtain results consistent with the standard nested-sampling retrieval method. Additionally, we can estimate the sensitivity of the measured spectrum to constraining the model parameters and we can quantify the information content of the spectrum. Our method can be straightforwardly applied using more sophisticated atmospheric models and also to interpreting an ensemble of spectra without having to retrain the random forest.

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