Modeling of Exoplanet Atmospheres

Spectrally characterizing exoplanet atmospheres will be one of the fastest moving astronomical disciplines in the years to come. Especially the upcoming James Webb Space Telescope (JWST) will provide spectral measurements from the near- to mid-infrared of unprecedented precision. With other next generation instruments on the horizon, it is crucial to possess the tools necessary for interpretating observations. To this end I wrote the petitCODE, which solves for the self-consistent atmospheric structures of exoplanets, assuming chemical and radiative-convective equilibrium. The code includes scattering, and models clouds. The code outputs the planet’s observable emission and transmission spectra. In addition, I constructed a spectral retrieval code, which derives the full posterior probability distribution of atmospheric parameters from observations. I used petitCODE to systematically study the atmospheres of hot jupiters and found, e.g., that their structures depend strongly on the type of their host stars. Moreover, I found that C/O ratios around unity can lead to atmospheric inversions. Next, I produced synthetic observations of prime exoplanet targets for JWST, and studied how well we will be able to distinguish various atmospheric scenarios. Finally, I verified the implementation of my retrieval code using mock JWST observations

Comparison of genome identities and amino acid identities between the predicted polyproteins of bat SaV and the selected SaV.

<p>Pro-Pol: Protease - Polymerase.</p

Unrooted maximum-likelihood tree based on full-length amino acid sequences of ORF1 precusor polyprotein.

<p>SH-like aLRT branch support values of greater than 0.70 are shown besides major branches. Scale bar indicates the number of inferred substitutions per site.</p

Genome organization of the bat SaV TLC58/HK.

<p>The genome organization of the bat SaV TLC58/HK in comparison with the genome organization of human SaV GI strain Mancheseter, human SaV GII strain Mc10, porcine enteric calicivirus, and norovirus GII strain MD145.</p

Scatterplot of codon usage.

<p>The scratterplot of codon usage summary statistics N_c and N_c′ against the proportion of G or C nucleotides at the 3^rd position of synonymous codons (GC_3s), showing greater codon usage bias in the bat SaV genome relative to other SaV genomes. Unlike the porcine enteric caliciviruses, the observed difference in codon usage bias persists with adjustment of background nucleotide composition (N_c′).</p

Species distribution of specimens and RT-PCR surveillance results in the present study.

<p>Species distribution of specimens and RT-PCR surveillance results in the present study.</p

Comparison of genomic features among selected SaV.

*<p>Only the formally recognized SaV genogroups are included.</p>†<p>Complete genome sequence is not available for canine SaV and California sea lion 1 SaV.</p><p>Sequence accession numbers are as follows: Bat SaV/TLC58/HK (JN899075), SaV/Manchester (X86560), SaV/Mc10 (NC_010624), porcine enteric calicivirus (PEC) (AF182760), SaV/Hu/Chiba/000671/1999/JP (AJ786349), SaV/Hu/Ehime475/2004/JP (DQ366344), canine SaV (JN387134), California sea lion 1 SaV (JN420370).</p

CpG dinucleotide bias in selected SaV genomes, as assessed by the odds ratio of CpG (ρ_CG) and other measures.

<p>Significantly less CpG suppression was found in the bat SaV genome, while a similar degree of negative GC skew was observed in all SaV genomes.</p

Unrooted maximum-likelihood trees of VP1 and VP2.

<p>The trees were constructed based on the full-length amino acid sequences of (a) VP1 major capsid protein, and (b) VP2 minor structural protein. SH-like aLRT branch support values of greater than 0.70 are shown besides major branches. Scale bar indicates the number of inferred substitutions per site.</p

Statistics of expression-pattern clusters and expression patterns.

<p>Statistics of expression-pattern clusters and expression patterns.</p