Context. KELT-9 b exemplifies a newly emerging class of short-period gaseous exoplanets that tend to orbit hot, early type stars – termed ultra-hot Jupiters. The severe stellar irradiation heats their atmospheres to temperatures of ~4000 K, similar to temperatures of photospheres of dwarf stars. Due to the absence of aerosols and complex molecular chemistry at such temperatures, these planets offer the potential of detailed chemical characterization through transit and day-side spectroscopy. Detailed studies of their chemical inventories may provide crucial constraints on their formation process(es) and evolution history. Aims. We aim to search the optical transmission spectrum of KELT-9 b for absorption lines by metals using the cross-correlation technique. Methods. We analysed two transit observations obtained with the HARPS-N spectrograph. We used an isothermal equilibrium chemistry model to predict the transmission spectrum for each of the neutral and singly ionized atoms with atomic numbers between three and 78. Of these, we identified the elements that are expected to have spectral lines in the visible wavelength range and used those as cross-correlation templates. Results. We detect (>5σ) absorption by Na I, Cr II, Sc II and Y II, and confirm previous detections of Mg I, Fe I, Fe II, and Ti II. In addition, we find evidence of Ca I, Cr I, Co I, and Sr II that will require further observations to verify. The detected absorption lines are significantly deeper than predicted by our model, suggesting that the material is transported to higher altitudes where the density is enhanced compared to a hydrostatic profile, and that the material is part of an extended or outflowing envelope. There appears to be no significant blue-shift of the absorption spectrum due to a net day-to-night side wind. In particular, the strong Fe II feature is shifted by 0.18 ± 0.27 km s−1, consistent with zero. Using the orbital velocity of the planet we derive revised masses and radii of the star and the planet: M* = 1.978 ± 0.023 M⊙, R* = 2.178 ± 0.011 R⊙, mp = 2.44 ± 0.70 MJ and Rp = 1.783 ± 0.009 RJ.

Summary. We describe a sensitive, reliable and reproducible method, based on three multiplex PCR assays, for the rapid detection of seven common a-thalassaemia deletions and one a-globin gene triplication. The new assay detects the a 0 deletions ± ± SEA , ± (a) 20?5 , ± ± MED , ± ± FIL and ± ± THAI in the ®rst multiplex PCR, the second multiplex detects the ±a 3?7 deletion and aaa anti3?7 variant, the third multiplex detects the ±a 4?2 deletion. This simple multiplex method should greatly facilitate the genetic screening and molecular diagnosis of these determinants in populations where athalassaemias are prevalent.

High-resolution optical spectroscopy is a powerful tool to characterise exoplanetary atmospheres from the ground. The sodium D lines, with their large cross sections, are especially suited to studying the upper layers of atmospheres in this context. We report on the results from Hot Exoplanet Atmosphere Resolved with Transit Spectroscopy survey (HEARTS), a spectroscopic survey of exoplanet atmospheres, performing a comparative study of hot gas giants to determine the effects of stellar irradiation. In this second installation of the series, we highlight the detection of neutral sodium on the ultra-hot giant WASP-76b. We observed three transits of the planet using the High-Accuracy Radial-velocity Planet Searcher (HARPS) high-resolution spectrograph at the European Southern Observatory (ESO) 3.6 m telescope and collected 175 spectra of WASP-76. We repeatedly detect the absorption signature of neutral sodium in the planet atmosphere (0.371 ± 0.034%; 10.75σ in a 0.75 Å passband). The sodium lines have a Gaussian profile with full width at half maximum (FWHM) of 27.6 ± 2.8 km s−1. This is significantly broader than the line spread function of HARPS (2.7 km s−1). We surmise that the observed broadening could trace the super-rotation in the upper atmosphere of this ultra-hot gas giant.

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 500-2500 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. Tsiaras et al. (2018). In order to isolate the spectral feature due to water 1 , they quantified the strength of absorption between 1.3-1.65 µm, relative to the continuum, in terms of the number of pressure scale heights, which they represented by AH . Based on the finding that both AH and the equilibrium temperature (Teq) follow log-normal distributions, Fu et al. (2017) concluded that their sample of AH is affected by observational bias. Tsiaras et al. (2018) defined an Atmospheric Detectability Index (ADI) to quantify the strength of detection of the water feature, but do not explicitly link the ADI to any trends in cloud properties. They concluded that all of their WFC3 transmission spectra, except for WASP-69b, are consistent with the presence of a grey cloud deck.Our intention is to build upon the Fu et al. (2017) and Tsiaras et al. (2018) studies by subjecting their WFC3 sample to a detailed atmospheric retrieval study and elucidating the presence of assumptions, limitations and trends. It follows the principle that the same datasets should be analysed by different groups (using different codes and techniques) 1 Technically, it is due to a collection of unresolved water lines.

The use of machine learning is becoming ubiquitous in astronomy [1,2,3], 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 [4,5,6]. Known as "atmospheric retrieval", it is a technique that originates from the Earth and planetary sciences [7]. 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 [8]. Here, we report an adaptation of the "random forest" method of supervised machine learning [9,10], 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) [11]. 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.We use the previously analysed Hubble Space Telescope Wide Field Camera 3 (WFC3) transmission spectrum of the hot Jupiter WASP-12b, where the volume mixing ratio of water was inferred to be ∼ 10 −4 to ∼ 10 −2 and the temperature ∼ 1000 K [12]. Transmission spectra measure the wavelengthdependent obscuration of starlight by a transiting exoplanet, which encodes signatures of absorption by molecules and clouds in the exoplanetary atmosphere. The choice of this spectrum was to ensure continuity between previous studies [11,12] and because we expect WFC3 to be the workhorse for measuring exo-atmospheric spectra for the immediate future. We implement the random forest method [9,10], which is a supervised form of machine learning. It combines the use of a decision tree [13] and bootstrapping with replacement, and may be used on both discrete and continuous training sets. A decision tree is a way of splitting a training set into subsets based on common characteristics of its members [14]. The splitting is performed so as to maximize the gain in information entropy [14]. Since decision trees are sensitive to slight changes in the training set, they are suitable for use with the bootstrapping method, which constructs the decision tree by randomly drawing from the training set [14].The training se...