4,761 research outputs found

    Exoplanet atmospheres with GIANO II. Detection of molecular absorption in the dayside spectrum of HD 102195b

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    The study of exoplanetary atmospheres is key to understand the differences between their physical, chemical and dynamical processes. Up to now, the bulk of atmospheric characterization analysis has been conducted on transiting planets. On some sufficiently bright targets, high-resolution spectroscopy (HRS) has also been successfully tested for non-transiting planets. We study the dayside of the non-transiting planet HD 102195b using the GIANO spectrograph mounted at TNG, demonstrating the feasibility of atmospheric characterization measurements and molecular detection for non-transiting planets with the HRS technique using 4-m class telescopes. The Doppler-shifted planetary signal changes on the order of many km/s during the observations, in contrast with the telluric absorption which is stationary in wavelength, allowing us to remove the contamination from telluric lines while preserving the features of the planetary spectrum. The emission signal from HD 102195b's atmosphere is then extracted by cross-correlating the residual spectra with atmospheric models. We detect molecular absorption from water vapor at 4.4σ\sigma level. We also find convincing evidence for the presence of methane, which is detected at the 4.1σ\sigma level. The two molecules are detected with a combined significance of 5.3σ\sigma, at a semi-amplitude of the planet radial velocity KP=128±6K_P=128\pm 6 km/s. We estimate a planet true mass of MP=0.46±0.03 MJM_P=0.46\pm 0.03~M_J and orbital inclination between 72.5 and 84.79∘^{\circ} (1σ\sigma). Our analysis indicates a non-inverted atmosphere for HD 102195b, as expected given the relatively low temperature of the planet, inefficient to keep TiO/VO in gas phase. Moreover, a comparison with theoretical expectations and chemical model predictions corroborates our methane detection and suggests that the detected CH4CH_4 and H2OH_2O signatures could be consistent with a low C/O ratio.Comment: 12 pages, 12 figures, accepted for publication in A&

    Possibility to realize spin-orbit-induced correlated physics in iridium fluorides

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    Recent theoretical predictions of "unprecedented proximity" of the electronic ground state of iridium fluorides to the SU(2) symmetric jeff=1/2j_{\mathrm{eff}}=1/2 limit, relevant for superconductivity in iridates, motivated us to investigate their crystal and electronic structure. To this aim, we performed high-resolution x-ray powder diffraction, Ir L3_3-edge resonant inelastic x-ray scattering, and quantum chemical calculations on Rb2_2[IrF6_6] and other iridium fluorides. Our results are consistent with the Mott insulating scenario predicted by Birol and Haule [Phys. Rev. Lett. 114, 096403 (2015)], but we observe a sizable deviation of the jeff=1/2j_{\mathrm{eff}}=1/2 state from the SU(2) symmetric limit. Interactions beyond the first coordination shell of iridium are negligible, hence the iridium fluorides do not show any magnetic ordering down to at least 20 K. A larger spin-orbit coupling in iridium fluorides compared to oxides is ascribed to a reduction of the degree of covalency, with consequences on the possibility to realize spin-orbit-induced strongly correlated physics in iridium fluorides

    Learning Probabilistic Termination Proofs

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    We present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. The result is thus a sound witness of probabilistic termination. Our learning strategy is agnostic to the source code and its verification counterpart supports the widest range of probabilistic single-loop programs that any existing tool can handle to date. We demonstrate the efficacy of our method over a range of benchmarks that include linear and polynomial programs with discrete, continuous, state-dependent, multi-variate, hierarchical distributions, and distributions with undefined moments

    Machine learning and multidrug-resistant gram-negative bacteria: An interesting combination for current and future research

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    The dissemination of multidrug-resistant Gram-negative bacteria (MDR-GNB) is associated with increased morbidity and mortality in several countries. Machine learning (ML) is a branch of artificial intelligence that consists of conferring on computers the ability to learn from data. In this narrative review, we discuss three existing examples of the application of ML algorithms for assessing three different types of risk: (i) the risk of developing a MDR-GNB infection, (ii) the risk of MDR-GNB etiology in patients with an already clinically evident infection, and (iii) the risk of anticipating the emergence of MDR in GNB through the misuse of antibiotics. In the next few years, we expect to witness an increasingly large number of research studies perfecting the application of ML techniques in the field of MDR-GNB infections. Very importantly, this cannot be separated from the availability of a continuously refined and updated ethical framework allowing an appropriate use of the large datasets of medical data needed to build efficient ML-based support systems that could be shared through appropriate standard infrastructures

    Exoplanet atmospheres with GIANO. I. Water in the transmission spectrum of HD 189733b

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    High-resolution spectroscopy (R ≥\ge 20,000) at near-infrared wavelengths can be used to investigate the composition, structure, and circulation patterns of exoplanet atmospheres. However, up to now it has been the exclusive dominion of the biggest telescope facilities on the ground, due to the large amount of photons necessary to measure a signal in high-dispersion spectra. Here we show that spectrographs with a novel design - in particular a large spectral range - can open exoplanet characterisation to smaller telescope facilities too. We aim to demonstrate the concept on a series of spectra of the exoplanet HD 189733 b taken at the Telescopio Nazionale Galileo with the near-infrared spectrograph GIANO during two transits of the planet. In contrast to absorption in the Earth's atmosphere (telluric absorption), the planet transmission spectrum shifts in radial velocity during transit due to the changing orbital motion of the planet. This allows us to remove the telluric spectrum while preserving the signal of the exoplanet. The latter is then extracted by cross-correlating the residual spectra with template models of the planet atmosphere computed through line-by-line radiative transfer calculations, and containing molecular absorption lines from water and methane. By combining the signal of many thousands of planet molecular lines, we confirm the presence of water vapour in the atmosphere of HD 189733 b at the 5.5-σ\sigma level. This signal was measured only in the first of the two observing nights. By injecting and retrieving artificial signals, we show that the non-detection on the second night is likely due to an inferior quality of the data. The measured strength of the planet transmission spectrum is fully consistent with past CRIRES observations at the VLT, excluding a strong variability in the depth of molecular absorption lines.Comment: 10 pages, 8 figures. Accepted for publication in Astronomy & Astrophysics. v2 includes language editin

    Mediterranean spreading of the bicolor purse oyster, Isognomon bicolor, and the chicken trigger, Malleus sp., vs. the Lessepsian prejudice

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    The introduction rate of alien species in the Mediterranean Sea is rapidly growing, and their taxonomical identification is increasingly challenging. This uncertain identification often leads to an incorrect estimation of the number of alien species, their route of introduction, and their potential negative effects. This is particularly true for some bivalves, which are characterized by high variation in their shells, resulting in uncertain morphological identification. This is the case for two alien bivalves, i.e., an Isognomonidae and a Malleidae species, both characterized by confused historical colonization records in the Mediterranean Sea, misidentifications, and controversial and changing nomenclatures that have insofar negatively affected our knowledge on their geographical distributions. In this respect, molecular approaches provide a strategy that is especially useful when traditional taxonomy fails, and DNA barcoding is a powerful and well-known tool to obtain reliable identifications through efficient molecular markers. In this work, we used the 16S rRNA marker to assess the preliminary identification of Isognomon sp. and Malleus sp. specimens from different localities in the Southern Mediterranean Sea. Bayesian inference (BI) and maximum likelihood (ML) methods were applied to test the monophyly of the phylogenetic linages and to clarify their taxonomic positions, allowing a complete overview of the colonization and spreading of these two alien bivalves in the Mediterranean Sea. In particular, the Isognomon sp. specimens were identified as the Atlantic I. bicolor, highlighting that previously suggested invasive migration patterns, (i.e., the Lessepsian migration), must be reconsidered with stronger critical attention in light of currently occurring global changes
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