Observation of topologically protected helical edge modes in Kagome elastic plates

The investigation of topologically protected waves in classical media has opened unique opportunities to achieve exotic properties like one-way phonon transport, protection from backscattering and immunity to imperfections. Contrary to acoustic and electromagnetic domains, their observation in elastic solids has so far been elusive due to the presence of both shear and longitudinal modes and their modal conversion at interfaces and free surfaces. Here we report the experimental observation of topologically protected helical edge waves in elastic media. The considered structure consists of an elastic plate patterned according to a Kagome architecture with an accidental degeneracy of two Dirac cones induced by drilling through holes. The careful breaking of symmetries couples the corresponding elastic modes which effectively emulates spin orbital coupling in the quantum spin Hall effect. The results shed light on the topological properties of the proposed plate waveguide and opens avenues for the practical realization of compact, passive and cost-effective elastic topological waveguides

Piezoelectric Phononic Plates: Retrieving the Frequency Band Structure via All-electric Experiments

We propose an experimental technique based on all-electric measurements to retrieve the frequency response of a one-dimensional piezoelectric phononic crystal plate, structured periodically with millimeter-scaled metallic strips on its two surfaces. The metallic electrodes, used for the excitation of Lamb-like guided modes in the plate, ensure at the same time control of their dispersion by means of externally loaded electric circuits that offer non-destructive tunability in the frequency response of these structures. Our results, in very good agreement with finite-element numerical predictions, reveal interesting symmetry aspects that are employed to analyze the frequency band structure of such crystals. More importantly, Lamb-like guided modes interact with electric-resonant bands induced by inductance loads on the plate, whose form and symmetry are discussed and analyzed in depth, showing unprecedented dispersion characteristics.Comment: This is the version of the article before peer review or editing, as submitted by an author to Smart Materials and Structures. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-665X/ab4aa

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N2O emissions from arable soils at regional or continental scales

Interferometric imaging of the sulfur-bearing molecules H2S, SO and CS in comet C/1995 O1 (Hale-Bopp)

We present observations of rotational lines of H2S, SO and CS performed in comet C/1995 O1 (Hale-Bopp) in March 1997 with the Plateau de Bure interferometer (IRAM). The observations provide informations on the spatial and velocity distributions of these molecules. They can be used to constrain their photodissociation rate and their origin. We use a radiative transfer code which allows us to compute synthetic line profiles and interferometric maps, to be compared to the observations. Both single-dish spectra and interferometric spectral maps show a day/night asymmetry in the outgassing. From the analysis of the spectral maps, including the astrometry, we show that SO and CS present in addition a jet-like structure that may be the gaseous counterpart of the dust high-latitude jet observed in optical images. A CS rotating jet is also observed. Using the astrometry provided by continuum radio maps obtained in parallel, we conclude that there is no need to invoke of nongravitational forces acting on this comet, and provide an updated orbit. The radial extension of H2S is found to be consistent with direct release from the nucleus. SO displays an extended radial distribution. Assuming that SO2 is the parent of SO, the photodissociation rate of SO is measured to be 1.5 E-4 s-1 at 1 AU from the Sun. This is lower than most laboratory-based estimates and may suggest that SO is not solely produced by SO2 photolysis. From the observations of J(2-1) and J(5-4) CS lines, we deduce a CS photodissociation rate of 1 to 5 E-5 s-1. The photodissociation rate of CS2, the likely parent of CS, cannot be constrained due to insufficient resolution, but our data are consistent with published values. These observations illustrate the cometary science that will be performed with the future ALMA interferometer.Comment: Accepted for publication in Astronomy & Astrophysic

Hydrogen Isocyanide in Comet 73P/Schwassmann-Wachmann (Fragment B)

We present a sensitive 3-sigma upper limit of 1.1% for the HNC/HCN abundance ratio in comet 73P/Schwassmann-Wachmann (Fragment B), obtained on May 10-11, 2006 using Caltech Submillimeter Observatory (CSO). This limit is a factor of ~7 lower than the values measured previously in moderately active comets at 1 AU from the Sun. Comet 73P/Schwassmann-Wachmann was depleted in most volatile species, except of HCN. The low HNC/HCN ratio thus argues against HNC production from polymers produced from HCN. However, thermal degradation of macromolecules, or polymers, produced from ammonia and carbon compounds, such as acetylene, methane, or ethane appears a plausible explanation for the observed variations of the HNC/HCN ratio in moderately active comets, including the very low ratio in comet 73P/Schwassmann-Wachmann reported here. Similar polymers have been invoked previously to explain anomalous 14N/15N ratios measured in cometary CN.Comment: 6 pages, 5 figures, 2 table

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

Detection of CO and HCN in Pluto's atmosphere with ALMA

Observations of the Pluto-Charon system, acquired with the ALMA interferometer on June 12-13, 2015, have yielded a detection of the CO(3-2) and HCN(4-3) rotational transitions from Pluto, providing a strong confirmation of the presence of CO, and the first observation of HCN, in Pluto's atmosphere. The CO and HCN lines probe Pluto's atmosphere up to ~450 km and ~900 km altitude, respectively. The CO detection yields (i) a much improved determination of the CO mole fraction, as 515+/-40 ppm for a 12 ubar surface pressure (ii) clear evidence for a well-marked temperature decrease (i.e., mesosphere) above the 30-50 km stratopause and a best-determined temperature of 70+/-2 K at 300 km, in agreement with recent inferences from New Horizons / Alice solar occultation data. The HCN line shape implies a high abundance of this species in the upper atmosphere, with a mole fraction >1.5x10-5 above 450 km and a value of 4x10-5 near 800 km. The large HCN abundance and the cold upper atmosphere imply supersaturation of HCN to a degree (7-8 orders of magnitude) hitherto unseen in planetary atmospheres, probably due to the slow kinetics of condensation at the low pressure and temperature conditions of Pluto's upper atmosphere. HCN is also present in the bottom ~100 km of the atmosphere, with a 10-8 - 10-7 mole fraction; this implies either HCN saturation or undersaturation there, depending on the precise stratopause temperature. The HCN column is (1.6+/-0.4)x10^14 cm-2, suggesting a surface-referred net production rate of ~2x10^7 cm-2s-1. Although HCN rotational line cooling affects Pluto's atmosphere heat budget, the amounts determined in this study are insufficient to explain the well-marked mesosphere and upper atmosphere's ~70 K temperature. We finally report an upper limit on the HC3N column density (< 2x10^13 cm-2) and on the HC15N / HC14N ratio (< 1/125).Comment: Revised version. Icarus, in press, Oct. 11, 2016. 57 pages, including 13 figures and 4 table

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions