120 research outputs found

    Mechanical tests and definition of new indexes of grape berry firmness. Evolution of berry skin hardness during alcoholic fermentation

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    The mechanical strength or firmness of a fruit is considered an important parameter to characterise its state of ripeness or conservation, as well as other parameters such as sugar level or color. The mechanical hardness of grapes influences the integrity and sanitary quality of the harvest. In this study, the mechanical characteristics of grapevine berries were studied at harvest time in order to determine their rheological properties (firmness and hardness of the berry skin) during alcoholic fermentation. Special indexes were defined measuring the energy needed to crush the berries to 50 % of their initial diameter, and applied successively to two different varieties. The entire berry firmness and the skin hardness were both different. Mechanical indexes linked to grape firmness were defined. Using these indexes, a significant effect on the firmness behavior due to variety was recorded: the skin of 'Grenache Noir' was found firmer and harder than 'Carignan Noir'. Furthermore, during the alcoholic fermentation, no change in skin hardness was observed for both varieties, despite changes in the composition of the must. These results give new information on mechanical properties of berries and could be used as an aid in the winemaking process. Indeed, they would probably help the winemaker to better choose the type of fermentation and maceration adapted to his grapes according to the type of wine he wishes to produce

    Colloidal stability of tannins: astringency, wine tasting and beyond

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    Tannin-tannin and tannin-protein interactions in water-ethanol solvent mixtures are studied in the context of red wine tasting. While tannin self-aggregation is relevant for visual aspect of wine tasting (limpidity and related colloidal phenomena), tannin affinities for salivary proline-rich proteins is fundamental for a wide spectrum of organoleptic properties related to astringency. Tannin-tannin interactions are analyzed in water-ethanol wine-like solvents and the precipitation map is constructed for a typical grape tannin. The interaction between tannins and human salivary proline-rich proteins (PRP) are investigated in the framework of the shell model for micellization, known for describing tannin-induced aggregation of beta-casein. Tannin-assisted micellization and compaction of proteins observed by SAXS are described quantitatively and discussed in the case of astringency

    Euclid preparation TBD. The effect of baryons on the Halo Mass Function

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    The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being sub-dominant to dark matter and dark energy, the baryonic component in our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model to precisely quantify the impact of baryons on galaxy cluster virial halo masses, using the baryon fraction within a cluster as proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters calibrated using non-radiative cosmological hydrodynamical simulations and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable one percent relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters, starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against varying the numerical implementation of the sub-resolution physical processes included in the simulations. Our work substantiates previous claims about the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model, arising from baryonic corrections to cluster masses, are sub-dominant when compared to the precision with which mass-observable relations will be calibrated using Euclid, as well as our current understanding of the baryon fraction within galaxy clusters.Comment: 18 pages, 10 figures, 4 tables, 1 appendix, abstract abridged for arXiv submissio

    Euclid Preparation. XXVIII. Forecasts for ten different higher-order weak lensing statistics

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    Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of ten different HOS on a common set of EuclidEuclid-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic (Ωm\Omega_{\rm m}, σ8\sigma_8) Fisher information for the one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and we compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around two in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a 4.54.5 times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with EuclidEuclid. The data used in this analysis are publicly released with the paper.Comment: 33 pages, 24 figures, main results in Fig. 19 & Table 5, version published in A&

    Euclid preparation: XV. Forecasting cosmological constraints for the Euclid and CMB joint analysis

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    The combination and cross-correlation of the upcoming Euclid data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of Euclid and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on Euclid-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a Euclid-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard Λ-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of Euclid probes with CMB data

    Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

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    Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with 90%\gtrsim 90\% precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of 0.87\sim 0.87 to 0.75\sim 0.75 depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from 0.89\sim 0.89 to 0.78\sim 0.78 for the different models

    Euclid preparation. XXIX. Water ice in spacecraft part I: The physics of ice formation and contamination

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    Molecular contamination is a well-known problem in space flight. Water is the most common contaminant and alters numerous properties of a cryogenic optical system. Too much ice means that Euclid's calibration requirements and science goals cannot be met. Euclid must then be thermally decontaminated, a long and risky process. We need to understand how iced optics affect the data and when a decontamination is required. This is essential to build adequate calibration and survey plans, yet a comprehensive analysis in the context of an astrophysical space survey has not been done before. In this paper we look at other spacecraft with well-documented outgassing records, and we review the formation of thin ice films. A mix of amorphous and crystalline ices is expected for Euclid. Their surface topography depends on the competing energetic needs of the substrate-water and the water-water interfaces, and is hard to predict with current theories. We illustrate that with scanning-tunnelling and atomic-force microscope images. Industrial tools exist to estimate contamination, and we must understand their uncertainties. We find considerable knowledge errors on the diffusion and sublimation coefficients, limiting the accuracy of these tools. We developed a water transport model to compute contamination rates in Euclid, and find general agreement with industry estimates. Tests of the Euclid flight hardware in space simulators did not pick up contamination signals; our in-flight calibrations observations will be much more sensitive. We must understand the link between the amount of ice on the optics and its effect on Euclid's data. Little research is available about this link, possibly because other spacecraft can decontaminate easily, quenching the need for a deeper understanding. In our second paper we quantify the various effects of iced optics on spectrophotometric data.Comment: 35 pages, 22 figures, A&A in press. Changes to previous version: language edits, added Z. Bolag as author in the arxiv PDF (was listed in the ASCII author list and in the journal PDF, but not in the arxiv PDF). This version is identical to the journal versio

    Euclid preparation. XXXI. Performance assessment of the NISP Red-Grism through spectroscopic simulations for the Wide and Deep surveys

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    This work focuses on the pilot run of a simulation campaign aimed at investigating the spectroscopic capabilities of the Euclid Near-Infrared Spectrometer and Photometer (NISP), in terms of continuum and emission line detection in the context of galaxy evolutionary studies. To this purpose we constructed, emulated, and analysed the spectra of 4992 star-forming galaxies at 0.3z2.50.3 \leq z \leq 2.5 using the NISP pixel-level simulator. We built the spectral library starting from public multi-wavelength galaxy catalogues, with value-added information on spectral energy distribution (SED) fitting results, and from Bruzual and Charlot (2003) stellar population templates. Rest-frame optical and near-IR nebular emission lines were included using empirical and theoretical relations. We inferred the 3.5σ\sigma NISP red grism spectroscopic detection limit of the continuum measured in the HH band for star-forming galaxies with a median disk half-light radius of \ang{;;0.4} at magnitude H=19.5±0.2H= 19.5\pm0.2\,AB\,mag for the Euclid Wide Survey and at H=20.8±0.6H = 20.8\pm0.6\,AB\,mag for the Euclid Deep Survey. We found a very good agreement with the red grism emission line detection limit requirement for the Wide and Deep surveys. We characterised the effect of the galaxy shape on the detection capability of the red grism and highlighted the degradation of the quality of the extracted spectra as the disk size increases. In particular, we found that the extracted emission line signal to noise ratio (SNR) drops by \sim\,45%\% when the disk size ranges from \ang{;;0.25} to \ang{;;1}. These trends lead to a correlation between the emission line SNR and the stellar mass of the galaxy and we demonstrate the effect in a stacking analysis unveiling emission lines otherwise too faint to detect.Comment: 23 pages, 21 figure

    Euclid preparation. TBD. Forecast impact of super-sample covariance on 3x2pt analysis with Euclid

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    Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest of these non-Gaussian contributions, with the potential to significantly degrade constraints on some of the parameters of the cosmological model under study -- especially for weak lensing cosmic shear. We compute and validate the impact of SSC on the forecast uncertainties on the cosmological parameters for the Euclid photometric survey, obtained with a Fisher matrix analysis, both considering the Gaussian covariance alone and adding the SSC term -- computed through the public code PySSC. The photometric probes are considered in isolation and combined in the `3×\times2pt' analysis. We find the SSC impact to be non-negligible -- halving the Figure of Merit of the dark energy parameters (w0w_0, waw_a) in the 3×\times2pt case and substantially increasing the uncertainties on Ωm,0,w0\Omega_{{\rm m},0}, w_0, and σ8\sigma_8 for cosmic shear; photometric galaxy clustering, on the other hand, is less affected due to the lower probe response. The relative impact of SSC does not show significant changes under variations of the redshift binning scheme, while it is smaller for weak lensing when marginalising over the multiplicative shear bias nuisance parameters, which also leads to poorer constraints on the cosmological parameters. Finally, we explore how the use of prior information on the shear and galaxy bias changes the SSC impact. Improving shear bias priors does not have a significant impact, while galaxy bias must be calibrated to sub-percent level to increase the Figure of Merit by the large amount needed to achieve the value when SSC is not included.Comment: 22 pages, 13 figure
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