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

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

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

Identification of structural features of condensed tannins that affect protein aggregation

A diverse panel of condensed tannins was used to resolve the confounding effects of size and subunit composition seen previously in tannin-protein interactions. Turbidimetry revealed that size in terms of mean degree of polymerisation (mDP) or average molecular weight (amw) was the most important tannin parameter. The smallest tannin with the relatively largest effect on protein aggregation had an mDP of ~7. The average size was significantly correlated with aggregation of bovine serum albumin, BSA (mDP: r=-0.916; amw: r=-0.925; p<0.01; df=27), and gelatin (mDP: r=-0.961; amw: r=-0.981; p<0.01; df=12). The procyanidin/prodelphinidin and cis-/trans-flavan-3-ol ratios gave no significant correlations. Tryptophan fluorescence quenching indicated that procyanidins and cis-flavan-3-ol units contributed most to the tannin interactions on the BSA surface and in the hydrophobic binding pocket (r=0.677; p<0.05; df=9 and r=0.887; p<0.01; df=9, respectively). Circular dichroism revealed that higher proportions of prodelphinidins decreased the apparent α-helix content (r=-0.941; p<0.01; df=5) and increased the apparent β-sheet content (r=0.916; p<0.05; df=5) of BSA

Euclid preparation: XXVII. A UV-NIR spectral atlas of compact planetary nebulae for wavelength calibration

The Euclid mission will conduct an extragalactic survey over 15 000 deg2 of the extragalactic sky. The spectroscopic channel of the Near-Infrared Spectrometer and Photometer (NISP) has a resolution of R ~ 450 for its blue and red grisms that collectively cover the 0.93-1.89 μm range. NISP will obtain spectroscopic redshifts for 3 × 107 galaxies for the experiments on galaxy clustering, baryonic acoustic oscillations, and redshift space distortion. The wavelength calibration must be accurate within 5 Å to avoid systematics in the redshifts and downstream cosmological parameters. The NISP pre-flight dispersion laws for the grisms were obtained on the ground using a Fabry-Perot etalon. Launch vibrations, zero gravity conditions, and thermal stabilisation may alter these dispersion laws, requiring an in-flight recalibration. To this end, we use the emission lines in the spectra of compact planetary nebulae (PNe), which were selected from a PN database. To ensure completeness of the PN sample, we developed a novel technique to identify compact and strong line emitters in Gaia spectroscopic data using the Gaia spectra shape coefficients. We obtained VLT/X-shooter spectra from 0.3 to 2.5 μm for 19 PNe in excellent seeing conditions and a wide slit, mimicking Euclid's slitless spectroscopy mode but with a ten times higher spectral resolution. Additional observations of one northern PN were obtained in the 0.80-1.90 μm range with the GMOS and GNIRS instruments at the Gemini North Observatory. The collected spectra were combined into an atlas of heliocentric vacuum wavelengths with a joint statistical and systematic accuracy of 0.1 Å in the optical and 0.3 Å in the near-infrared. The wavelength atlas and the related 1D and 2D spectra are made publicly available

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

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

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 Euclid-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic (Ωm, σ 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 4.5 times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with Euclid. The data used in this analysis are publicly released with the paper

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

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 $Euclid$-like mocks, derived from N-body simulations. In this first paper of the HOWLS series, we computed the nontomographic ($\Omega_{\rm m}$, $\sigma_8$) 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 $4.5$ times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with $Euclid$. 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

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: XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for 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. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with ≳90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band

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

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 $\gtrsim 90\%$ 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 $\sim 0.87$ to $\sim 0.75$ 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 $\sim 0.89$ to $\sim 0.78$ for the different models