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Future data provided by the Euclid mission will allow us to better understand the cosmic history of the Universe. A metric of its performance is the figure-of-merit (FoM) of dark energy, usually estimated with Fisher forecasts. The expected FoM has previously been estimated taking into account the two main probes of Euclid, namely the three-dimensional clustering of the spectroscopic galaxy sample, and the so-called 3×2pt signal from the photometric sample (i.e., the weak lensing signal, the galaxy clustering, and their cross-correlation). So far, these two probes have been treated as independent. In this paper, we introduce a new observable given by the ratio of the (angular) two-point correlation function of galaxies from the two surveys. For identical (normalised) selection functions, this observable is unaffected by sampling noise, and its variance is solely controlled by Poisson noise. We present forecasts for Euclid where this multi-tracer method is applied and is particularly relevant because the two surveys will cover the same area of the sky. This method allows for the exploitation of the combination of the spectroscopic and photometric samples. When the correlation between this new observable and the other probes is not taken into account, a significant gain is obtained in the FoM, as well as in the constraints on other cosmological parameters. The benefit is more pronounced for a commonly investigated modified gravity model, namely the γ parametrisation of the growth factor. However, the correlation between the different probes is found to be significant and hence the actual gain is uncertain. We present various strategies for circumventing this issue and still extract useful information from the new observable. [ABSTRACT FROM AUTHOR]

Context. ALMA observations show that dusty, distant, massive (M* ≳ 1011 M⊙) galaxies usually have a remarkable star-formation activity, contributing of the order of 25% of the cosmic star-formation rate density at z ≈ 3–5, and up to 30% at z ∼ 7. Nonetheless, they are elusive in classical optical surveys, and current near-IR surveys are able to detect them only in very small sky areas. Since these objects have low space densities, deep and wide surveys are necessary to obtain statistically relevant results about them. Euclid will potentially be capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if Euclid will be able to identify and characterise these objects. Aims. The goal of this work is to assess the capability of Euclid, together with ancillary optical and near-IR data, to identify these distant, dusty, and massive galaxies based on broadband photometry. Methods. We used a gradient-boosting algorithm to predict both the redshift and spectral type of objects at high z. To perform such an analysis, we made use of simulated photometric observations that mimic the Euclid Deep Survey, derived using the state-of-the-art Spectro-Photometric Realizations of Infrared-selected Targets at all-z (SPRITZ) software. Results. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the simulated Euclid Deep Survey catalogue at z > 2, while drastically decreasing the runtime with respect to spectral-energy-distribution-fitting methods. In particular, we studied the analogue of HIEROs (i.e. sources selected on the basis of a red H − [4.5]> 2.25), combining Euclid and Spitzer data at the depth of the Deep Fields. These sources include the bulk of obscured and massive galaxies in a broad redshift range, 3 < z < 7. We find that the dusty population at 3 ≲ z ≲ 7 is well identified, with a redshift root mean squared error and catastrophic outlier fraction of only 0.55 and 8.5% (HE ≤ 26), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the impact of massive and dusty galaxies on the cosmic star-formation rate over time. [ABSTRACT FROM AUTHOR]

In this paper we investigate the impact of lensing magnification on the analysis of Euclid's spectroscopic survey using the multipoles of the two-point correlation function for galaxy clustering. We determine the impact of lensing magnification on cosmological constraints as well as the expected shift in the best-fit parameters if magnification is ignored. We considered two cosmological analyses: (i) a full-shape analysis based on the Λ cold dark matter (CDM) model and its extension w0waCDM and (ii) a model-independent analysis that measures the growth rate of structure in each redshift bin. We adopted two complementary approaches in our forecast: the Fisher matrix formalism and the Markov chain Monte Carlo method. The fiducial values of the local count slope (or magnification bias), which regulates the amplitude of the lensing magnification, have been estimated from the Euclid Flagship simulations. We used linear perturbation theory and modelled the two-point correlation function with the public code coffe. For a ΛCDM model, we find that the estimation of cosmological parameters is biased at the level of 0.4–0.7 standard deviations, while for a w0waCDM dynamical dark energy model, lensing magnification has a somewhat smaller impact, with shifts below 0.5 standard deviations. For a model-independent analysis aimed at measuring the growth rate of structure, we find that the estimation of the growth rate is biased by up to 1.2 standard deviations in the highest redshift bin. As a result, lensing magnification cannot be neglected in the spectroscopic survey, especially if we want to determine the growth factor, one of the most promising ways to test general relativity with Euclid. We also find that, by including lensing magnification with a simple template, this shift can be almost entirely eliminated with minimal computational overhead. [ABSTRACT FROM AUTHOR]

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 subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are 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 1% 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 variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding 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 subdominant when compared to the precision with which mass–observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters. [ABSTRACT FROM AUTHOR]

This work considers which higher order modeling effects on the cosmic shear angular power spectra must be taken into account for Euclid. We identified the relevant terms and quantified their individual and cumulative impact on the cosmological parameter inferences from Euclid. We computed the values of these higher order effects using analytic expressions and calculated the impact on cosmological parameter estimations using the Fisher matrix formalism. We reviewed 24 effects and determined the ones that potentially need to be accounted for, namely: the reduced shear approximation, magnification bias, source-lens clustering, source obscuration, local Universe effects, and the flat Universe assumption. After computing these effects explicitly and calculating their cosmological parameter biases, using a maximum multipole of ℓ = 5000, we find that the magnification bias, source-lens clustering, source obscuration, and local Universe terms individually produce significant (> 0.25σ) cosmological biases in one or more parameters; accordingly, these effects must be accounted for and warrant further investigation. In total, we find biases in Ωm, Ωb, h, and σ8 of 0.73σ, 0.28σ, 0.25σ, and −0.79σ, respectively, for the flat ΛCDM. For the w0waCDM case, we found biases in Ωm, Ωb, h, ns, σ8, and wa of 1.49σ, 0.35σ, −1.36σ, 1.31σ, −0.84σ, and −0.35σ, respectively. These are increased relative to the ΛCDM due to additional degeneracies as a function of redshift and scale. [ABSTRACT FROM AUTHOR]

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and consequently a substantial computational effort, seeking methods to speed the calibration up is valuable. We study the efficiency of different noise cancellation methods that aim at reducing the simulation volume required to reach a given precision in the shear measurement. The more efficient a method is, the faster we can estimate the relevant biases up to a required precision level. Explicitly, we compared fit methods with different noise cancellations and a method based on responses. We used GalSim to simulate galaxies both on a grid and at random positions in larger scenes. Placing the galaxies at random positions requires their detection, which we performed with SExtractor. On the grid, we neglected the detection step and, therefore, the potential detection bias arising from it. The shear of the simulated images was measured with the fast moment-based method KSB, for which we note deviations from purely linear shear measurement biases. For the estimation of uncertainties, we used bootstrapping as an empirical method. We extended the response-based approach to work on a wider range of shears and provide accurate estimates of selection biases. We find that each method we studied on top of shape noise cancellation can further increase the efficiency of calibration simulations. The improvement depends on the considered shear amplitude range and the type of simulations (grid-based or random positions). The response method on a grid for small shears provides the biggest improvement. Here the runtime for the estimation of multiplicative biases can be lowered by a factor of 145 compared to the benchmark simulations without any cancellation. In the more realistic case of randomly positioned galaxies, we still find an improvement factor of 70 for small shears using the response method. Alternatively, the runtime can be lowered by a factor of 7 already using pixel noise cancellation on top of shape noise cancellation. Furthermore, we demonstrate that the efficiency of shape noise cancellation can be enhanced in the presence of blending if entire scenes are rotated instead of individual galaxies. [ABSTRACT FROM AUTHOR]

Kristina F Simacek,1 Tanya Nelson,2 Mignon Miller-Baldi,3 Susan C Bolge2 1PatientsLikeMe, Cambridge, MA, USA; 2Janssen Scientific Affairs, LLC, Raritan, NJ, USA; 3Janssen Medical Information Center, Titusville, NJ, USA Background: As patients are the ultimate stakeholder in their health, their perspectives should be included along with researchers, providers, and funders of research design, execution, and interpretation. Despite the high prevalence of type 2 diabetes mellitus (T2DM), patients are rarely directly included in these decisions. Purpose: We sought to determine areas of research most important to patients with T2DM, identify ways through which patients with T2DM want to engage in research, and evaluate online patient research networks as a source for obtaining patient perspectives on research engagement. Patients and methods: This study used an online patient community forum (PatientsLikeMe) to host two asynchronous moderated discussions, each with three to four prompted discussion posts. A qualitative summary of themes was derived from the posts. Results: Eighty-eight participants with T2DM took part. Participants were mostly white (86%), averaged 58.6 years of age, half were female (50%), and over half (62%) resided in the US. Research priorities included managing T2DM with comorbidities, controlling blood sugar levels, finding a cure, and understanding causes of T2DM. Participants wanted to see direct applications of research to their lives. Clinical research was perceived to have overly restrictive eligibility criteria and to measure outcome sets that do not adequately address patient health concerns. Participants indicated broad interest in partnering in research and a willingness to apply their skills and educational background to specific stages in the research process. Conclusion: Patients with T2DM would like researchers to address outcomes that have meaning in patients’ daily lives. Initiatives to involve patients in research should leverage and enable patients to contribute as participants, advisors, or co-investigators, going beyond research topic prioritization to full participation throughout the research process based on their abilities and interest. This study provides support for the use of online patient research network discussions to generate rich qualitative data to engage patients in research. Keywords: qualitative research, patient engagement, patient involvement, research priorities, diabetes mellitus, social networking, social media

We investigate the main tensions within the current standard model of cosmology from the perspective of the main statistics of cosmic voids, using the final BOSS DR12 data set. For this purpose, we present the first estimate of the S8 ≡ σ8 Ωm/0.3 and H0 parameters obtained from void number counts and shape distortions. To analyze void counts we relied on an extension of the popular volume-conserving model for the void size function, tailored to the application on data, including geometric and dynamic distortions. We calibrated the two nuisance parameters of this model with the official BOSS Collaboration mock catalogs and propagated their uncertainty through the statistical analysis of the BOSS void number counts. The constraints from void shapes come from the study of the geometric distortions of the stacked void-galaxy cross-correlation function. In this work we focus our analysis on the Ωm − σ8 and Ωm − H0 parameter planes and derive the marginalized constraints S8 = 0.813−0.068+0.093 and H0 = 67.3−9.1+10.0 km s−1 Mpc−1, which are fully compatible with constraints from the literature. These results are expected to notably improve in precision when analyzed jointly with independent probes and will open a new viewing angle on the rising cosmological tensions in the near future. [ABSTRACT FROM AUTHOR]

We have investigated the cosmological constraints that can be expected from measurement of the cross-correlation of galaxies with cosmic voids identified in the Euclid spectroscopic survey, which will include spectroscopic information for tens of millions of galaxies over 15 000 deg2 of the sky in the redshift range 0.9 ≤ z < 1.8. We have done this using simulated measurements obtained from the Flagship mock catalogue, the official Euclid mock that closely matches the expected properties of the spectroscopic dataset. To mitigate anisotropic selection-bias effects, we have used a velocity field reconstruction method to remove large-scale redshift-space distortions from the galaxy field before void-finding. This allowed us to accurately model contributions to the observed anisotropy of the cross-correlation function arising from galaxy velocities around voids as well as from the Alcock–Paczynski effect, and we studied the dependence of constraints on the efficiency of reconstruction. We find that Euclid voids will be able to constrain the ratio of the transverse comoving distance DM and Hubble distance DH to a relative precision of about 0.3%, and the growth rate fσ8 to a precision of between 5% and 8% in each of the four redshift bins covering the full redshift range. In the standard cosmological model, this translates to a statistical uncertainty ΔΩm = ±0.0028 on the matter density parameter from voids, which is better than what can be achieved from either Euclid galaxy clustering and weak lensing individually. We also find that voids alone can measure the dark energy equation of state to a 6% precision. [ABSTRACT FROM AUTHOR]

Next-generation telescopes, like Euclid, Rubin /LSST, and Roman , will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine-learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFRs) can be measured with deep-learning algorithms for observed galaxies within data mimicking the Euclid and Rubin /LSST surveys. We find that deep-learning neural networks and convolutional neural networks (CNNs), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multiband magnitudes together with |$H_{\scriptscriptstyle \rm E}$| -band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving (i) the redshift within a normalized error of <0.15 for 99.9 |${{\ \rm per\ cent}}$| of the galaxies with signal-to-noise ratio >3 in the |$H_{\scriptscriptstyle \rm E}$| band; (ii) the stellar mass within a factor of two (⁠|$\sim\!0.3 \rm \ dex$|⁠) for 99.5 |${{\ \rm per\ cent}}$| of the considered galaxies; and (iii) the SFR within a factor of two (⁠|$\sim\!0.3 \rm \ dex$|⁠) for |$\sim\!70{{\ \rm per\ cent}}$| of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning. [ABSTRACT FROM AUTHOR]

Euclid's photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quantity. We present a new calibration of the analytic HMF, at the level of accuracy and precision required for the uncertainty in this quantity to be subdominant with respect to other sources of uncertainty in recovering cosmological parameters from Euclid cluster counts. Our model is calibrated against a suite of N-body simulations using a Bayesian approach taking into account systematic errors arising from numerical effects in the simulation. First, we test the convergence of HMF predictions from different N-body codes, by using initial conditions generated with different orders of Lagrangian Perturbation theory, and adopting different simulation box sizes and mass resolution. Then, we quantify the effect of using different halo finder algorithms, and how the resulting differences propagate to the cosmological constraints. In order to trace the violation of universality in the HMF, we also analyse simulations based on initial conditions characterised by scale-free power spectra with different spectral indexes, assuming both Einstein–de Sitter and standard ΛCDM expansion histories. Based on these results, we construct a fitting function for the HMF that we demonstrate to be sub-percent accurate in reproducing results from 9 different variants of the ΛCDM model including massive neutrinos cosmologies. The calibration systematic uncertainty is largely sub-dominant with respect to the expected precision of future mass–observation relations; with the only notable exception of the effect due to the halo finder, that could lead to biased cosmological inference. [ABSTRACT FROM AUTHOR]

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE – YE, JE – HE and u – IE, IE – JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2. [ABSTRACT FROM AUTHOR]

Recently, many plant-derived bioactive agents have been included in dermo-cosmetics formulations. This leads to an extensive portfolio of innovative products with an expanded range of benefits, including anti-aging, antioxidant, hydrating and depigmenting. Although different technologies drawing on science and nature are used to create these high-performing molecules, there remains some debate about the mechanism of action of the natural bioactive ingredients within dermo-cosmetics. This review recapitulates the main biological mechanisms underlying the activity of natural active ingredients, with a specific focus on their synergistic use for the management of common, yet quite specific, skin conditions. A total of 28 plant-derived bioactives were selected from the Givaudan Active Beauty (Argenteuil, France) portfolio, a multinational company specializing in innovative natural actives research. An extensive literature review about their biological activity was conducted by a PubMed search using different keywords. No language or publication date restrictions were used. Givaudan Active Beauty data on file were also considered. The bioactive ingredients were described according to the pathogenetic mechanisms underlying 10 common skin conditions that dermo-cosmetics may address. Literature data have shown that plant-derived bioactives are involved in a wide range of biological mechanisms showing anti-inflammatory, antioxidant, and moisturizing properties, along with skin barrier protection and collagen synthesis activities. As a result, different combinations of bioactives within dermo-cosmetics can be defined to counteract simultaneously the different pathogenetic mechanisms underlying different skin conditions. Available literature supports the synergistic use of plant-derived bioactive agents within dermo-cosmetics as a viable and safe option for managing the most common skin conditions. [ABSTRACT FROM AUTHOR]

This paper describes the relationships between large-scale modes of climate variability and its related weather types with the fluctuations in the yield of maize crops in Veneto, Italy. The teleconnections analysed in this work are the winter North Atlantic Oscillation (NAO) and the summer North Atlantic Oscillation (SNAO); the West African monsoon (WAM) and the Intertropical Front (ITF). Despite that these indices are not rigorously linked to one another, they result in being considerably related to atmospheric circulation regimes and associated weather types. They have an impact on temperature and precipitation patterns in Italy and on yields of maize crops in Veneto, a region located in northeast Italy. Yields are strongly affected by large-scale temperate and tropical variability directly through three main circulation regimes. Troughing weather regimes that produced below average temperatures depress yields over the entire Veneto region, as does the zonal regime that affects rainfall. Results confirm the relevance of large-scale modes and associated weather regimes and types on maize crop yields fluctuations in Veneto. [ABSTRACT FROM AUTHOR]

A theory is presented for the evaluation of the different terms of the pressure gradient force, when mesoscale flow is driven by a sensible heat source in the planetary boundary layer (PBL), or by an elevated confined heat source, such as the release of the latent heat of condensation in a cloud. The nonlinear and linear, and the nonhydrostatic and the hydrostatic pressure gradient contributions are evaluated. The validity of the different approximations is discussed as a function of time and space scales. In addition, the validity of this approach is explored as a function of atmospheric environmental parameters, such as static stability, large-scale flow, and dissipation. By accessing the relative importance of each contribution, specific solution techniques for mesoscale atmospheric flows can be adopted. For example, when the linear contributions dominate, an exact analytic model could be used, rather than relying on numerical approximation solution techniques. When the hydrostatic contribution dominates, the spatial variation of the vertical temperature profile can be used to uniquely define the horizontal pressure gradient force.

The aim of this article is to discuss pure variable inclusion logics, that is, logical systems where valid entailments require that the propositional variables occurring in the conclusion are included among those appearing in the premises, or vice versa. We study the subsystems of Classical Logic satisfying these requirements and assess the extent to which it is possible to characterise them by means of a single logical matrix. In addition, we semantically describe both of these companions to Classical Logic in terms of appropriate matrix bundles and as semilattice-based logics, showing that the notion of consequence in these logics can be interpreted in terms of truth (or non-falsity) and meaningfulness (or meaninglessness) preservation. Finally, we use Płonka sums of matrices to investigate the pure variable inclusion companions of an arbitrary finitary logic. [ABSTRACT FROM AUTHOR]

Intron-containing tRNA precursors on both sides of the intron are divided by tRNA splicing endonuclease. Based on the experiment, the endonuclease uses different sets of structural elements which supersede the popular belief that the enzyme connects only to the mature domain by means of invariant bases. In the mature domain, a subset of recognition elements is necessary for recognition of both cleavage sites but in the exon-intron boundaries, two other subsets are utilized for recognition of either one or the other cleavage site. The findings advance the two-active model for eucaryal endonuclease.