Cosmic shear statistics in cosmologies with non-Gaussian initial conditions

We computed the power spectrum of weak cosmic shear in models with non-Gaussian primordial density fluctuations. Cosmological initial conditions deviating from Gaussianity have recently attracted much attention in the literature, especially with respect to their effect on the formation of non-linear structures and because of the bounds that they can put on the inflationary epoch. The fully non-linear matter power spectrum was evaluated with the use of the physically motivated, semi-analytic halo model, where the mass function and linear halo bias were suitably corrected for non-Gaussian cosmologies. In agreement with previous work, we found that a level of non-Gaussianity compatible with CMB bounds and with positive skewness produces an increase in power of the order of a few percent at intermediate scales. We then used the matter power spectrum, together with observationally motivated background source redshift distributions in order to compute the cosmological weak lensing power spectrum. We found that the degree of deviation from the power spectrum of the reference Gaussian model is small compared to the statistical error expected from even future weak lensing surveys. However, summing the signal over a large range of multipoles can beat down the noise, bringing to a significant detection of non-Gaussianity at the level of $|f_\mathrm{NL}| \simeq$ few tens, when all other cosmological parameters are held fixed. Finally, we have shown that the constraints on the level of non-Gaussianity can be improved by $\sim 20$ with the use of weak lensing tomography.Comment: 15 pages, 10 figures. Accepted by MNRA

Young stellar clusters and associations in M33

We analyse multi-wavelength observations of 32 young star clusters and associations in M33 with known oxygen abundance (8 < 12 + log(O/H) < 8.7), using ultraviolet (UV), optical, mid-infrared (MIR), CO (1-0) and 21-cm line (HI) observations. We derive their spectral energy distribution, and we determine age, bolometric luminosities, masses and the extinction, by comparing the multi-band integrated photometry to single-age stellar population models. The stellar system ages range between 2 and 15 Myr, masses are between 3 x 10^2 and 4 x 10^4 M_sun, and the intrinsic extinction, A_V, varies from 0.3 to 1 mag. We find a correlation between age and extinction, and between the cluster mass and size. The MIR emission shows the presence of a dust component around the clusters whose fractional luminosity at 24 um, L_{24}/L_{Bol}, decreases with the galactocentric distance. However, the total IR luminosity inferred from L_{24} is smaller than what we derive from the extinction corrections. The Halpha luminosity predicted by population synthesis models is larger than the observed one, especially for low-mass systems (M < 10^4 M_sun). Such a difference is reduced, but not erased, when the incomplete sampling of the initial mass function (IMF) at the high-mass end is taken into account. Our results suggest that a non-negligible fraction of UV ionising and non-ionising radiation is leaking into the ISM outside the HII regions. This would be in agreement with the large UV and Halpha diffuse fractions observed in M33, but it implies that stellar systems younger than 3 Myr retain, on average, only 30% of their Lyman continuum photons. However, the uncertainties on cluster ages and the stochastic fluctuations of the IMF do not allow to accurately quantify this issue. We also consider the possibility that this discrepancy is the consequence of a suppressed or delayed formation of the most massive stars.Comment: 17 pages, 13 figures. Accepted for publications in A&A; v2 --> Table 2 corrected because of a misprint in the FUV magnitude

Halo statistics in non-Gaussian cosmologies: the collapsed fraction, conditional mass function, and halo bias from the path-integral excursion set method

Characterizing the level of primordial non-Gaussianity (PNG) in the initial conditions for structure formation is one of the most promising ways to test inflation and differentiate among different scenarios. The scale-dependent imprint of PNG on the large-scale clustering of galaxies and quasars has already been used to place significant constraints on the level of PNG in our observed Universe. Such measurements depend upon an accurate and robust theory of how PNG affects the bias of galactic halos relative to the underlying matter density field. We improve upon previous work by employing a more general analytical method - the path-integral extension of the excursion set formalism - which is able to account for the non-Markovianity caused by PNG in the random-walk model used to identify halos in the initial density field. This non-Markovianity encodes information about environmental effects on halo formation which have so far not been taken into account in analytical bias calculations. We compute both scale-dependent and -independent corrections to the halo bias, along the way presenting an expression for the conditional collapsed fraction for the first time, and a new expression for the conditional halo mass function. To leading order in our perturbative calculation, we recover the halo bias results of Desjacques et. al. (2011), including the new scale-dependent correction reported there. However, we show that the non-Markovian dynamics from PNG can lead to marked differences in halo bias when next-to-leading order terms are included. We quantify these differences here. [abridged]Comment: Accepted for publication in MNRAS. Includes minor revisions recommended by referee, slightly revised notation for clarity, and corrected typo

Imprints of dark energy on cosmic structure formation: II) Non-Universality of the halo mass function

The universality of the halo mass function is investigated in the context of dark energy cosmologies. This widely used approximation assumes that the mass function can be expressed as a function of the matter density omega_m and the rms linear density fluctuation sigma only, with no explicit dependence on the properties of dark energy or redshift. In order to test this hypothesis we run a series of 15 high-resolution N-body simulations for different cosmological models. These consists of three LCDM cosmologies best fitting WMAP-1, 3 and 5 years data, and three toy-models characterized by a Ratra-Peebles quintessence potential with different slopes and amounts of dark energy density. These toy models have very different evolutionary histories at the background and linear level, but share the same sigma8 value. For each of these models we measure the mass function from catalogues of halos identified in the simulations using the Friend-of-Friend (FoF) algorithm. We find redshift dependent deviations from a universal behaviour, well above numerical uncertainties and of non-stochastic origin, which are correlated with the linear growth factor of the investigated cosmologies. Using the spherical collapse as guidance, we show that such deviations are caused by the cosmology dependence of the non-linear collapse and virialization process. For practical applications, we provide a fitting formula of the mass function accurate to 5 percents over the all range of investigated cosmologies. We also derive an empirical relation between the FoF linking parameter and the virial overdensity which can account for most of the deviations from an exact universal behavior. Overall these results suggest that the halo mass function contains unique cosmological information since it carries a fossil record of the past cosmic evolution.Comment: 21 pages, 19 figures, 5 tables, published in MNRAS. Paper I: arXiv:0903.549

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

<p>Abstract</p> <p>Background</p> <p>Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child.</p> <p>The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (<it>MTHFR </it>677C>T and 1298A>C, <it>MTRR </it>66A>G, <it>MTR </it>2756A>G, <it>RFC1 </it>80G>A and <it>TYMS </it>28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map.</p> <p>Results</p> <p>TWIST system selected 6 variables (BNMN frequency, <it>MTHFR </it>677TT, <it>RFC1 </it>80AA, <it>TYMS </it>1494 6bp +/+, <it>TYMS </it>28bp 3R/3R and <it>MTR </it>2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother).</p> <p>Conclusions</p> <p>Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.</p

Constraining primordial non-Gaussianity with future galaxy surveys

We study the constraining power on primordial non-Gaussianity of future surveys of the large-scale structure of the Universe for both near-term surveys (such as the Dark Energy Survey - DES) as well as longer term projects such as Euclid and WFIRST. Specifically we perform a Fisher matrix analysis forecast for such surveys, using DES-like and Euclid-like configurations as examples, and take account of any expected photometric and spectroscopic data. We focus on two-point statistics and we consider three observables: the 3D galaxy power spectrum in redshift space, the angular galaxy power spectrum, and the projected weak-lensing shear power spectrum. We study the effects of adding a few extra parameters to the basic LCDM set. We include the two standard parameters to model the current value for the dark energy equation of state and its time derivative, w_0, w_a, and we account for the possibility of primordial non-Gaussianity of the local, equilateral and orthogonal types, of parameter fNL and, optionally, of spectral index n_fNL. We present forecasted constraints on these parameters using the different observational probes. We show that accounting for models that include primordial non-Gaussianity does not degrade the constraint on the standard LCDM set nor on the dark-energy equation of state. By combining the weak lensing data and the information on projected galaxy clustering, consistently including all two-point functions and their covariance, we find forecasted marginalised errors sigma (fNL) ~ 3, sigma (n_fNL) ~ 0.12 from a Euclid-like survey for the local shape of primordial non-Gaussianity, while the orthogonal and equilateral constraints are weakened for the galaxy clustering case, due to the weaker scale-dependence of the bias. In the lensing case, the constraints remain instead similar in all configurations.Comment: 20 pages, 10 Figures. Minor modifications; accepted by MNRA

Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis

Importance: Cognitive impairment is a common and disabling feature of multiple sclerosis (MS), but a precise characterization of cognitive phenotypes in patients with MS is lacking. Objectives: To identify cognitive phenotypes in a clinical cohort of patients with MS and to characterize their clinical and magnetic resonance imaging (MRI) features. Design, setting, and participants: This multicenter cross-sectional study consecutively screened clinically stable patients with MS and healthy control individuals at 8 MS centers in Italy from January 1, 2010, to October 31, 2019. Patients with MS and healthy control individuals who were not using psychoactive drugs and had no history of other neurological or medical disorders, learning disability, severe head trauma, and alcohol or drug abuse were enrolled. Main outcomes and measures: Participants underwent a neurological examination and a cognitive evaluation with the Rao Brief Repeatable Battery and Stroop Color and Word Test. A subgroup of participants also underwent a brain MRI examination. Latent profile analysis was used on cognitive test z scores to identify cognitive phenotypes. Linear regression and mixed-effects models were used to define clinical and MRI features of each phenotype. Results: A total of 1212 patients with MS (mean [SD] age, 41.1 [11.1] years; 784 women [64.7%]) and 196 healthy control individuals (mean [SD] age, 40.4 [8.6] years; 130 women [66.3%]) were analyzed in this study. Five cognitive phenotypes were identified: preserved cognition (n = 235 patients [19.4%]), mild-verbal memory/semantic fluency (n = 362 patients [29.9%]), mild-multidomain (n = 236 patients [19.5%]), severe-executive/attention (n = 167 patients [13.8%]), and severe-multidomain (n = 212 patients [17.5%]) involvement. Patients with preserved cognition and mild-verbal memory/semantic fluency were younger (mean [SD] age, 36.5 [9.8] years and 38.2 [11.1] years) and had shorter disease duration (mean [SD] 8.0 [7.3] years and 8.3 [7.6] years) compared with patients with mild-multidomain (mean [SD] age, 42.6 [11.2] years; mean [SD] disease duration, 12.8 [9.6] years; P < .001), severe-executive/attention (mean [SD] age, 42.9 [11.7] years; mean [SD] disease duration, 12.2 [9.5] years; P < .001), and severe-multidomain (mean [SD] age, 44.0 [11.0] years; mean [SD] disease duration, 13.3 [10.2] years; P < .001) phenotypes. Severe cognitive phenotypes prevailed in patients with progressive MS. At MRI evaluation, compared with those with preserved cognition, patients with mild-verbal memory/semantic fluency exhibited decreased mean (SE) hippocampal volume (5.42 [0.68] mL vs 5.13 [0.68] mL; P = .04), patients with the mild-multidomain phenotype had decreased mean (SE) cortical gray matter volume (687.69 [35.40] mL vs 662.59 [35.48] mL; P = .02), patients with severe-executive/attention had higher mean (SE) T2-hyperintense lesion volume (51.33 [31.15] mL vs 99.69 [34.07] mL; P = .04), and patients with the severe-multidomain phenotype had extensive brain damage, with decreased volume in all the brain structures explored, except for nucleus pallidus, amygdala and caudate nucleus. Conclusions and relevance: This study found that by defining homogeneous and clinically meaningful phenotypes, the limitations of the traditional dichotomous classification in MS can be overcome. These phenotypes can represent a more meaningful measure of the cognitive status of patients with MS and can help define clinical disability, support clinicians in treatment choices, and tailor cognitive rehabilitation strategies

Matter power spectra in dynamical-Dark Energy cosmologies

(abridged) We used a suite of numerical cosmological simulations in order to investigate the effect of gas cooling and star formation on the large scale matter distribution. The simulations follow the formation of cosmic structures in five different Dark Energy models: the fiducial $\Lambda$CDM cosmology and four models where the Dark Energy density is allowed to have a non-trivial redshift evolution. For each cosmology we have a control run with dark matter only, in order to allow a direct assessment of the impact of baryonic processes. We found that the power spectra of gas and stars, as well as the total matter power spectrum, are in qualitative agreement with the results of previous works in the framework of the fiducial model, although several quantitative differences exist. We used the halo model in order to investigate the backreaction of gas and stars on the dark matter distribution, finding that it is very well reproduced by increasing the average dark matter halo concentration by 17%, irrespective of the mass. Moving to model universes dominated by dynamical Dark Energy, it turns out that they introduce a specific signature on the power spectra of the various matter components, that is qualitatively independent of the exact cosmology considered. This generic shape is well captured by the halo model, however the finer details of the dark matter power spectrum can be precisely captured only at the cost of a few slight modifications to the ingredients entering the model. The backreaction of baryons onto the dark matter distribution works pretty much in the same way as in the reference $\Lambda$CDM model. Nonetheless, the increment in average concentration is less pronounced than in the fiducial model (only $\sim 10$), in agreement with a series of other clues pointing toward the fact that star formation is less efficient when Dark Energy displays a dynamical evolution.Comment: 15 pages, 8 figures. Accepted by MNRA