Playing dice with the universe: Bayesian statistical analyses of cosmological models and new observables

The ultimate goal of cosmologists is to find a cosmological model able to explain the current observational data. In this sense, the Standard Cosmological model establishes that our universe is mainly composed of two unknown components: a type of matter that is known to only interact through gravitation, Cold Dark Matter, and a substance responsible for the current accelerated expansion of the universe that can be modelled by a cosmological constant. Still, this model, though successful, fails to answer hot-burning questions in the field. For this reason, theoretical cosmologists focus on developing further modifications of the model to test them against astrophysical data and check whether alternative scenarios can provide a better explanation of the observations.This thesis is dedicated to the Bayesian statistical analyses of extensions of the Standard Cosmological model using several astronomical data sets, and to the forecast of new observables and experiments. The first part focuses on data science and inflation, and it aims to constrain inflationary models using advanced inference techniques. The second part of the thesis is dedicated to the novel concept of cross-correlations of gravitational-wave physics and large scale structure observables. The third part of this thesis is dedicated to the incoming ESA Euclid satellite, and in particular, it focuses on a crucial data science analysis software for the mission: the code “Cosmological Likelihood for Observables in Euclid”, also known as CLOE.Theoretical Physic

Learning how to surf: reconstructing the propagation and origin of gravitational waves with Gaussian processes

Large scale structure and cosmolog

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America.

Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates

Euclid preparation. TBD. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

Cosmological surveys planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions. However, precision pushes us to high levels of accuracy in the theoretical modelling of the LSS observables, in order not to introduce biases in the estimation of cosmological parameters. In particular, effects such as redshift-space distortions (RSD) can become relevant in the computation of harmonic-space power spectra even for the clustering of the photometrically selected galaxies, as it has been previously shown in literature studies. In this work, we investigate the contribution of linear RSD, as formulated in the Limber approximation by arXiv:1902.07226, in forecast cosmological analyses with the photometric galaxy sample of the Euclid survey, in order to assess their impact and quantify the bias on the measurement of cosmological parameters that neglecting such an effect would cause. We perform this task by producing mock power spectra for photometric galaxy clustering and weak lensing, as expected to be obtained from the Euclid survey. We then use a Markov chain Monte Carlo approach to obtain the posterior distributions of cosmological parameters from such simulated observations. We find that neglecting the linear RSD leads to significant biases both when using galaxy correlations alone and when these are combined with cosmic shear, in the so-called 3$\times$2pt approach. Such biases can be as large as $5\,\sigma$-equivalent when assuming an underlying $\Lambda$CDM cosmology. When extending the cosmological model to include the equation-of-state parameters of dark energy, we find that the extension parameters can be shifted by more than $1\,\sigma$.Comment: 15 pages, 5 figures. To be submitted in A&

Cross-correlation of the astrophysical gravitational-wave background with galaxy clustering

Large scale structure and cosmologyTheoretical Physic

Constraints on extended Bekenstein models from cosmological, astrophysical, and local data

Large scale structure and cosmolog

Melatonin decreases pulmonary vascular remodeling and oxygen sensitivity in pulmonary hypertensive newborn lambs

Background: Chronic hypoxia and oxidative stress during gestation lead to pulmonary hypertension of the neonate (PHN), a condition characterized by abnormal pulmonary arterial reactivity and remodeling. Melatonin has strong antioxidant properties and improves pulmonary vascular function. Here, we aimed to study the effects of melatonin on the function and structure of pulmonary arteries from PHN lambs. Methods: Twelve lambs (Ovis aries) gestated and born at highlands (3,600 m) were instrumented with systemic and pulmonary catheters. Six of them were assigned to the control group (CN, oral vehicle) and 6 were treated with melatonin (MN, 1 mg.kg(-1) .d(-1)) during 10 days. At the end of treatment, we performed a graded oxygenation protocol to assess cardiopulmonary responses to inspired oxygen variations. Further, we obtained lung and pulmonary trunk samples for histology, molecular biology, and immunohistochemistry determinations. Results: Melatonin reduced the in vivo pulmonary pressor response to oxygenation changes. In addition, melatonin decreased cellular density of the media and diminished the proliferation marker KI67 in resistance vessels and pulmonary trunk (p < 0.05). This was associated with a decreased in the remodeling markers alpha-actin (CN 1.28 +/- 0.18 vs. MN 0.77 +/- 0.04, p < 0.05) and smoothelin-B (CN 2.13 +/- 0.31 vs. MN 0.88 +/- 0.27, p < 0.05). Further, melatonin increased vascular density by 134% and vascular luminal surface by 173% (p < 0.05). Finally, melatonin decreased nitrotyrosine, an oxidative stress marker, in small pulmonary vessels (CN 5.12 +/- 0.84 vs. MN 1.14 +/- 0.34, p < 0.05). Conclusion: Postnatal administration of melatonin blunts the cardiopulmonary response to hypoxia, reduces the pathological vascular remodeling, and increases angiogenesis in pulmonary hypertensive neonatal lambs. These effects improve the pulmonary vascular structure and function in the neonatal period under chronic hypoxia

Melatonin Decreases Pulmonary Vascular Remodeling and Oxygen Sensitivity in Pulmonary Hypertensive Newborn Lambs

Background: Chronic hypoxia and oxidative stress during gestation lead to pulmonary hypertension of the neonate (PHN), a condition characterized by abnormal pulmonary arterial reactivity and remodeling. Melatonin has strong antioxidant properties and improves pulmonary vascular function. Here, we aimed to study the effects of melatonin on the function and structure of pulmonary arteries from PHN lambs.Methods: Twelve lambs (Ovis aries) gestated and born at highlands (3,600 m) were instrumented with systemic and pulmonary catheters. Six of them were assigned to the control group (CN, oral vehicle) and 6 were treated with melatonin (MN, 1 mg.kg−1.d−1) during 10 days. At the end of treatment, we performed a graded oxygenation protocol to assess cardiopulmonary responses to inspired oxygen variations. Further, we obtained lung and pulmonary trunk samples for histology, molecular biology, and immunohistochemistry determinations.Results: Melatonin reduced the in vivo pulmonary pressor response to oxygenation changes. In addition, melatonin decreased cellular density of the media and diminished the proliferation marker KI67 in resistance vessels and pulmonary trunk (p &lt; 0.05). This was associated with a decreased in the remodeling markers α-actin (CN 1.28 ± 0.18 vs. MN 0.77 ± 0.04, p &lt; 0.05) and smoothelin-B (CN 2.13 ± 0.31 vs. MN 0.88 ± 0.27, p &lt; 0.05). Further, melatonin increased vascular density by 134% and vascular luminal surface by 173% (p &lt; 0.05). Finally, melatonin decreased nitrotyrosine, an oxidative stress marker, in small pulmonary vessels (CN 5.12 ± 0.84 vs. MN 1.14 ± 0.34, p &lt; 0.05).Conclusion: Postnatal administration of melatonin blunts the cardiopulmonary response to hypoxia, reduces the pathological vascular remodeling, and increases angiogenesis in pulmonary hypertensive neonatal lambs.These effects improve the pulmonary vascular structure and function in the neonatal period under chronic hypoxia

Euclid:The search for primordial features

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measurements from the $Euclid$ space mission will provide unique constraints on the primordial power spectrum, thanks to the redshift coverage and high-accuracy measurement of nonlinear scales, thus allowing us to investigate deviations from the standard power-law primordial power spectrum. We consider two models with primordial undamped oscillations superimposed on the matter power spectrum, one linearly spaced in $k$-space the other logarithmically spaced in $k$-space. We forecast uncertainties applying a Fisher matrix method to spectroscopic galaxy clustering, weak lensing, photometric galaxy clustering, cross correlation between photometric probes, spectroscopic galaxy clustering bispectrum, CMB temperature and $E$-mode polarization, temperature-polarization cross correlation, and CMB weak lensing. We also study a nonlinear density reconstruction method to retrieve the oscillatory signals in the primordial power spectrum. We find the following percentage relative errors in the feature amplitude with $Euclid$ primary probes for the linear (logarithmic) feature model: 21% (22%) in the pessimistic settings and 18% (18%) in the optimistic settings at 68.3% confidence level (CL) using GC$_{\rm sp}$+WL+GC$_{\rm ph}$+XC. Combining all the sources of information explored expected from $Euclid$ in combination with future SO-like CMB experiment, we forecast ${\cal A}_{\rm lin} \simeq 0.010 \pm 0.001$ at 68.3% CL and ${\cal A}_{\rm log} \simeq 0.010 \pm 0.001$ for GC$_{\rm sp}$(PS rec + BS)+WL+GC$_{\rm ph}$+XC+SO-like both for the optimistic and pessimistic settings over the frequency range $(1,\,10^{2.1})$