I'm a phenomenologist who merges physical theories and observations of the universe to unravel its mysteries.
My research sharpens our understanding and modeling capabilities for present and future measurements,
harnessing cutting-edge statistical machine learning techniques to extract insights from complex datasets.
My focus areas include testing experiment agreement, studying Dark Energy, Dark Matter, cosmological neutrinos,
and Gravity.
Scroll down to find more information about my research interests
and publications.
Marco Raveri
Physics Department,
University of Genova,
Via Dodecaneso, 33,
Genova, 16146, GE, Italy
marco.raveri@unige.it
A Universe described by General Relativity and filled with ordinary matter is naturally expected to experience decelerated expansion. One of the most remarkable results of contemporary observational cosmology is the evidence that this is not the case. This cosmic acceleration is today one of the few evidences of the existence of physical phenomena beyond what we already know.
How we can build working models of this phenomenon? How we can test them? What cosmological probes can we use to distinguish between different candidate models?
As we gather more and more precise measurements small hints of discrepancies between different cosmological probes appeared. The expansion rate of the Universe as derived from cosmic microwave background observations differs from direct measurements from the distance ladder. Measurements from large scale structure surveys and the cosmic microwave background show different pictures of how cosmological structures grew over time. How significant are these discrepancies? Can they point toward a radical re-evaluation of our cosmological model? Are they just due to residual systematic effects?
INSPIRE LINK to my publications
The phase space numerical investigation of different dark energy models for the first order system. Initial conditions are evolved both in the past (blue lines) and in the future (green lines). The red line corresponds to the ΛCDM trajectory.
The marginalized joint posterior of a subset of parameters of the K-mouflage model and the Hubble constant. In all three panels different colors correspond to different combination of cosmological probes, as shown in legend. The darker and lighter shades correspond respectively to the 68% C.L. and the 95% C.L. regions.
The CMB anisotropy source functions in k-space in units of amplitude of primordial comoving curvature perturbation in two MG example models and GR. Different lines correspond to different physical effects and models, as shown in figure and legend. The vertical dashed line shows mode that crosses the horizon at recombination.
The statistical significance of different Concordance and Discordance Estimators for various data set couples: the difference in log-likelihood at maximum posterior (MAP), the update parameter shifts test, the exact 1D parameter shifts, and the “rule of thumb difference in mean”. Different colors indicate different tests, as shown in legend. The labels report different levels of statistical significance: P1 ≡ 32%, P2 ≡ 5%, P3 ≡ 0.3%, P4 ≡ 0.007%. Values that are identified as failure modes of one of the estimators are not shown in figure. The darker shade indicates results that are not statistically significant.
EFTCAMB is a patch of the public Einstein-Boltzmann solver CAMB, which implements the Effective Field Theory approach to cosmic acceleration. The code can be used to investigate the effect of different EFT operators on linear perturbations as well as to study perturbations in any specific DE/MG model that can be cast into EFT framework. To interface EFTCAMB with cosmological data sets, we equipped it with a modified version of CosmoMC, namely EFTCosmoMC, creating a bridge between the EFT parametrization of the dynamics of perturbations and observations.
CosmicFish is a forecasting tool to study what future cosmology will look like. This tools is using EFTCAMB and MGCAMB to ensure maximum coverage of cosmological models and will serve two important purposes. At first it will allow to optimise model testing, forecasting the expected constraints on several models and parametrizations to select the ones that are better constrained by the data. In the second place it will allow the design and optimization of experimental probes that aim at testing gravitational theories.