About Me

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 statistical methods for data analysis, studying Dark Energy, Dark Matter, cosmological neutrinos, and Gravity.
Scroll down to find more information about my research interests and publications.


Contact Details

Marco Raveri
Physics Department,
University of Genova,
Via Dodecaneso, 33,
Genova, 16146, GE, Italy
marco.raveri@unige.it

Research

Cosmic Acceleration and Gravity

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?

Cosmic Concordance

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?

More generally, I am interested in

  • Statistical, computational, and machine learning methods in cosmology;
  • Dark matter, dark radiation, and neutrinos in the Universe;
  • Large-scale structure as a probe of fundamental physics;
  • Cosmological perturbation theory;
  • Testing new physics with cosmological observations;
  • Development of novel cosmological probes;
  • Non-standard explanations of cosmic acceleration.

Publications

INSPIRE LINK to all my publications

My most representative publications

While I am proud of all my publications, some of them are particularly representative of my research interests. Here is a selection of them:

  1. Understanding posterior projection effects with normalizing flows [arXiv:2409.09101]
  2. Resolving the Hubble tension at late times with Dark Energy [arXiv:2309.06795]
  3. What does a cosmological experiment really measure? Covariant posterior decomposition with normalizing flows [arXiv:2112.05737]
  4. Imprints of cosmological tensions in reconstructed gravity [arXiv:2107.12992]
  5. Non-Gaussian estimates of tensions in cosmological parameters [arXiv:2105.03324]
  6. Reconstructing Gravity on Cosmological Scales [arXiv:1902.01366]
  7. Concordance and Discordance in Cosmology [arXiv:1806.04649]
  8. Effective Field Theory of Cosmic Acceleration: an implementation in CAMB [arXiv:1312.5742]

Codes

EFTCAMB / EFTCosmoMC

EFTCAMB is an extension of the public Einstein–Boltzmann solver CAMB, designed to implement the Effective Field Theory (EFT) approach to cosmic acceleration. The code allows users to explore the impact of different EFT operators on linear perturbations, as well as to analyze perturbations in any specific dark energy or modified gravity model that can be formulated within the EFT framework.

TENSIOMETER

Tensiometer is a Python toolkit designed to quantify and analyze the level of agreement or tension between high-dimensional posterior distributions. It offers a suite of methods ranging from Gaussian-based metrics to advanced, non-Gaussian techniques powered by machine learning to assess discrepancies between distributions. The package also includes features for examining posterior profiles, chain convergence diagnostics, and extracting measured parameter summaries. Full documentation with examples is available here.

COSMICFISH

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.