The Large-scale structure of the Universe

(fast and renormalized) Two-loop EFT

Towards a fast implementation and renormalization of the two-loop EFT

Described in: Renormalization and fast computation

Collaboration with: Mathias Garny, Thomas Bakx, Zvonimir Vlah, Elisa Chisari

Galaxy bias renormalization group

We describe how the galaxy bias parameters evolve as a function of the energy scale considered, similarly to the RG-flow from particle physics.

Described in: Bias RG-flow, with stochasticity, with PNG, at two-loops

Collaboration with: Fabian Schmidt, Charalampos Nikolis, Mathias Garny, Thomas Bakx, Zvonimir Vlah

Multi-tracer and EFTofLSS

We generalize the EFTofLSS approach to include multiple tracers. We find ~40% improvement in the extraction of cosmological parameters.

Described in: Real space, Redshift space, Fisher analysis

Collaboration with: Thiago Mergulhão, Rodrigo Voivodic, Raul Abramo, Francesco Conteddu

Beyond-LCDM

I am also interested in how we can probe models beyond Lambda-CDM with LSS and CMB data

Described in: IDM-DR, IDM-DR with Clusters, IDM-DR in data, HNEDE, HNEDE with interactions

Collaboration with: Mathias Garny, Asmaa Mazoun, Joe Mohr, Sebastian Bocquet, Florian Niedermann, Martin Sloth

EFTofLSS at 3-loops

We extend the EFTofLSS framework to 3-loops, showing that the perturbative series behaves as an asymptotic series and discussing the convergence radius of the theory.

Described in: 3Loop

Collaboration with: Thomas Konstandin, Rafael Porto

The Halo-Void (dust) model

We generalize the Halo Model to include voids and dust, showing that it improves the transition between the 1Halo and 2Halo term. The Halo Void model also solves the normalization issues with the halo mass function.

Described in: HaloVoidDust

Collaboration with: Rodrigo Voivodic, Marcos Lima

Schrödinger-Poisson method

We develop a Schrödinger-Poisson code to account for the evolution of the BAO scale

Described in: SP

Collaboration with: Mathias Garny, Thomas Konstandin

Log-density fields

We implement the EFTofLSS framework to log-transformed field, showing that this field is more Gaussian and linear

Described in: LogEFT

Collaboration with: Rodrigo Voivodic

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