©2019 by Natalia Porqueres






I am postdoc researcher at Imperial College, in London, working with Prof. Alan Heavens. Before moving to Imperial, I earned my PhD at the Max-Planck Institute for Astrophysics, under the supervision of Torsten Ensslin and Jens Jasche.

My work focuses on understanding the large-scales structures of the Universe. I develop statistical and data science methods to study the formation and dynamical evolution of cosmic large-scale structures and galaxies from cosmological datasets. In particular, I investigated the nature of AGN and quasars, one of the most powerful sources of the Universe, and I used the information in the quasar spectra to draw conclusions on the matter clustering at high redshift. 



Large-scale structure inference from the Lyman-α forest

Most of the analyses of the cosmic large-scale structure are based on galaxy observations. However, galaxies mostly trace the overdense regions of the Universe. A complementary probe of the large-scale structure is the Lyman-α forest, which sensitive to the low-density regimes. In addition, by probing small scales down to 1 Mpc, the Lyman-α forest is sensitive to neutrino masses and dark matter models. 

A usual approach to extract information from the Ly-α forest is to analyse the power spectrum. However, the non-linear dynamics transport the information from the two-point statistics to high-order correlations. 

I developed a Bayesian framework to infer the 3D dark matter density and its dynamics from the Lyman-α forest. This method employs a physical forward model that encodes the high-order correlations of the dark matter density. 

Robust data models for contaminated datasets

Next generation of galaxy surveys will not be limited by statistical noise but by systematic uncertainties. In the past, such effects have been addressed by generating templates for such contaminations and accounting for their effect in a Bayesian framework. 

Since the future surveys can be subject to yet unknown contaminations, I developed a robust likelihood to effectively deal with effects induced by unknown foreground and target contaminations. This likelihood recovers an unbiased power spectrum at all scales even from data subject to contaminations. 

This likelihood has been applied to the analysis of SDSS-III data in Lavaux, Jasche & Leclerqc (2019), recovering the unbiased power spectrum even at large scales (k<0.01) where the state-of-the-art analyses show deviations due to contaminations in the data. 

Formation and evolution of AGN

Active galactic nuclei (AGN) are black holes at the centre of galaxies that accrete material from the galaxy’s central region. As the material falls in towards the black hole, it spirals in and forms into a disk. This accretion disk heats up due to the gravitational and frictional forces and emits strong radiation in X-ray, gamma-rays, and radio. AGN are one of the most powerful sources of the Universe. 

I investigated how AGN formation and evolution depends on their environment. I characterized the dark matter environment with a dark matter density field inferred from a galaxy survey. This analysis provided evidence of an evolutionary sequence between two types of AGN.



You can find my published research in:




I am a member of the Aquila Consortium, a group of people interested in applying data science methods to the analysis of cosmological data. 

You can find more information about our science in 




Blackett Laboratory
Imperial College London,
London SW7 2AZ

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