Natalia Porqueres

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I am a postdoc researcher at Imperial College, in London, working in the fields of cosmology and astrostatistics with Alan Heavens and Daniel Mortlock. I am also a member of the Aquila Consortium, a group of people interested in applying data science methods to cosmological data. Formerly, I was a PhD student at the Max-Planck Institute for Astrophysics (MPA), supervised by Jens Jasche and Torsten Ensslin.

My work focuses on understanding the large-scale structures of the Universe. I develop statistical and data analysis tools to study the formation and dynamics of cosmic large-scale structures. 

 
 

Research

Bayesian forward modelling of cosmic shear data

The next-generation of surveys will provide unprecedented precision in cosmic shear measurements, posing a challenge on how to extract as much information from the data as possible. Harvesting the full information content of the data will require a field-based approach, in which every data point is used,  rather than relying on summary statistics that do not capture all the information and whose distributions are not well known.

 

I developed a Bayesian hierarchical model to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data.  This approach employs a physical description of gravity, improving upon previous methods that assumed Gaussian distributions of the shear field, and allowing us to sample from the initial conditions. With this more complex data model, we get a better representation of the data, and we extract information beyond the two-point statistics, exploiting the full information content of the shear fields.

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. Also, 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.

 
 

Talks

  • 2021 February, Euclid Forward Modelling WP (invited, remote). "Bayesian forward modelling of cosmic shear data". 

  • 2021 January, Leiden University (invited, remote). "Bayesian forward modelling of cosmic shear data". 

  • 2020 December, Euclid UK Annual Meeting. "Bayesian forward modelling of cosmic shear data". 

  • 2020 December, University of Arizona (invited, remote). "Bayesian forward modelling of cosmic shear data".​

  • 2020 December, Euclid Weak Lensing WP (remote). "Bayesian forward modelling of cosmic shear data". 

  • 2020 July, Cosmology Talks, University of Auckland (invited, remote).  "A field-based approach to inference from Lyman alpha forest data".​

  • 2020 May, Queen Mary University, London (invited). "Inferring the dynamics of cosmic structures from the Lyman-alpha forest".​

  • 2020 February, Laboratoire Lagrange, Nice (invited). "Inferring the dynamics of cosmic structures at high-redshift".​

  • 2020 February, University College of London (UCL), London (invited). "Inferring the dynamics of cosmic structures at high-redshift from the Ly-alpha forest". 

  • 2020 January, The Cosmic Web in the Local Universe Conference, Lorentz Center, Leiden (invited). "Inferring the dynamics of cosmic structures at high-redshift from the Ly-alpha forest".​

  • 2019 December, Euclid UK Annual Meeting, Royal Astronomical Society. "Bayesian treatment of unknown foreground contaminations in galaxy surveys"​

  • 2019 December, London Cosmology Discussion Meeting (LCDM), Royal Astronomical Society. "Inferring the dynamics of cosmic structures at high-redshift from the Lyman-alpha forest". ​

  • 2019 November, Institut de ciències del cosmos (ICCUB), Universitat de Barcelona. "Inferring the dynamics of cosmic structures at high-redshift". ​

  • 2019 October, Imperial College London. "Inferring the dynamics of cosmic structures at high-redshift".​

  • 2018 November, CCA Flatiron Institute, New York (invited). "Inferring high-redshift large-scale structure dynamics from the Lyman-alpha forest"​

  • 2018 November, University of California, Berkeley (invited). "Inferring high-redshift large-scale structure dynamics from the Lyman-alpha forest"​​

  • 2018 October, Institute Henri Poincare, Paris. "Inferring high-redshift large-scale structure dynamics from the Lyman-alpha forest"​​.

  • 2018 September, Max-Planck Institue for Astrophysics, Garching. "Inferring high-redshift large-scale structure dynamics from the Lyman-alpha forest"​​

  • 2018 September, Oskar Klein Center, University of Stockholm. "Inferring high-redshift large-scale structure dynamics from the Lyman-alpha forest"​

  • 2018 July, Summer school on large-scale structure, Berlin. Poster: "Bayesian insights into the Lyman alpha forest"​

  • 2017 November, Institut d'Astrophysique de Paris (IAP). "Imprints of the large-scale structure on AGN formation and evolution"​

  • 2017 October, Excellence Cluster Universe, Garching. "Cosmic expansion history from SNe Ia data via information field theory"​

 

Aquila Consortium

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

You can find more information about our science in here.

 
 

Publications

Contact

Blackett Laboratory
Imperial College London,
London SW7 2AZ