Natalia Porqueres
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 MaxPlanck Institute for Astrophysics (MPA), supervised by Jens Jasche and Torsten Ensslin.
My work focuses on understanding the largescale structures of the Universe. I develop statistical and data analysis tools to study the formation and dynamics of cosmic largescale structures.
Research
Bayesian forward modelling of cosmic shear data
The nextgeneration 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 fieldbased 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 twopoint statistics, exploiting the full information content of the shear fields.
Largescale structure inference from the Lymanα forest
Most of the analyses of the cosmic largescale structure are based on galaxy observations. However, galaxies mostly trace the overdense regions of the Universe. A complementary probe of the largescale structure is the Lymanα forest, which sensitive to the lowdensity regimes. Also, by probing small scales down to 1 Mpc, the Lymanα forest is sensitive to neutrino masses and dark matter models.
A usual approach to extract information from the Lyα forest is to analyse the power spectrum. However, the nonlinear dynamics transport the information from the twopoint statistics to highorder correlations.
I developed a Bayesian framework to infer the 3D dark matter density and its dynamics from the Lymanα forest. This method employs a physical forward model that encodes the highorder correlations of the dark matter density.
Robust data models for contaminated datasets
Nextgeneration 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 SDSSIII data in Lavaux, Jasche & Leclerqc (2019), recovering the unbiased power spectrum even at large scales (k<0.01) where the stateoftheart 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 Xray, gammarays, 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.

Bayesian forward modelling of cosmic shear data. NP, Heavens, Mortlock, Lavaux (Accepted in MNRAS, 2021).

A hierarchical fieldlevel inference approach to reconstruction from sparse Lymanalpha forest data. NP, Hahn, Jasche, Lavaux (A&A, 2020)

Inferring high redshift largescale structure dynamics from the Lymanalpha forest. NP, Jasche, Lavaux, Ensslin (A&A, 2019).

Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys. NP, Kodi Ramanah, Jasche, Lavaux (A&A, 2019).

Imprints of the largescale structure on AGN formation and evolution. NP, Jasche, Ensslin, Lavaux (A&A, 2018).

NIFTy 3  Numerical Information Field Theory. Steiniger et al. (Annalen der Physik, 2018)

Cosmic expansion history from SNe Ia data via information field theory. NP, Ensslin, Greiner, Boehm, et al. (A&A, 2017)
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 fieldbased approach to inference from Lyman alpha forest data".

2020 May, Queen Mary University, London (invited). "Inferring the dynamics of cosmic structures from the Lymanalpha forest".

2020 February, Laboratoire Lagrange, Nice (invited). "Inferring the dynamics of cosmic structures at highredshift".

2020 February, University College of London (UCL), London (invited). "Inferring the dynamics of cosmic structures at highredshift from the Lyalpha forest".

2020 January, The Cosmic Web in the Local Universe Conference, Lorentz Center, Leiden (invited). "Inferring the dynamics of cosmic structures at highredshift from the Lyalpha 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 highredshift from the Lymanalpha forest".

2019 November, Institut de ciències del cosmos (ICCUB), Universitat de Barcelona. "Inferring the dynamics of cosmic structures at highredshift".

2019 October, Imperial College London. "Inferring the dynamics of cosmic structures at highredshift".

2018 November, CCA Flatiron Institute, New York (invited). "Inferring highredshift largescale structure dynamics from the Lymanalpha forest"

2018 November, University of California, Berkeley (invited). "Inferring highredshift largescale structure dynamics from the Lymanalpha forest"

2018 October, Institute Henri Poincare, Paris. "Inferring highredshift largescale structure dynamics from the Lymanalpha forest".

2018 September, MaxPlanck Institue for Astrophysics, Garching. "Inferring highredshift largescale structure dynamics from the Lymanalpha forest"

2018 September, Oskar Klein Center, University of Stockholm. "Inferring highredshift largescale structure dynamics from the Lymanalpha forest"

2018 July, Summer school on largescale structure, Berlin. Poster: "Bayesian insights into the Lyman alpha forest"

2017 November, Institut d'Astrophysique de Paris (IAP). "Imprints of the largescale 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
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