Projects | Linda Blot

Nefertiti: a cosmological simulation code for clustering dark energy scenarios

Tue, 11 Jul 2023 14:25:22 +0900

Euclid Flagship mock galaxy catalogue

Thu, 22 Nov 2018 22:44:24 +0100

The cosmological analysis of galaxy survey data requires modelling the large scale distribution of galaxies in synthetic realisations of the sky that are usually referred to as mock galaxy catalogues. These provide a controlled environment in which to test the impact of different aspects of the survey and allow to study the interplay of observational and statistical errors. For this reason they need to cover the same volume of the survey and to be statistically compatible with observed galaxy catalogues.

During my position at the Institute of Space Sciences in Spain I developed algorithms to generate such catalogues, building upon the expertise of the members of the MICE collaboration. We worked in close collaboration with PIC, the Spanish Euclid Data Centre, where a dedicated Big Data platform was installed, and produced a pipeline that is able to consistently generate dozens of observed properties for extremely large volumes in a timescale of a few hours ( Carretero et al. 2017). This represented a significant improvement compared to previous implementations that required several days to be completed and were not automated, making them more prone to human errors. Being able to produce several iterations of the mocks in the span of a few days will also be crucial for the analysis of Euclid data.

This pipeline was first deployed to generate the Euclid Flagship galaxy mock, the largest ever produced, with 2.6 billion objects, more than 100 galaxy properties and covering a redshift range up to z=2.3 ( Euclid Collaboration: Castander et al. 2024). Our team won the 2018 Euclid STAR Prize for this work. My role in this team has been the re-factoring of the code to take advantage of the Big Data platform both in the calibration and production phases.

Covariance matrices from large ensembles of simulations

Thu, 22 Nov 2018 22:33:04 +0100

In order to extract the maximum amount of cosmological information from large scale structure surveys we need to make sure that we are able to model cosmological observables and their statistical errors at this unprecedented level of accuracy on the smallest scales that will be accessible to these surveys. This is where cosmological simulations are indispensable, since these scales are in the non-linear regime of gravitational collapse of cosmic structures, which cannot be modelled with analytical techniques. My work focused on estimating covariance matrices for large scale structure observables, which encode their statistical errors, using large ensembles of simulations.

The main cosmological observables of large galaxy surveys aim at indirectly measuring the underlying matter power spectrum, a second-order statistic that contains most of the cosmological information. This is why part of my research work focused on this quantity. In particular I have worked on the estimation of covariances from DEUS PUR, an ensemble of more than 12000 N-body simulations, exploring the effect of numerical systematics due to the limited mass resolution of the simulations ( Blot et al. 2015), the effect of non-linearities on the probability distribution function of the matter power spectrum and sample covariance estimators ( Blot et al. 2015 & 2016) and the information content of the power spectrum when combined with the third-order statistic: the bispectrum ( Chan & Blot 2017).

The DEUS PUR project produced the largest ensemble of simulations at the time of realisation, providing an invaluable resource for statistical studies of the properties of the dark matter density field in the non-linear regime. It was made possible by a Grand Challenge project at the IDRIS supercomputing centre. I was involved in all the stages of the project, from the production of the simulations to the subsequent analysis and interpretation of the results. The covariance matrices computed from these simulations are publicly available and have been used as a benchmark in numerous works aimed at predicting the covariance matrix from analytical methods.

Having such a large number of simulations allowed us to detect subtle effects that were previously undiscovered, such as the non-gaussianity of the distribution of the power spectrum estimator on non-linear scales ( Blot et al. 2015) and the impact of such non-gaussianity on the distribution of the sample covariance estimator ( Blot et al. 2016).