CosmoStat_Twitter_Profile

CosmoClub: 15-11-2017

Date: November 15th 2017

Speaker: Eleonora Villa (SISSA)

Title: Theoretical systematics in galaxy clustering in LCDM and beyond


Abstract

We study the impact of neglecting lensing magnification in galaxy clustering analyses for future galaxy surveys, considering the ΛCDM model and two extensions: massive neutrinos and modifications of General Relativity. Our study focuses on the biases on the constraints and on the estimation of the cosmological parameters. Our results show that the information present in the lensing contribution does improve the constraints on the modified gravity parameters whereas the lensing constraining power is negligible on the ΛCDM parameters. On the other hand the estimation is biased for all the parameters if lensing is not taken into account.
This effect is particularly significant for the modified gravity parameters. Our findings show the importance of including lensing in galaxy clustering analyses for testing General Relativity.

CosmoStat_Twitter_Profile

CosmoClub: 08-11-2017

Date: November 8th 2017

Speaker: François Orieux (L2S, Centrale-Supélec)

Title: Semi-unsupervised Bayesian Convex Image Restoration with Location Mixture of Gaussian


Abstract

Convex image restoration is a major field in inverse problems. The problem is often addressed by hand-tuning hyper-parameters. We propose an incremental contribution about a Bayesian approach where a convex field is constructed via Location Mixture of Gaussian and the estimator computed with a fast MCMC algorithm. Main contributions are a new field with several operator avoiding crosslike artifacts and a fallback sampling algorithm to prevent numerical errors. Results, in comparison to standard supervised results, have equivalent quality in a quasi-unsupervised approach and go with uncertainty quantification.

CosmoStat_Twitter_Profile

CosmoClub: 19-10-2017

Date: October 19th 2017

Speaker: François Lanusse (Carnegie Mellon University)

Title: Towards a better control of weak lensing systematics in the LSST era using Deep Learning


Abstract

In this talk, I will present several applications of the most recent advances made in the field of Deep Learning to some of the main sources of systematics affecting the weak lensing probe in next generation wide field cosmological surveys.
Most current shape measurement methods used for weak lensing will require precise calibration to reach their science requirements. This calibration step is typically performed through extensive galaxy image simulations. I will present an application of Deep Generative Models, based on variational inference, aiming at producing realistic galaxy images with complex morphologies which can be used as inputs of the image simulation pipeline instead of scarce and expensive space-based observations.
A second important source of systematics comes from uncertainties in photometric redshift estimation. Most current techniques rely solely on color measurements to build a redshift estimation. I will present an extension of these ideas but based on using multi-band galaxy images as input of the
supervised regression problem. The proposed architecture, based on Deep Residual Networks (resnet), is able to complement color information with morphology information in order to improve the accuracy of the redshift estimate.
Finally, another major potential source of systematics comes from intrinsic galaxy alignments (IA). While hydrodynamical simulations can reproduce to some extent these alignments, they are limited to fairly small cosmological volumes. However, to properly test IA marginalisation schemes at the scale of
LSST or Euclid, much larger simulation volumes are necessary. I will present an application Graph Convolutional Networks which allows us to model the IA signal on a graph of the cosmic web. This method allows us to populate simpler Dark Matter only N-body simulations of much larger volumes with realistic galaxy alignments.

CosmoStat_Twitter_Profile

CosmoClub: 06-10-2017

Date: October 6th 2017

Speaker: Daniela Saadeh (University of Nottingham)

Title: How isotropic is the Universe?


Abstract

A fundamental assumption of the standard model of cosmology is that the large-scale Universe is isotropic. Because of its centrality, it is essential to test this assumption. Breaking isotropy leads to Bianchi cosmologies, a set of solutions to the Einstein field equations of which only the subset describing rotating universes was previously tested against data.
In this talk, I present a general test of isotropy considering, for the first time, all the degrees of freedom of anisotropic expansion. We analyse cosmic microwave background data from Planck, carrying out the first joint analysis of temperature and polarization data for this purpose. We also show that improved constraints on anisotropy may be obtained by extending the likelihood to high ell.
For the vector mode (associated with rotating universes), we obtain a limit on the anisotropic expansion that is an order of magnitude tighter than previous Planck results using the CMB temperature only. We recover upper limits for all the other modes, with the weakest one arising from the regular tensor modes. We disfavour anisotropic expansion of the Universe with odds of 121,000:1 against.

CosmoStat_Twitter_Profile

CosmoClub: 06-04-2017

Date: April 6th 2017

Speaker: Julie Josse (Ecole Polytechnique)

Title: Missing data imputation using principal component methods


Abstract

Missing values are ubiquitous and can occur for plenty of reasons: machines that fail, survey participants who do not answer to all questions, etc. The problem of missing values is somehow exacerbated with the amount of available data: data are often multisources (several projects aim to build large repositories by compiling data from preexisting databases) and due to the wide heterogeneity of measurement methods and research objectives, these large databases often exhibit extraordinarily high number of missing values. Missing values are problematic since most statistical methods can not be applied directly on a incomplete data.
In this talk, I will present recent tools developed to handle, in a practicable way, missing values. Among them, we can note the treatment of heterogeneous data (both quantitative and categorical) as well as the possibility to go far beyond single estimations and suggest subtle ways of assessing uncertainties. I will discuss imputation methods based on (regularized) singular value decomposition that caught the attention of the community due to their ability to handle large matrices with large amount of missing entries. Then, I will show how to extend these methods to multiple imputation to get notions of confidence intervals to know which credit should be given to analyses obtained from an incomplete data set. Such multiple imputation methods also offer new ways to visualize the variability of the results due to missing values.

CosmoStat_Twitter_Profile

CosmoClub: 30-03-2017

Date: March 30th 2017

Speaker: Matthieu Simeoni (EPFL)

Title: Bluebild: a Stable, Accurate and Efficient Imager for Radio Astronomy


Abstract

Radio astronomy imaging has been primarily focused on a planar approximation to a portion of the observed sphere, producing images of a fixed resolution. Historically, Fourier analysis played a pivotal role, with algorithmic modifications made to fit that paradigm. It was thought that spherical calculation was computationally impractical, and that inherent numerical instability meant only a dirty image (a very rough least-squares approximation) could be made. The computational and energy demands of instruments such as the planned Square Kilometre Array (SKA) have made a new approach imperative.
Here we present an efficient algorithm called Bluebild, that reconstructs directly on the celestial sphere, producing, for the first time, a true least-square estimate of the sky. Wide-field and flexible beamformed imaging follow naturally. It produces a continuous image description that may be stored independently of resolution, and sampled up to the fundamental telescope limit. A multi-scale sky decomposition becomes an intrinsic part of the process, and algorithmic linearity permits uncertainty assessment across the chain. The algorithm is fast, far simpler and more intuitive than previous methods. We show sky images produced are more accurate, and can be analysed in much more depth. Results with real LOFAR data will be presented.

CosmoStat_Twitter_Profile

CosmoClub: 27-03-2017

Date: March 27th 2017

Speaker: Francois Meyer (University of Colorado, Boulder/INRIA)

Title: Detecting Structural  Changes in Dynamic Community Networks


Abstract

The study of time-varying (dynamic) networks (graphs) is of fundamental importance for computer network analytics. Several methods have been proposed to detect the effect of significant structural changes in a time series of graphs.
The main contribution of this work is a detailed analysis of a dynamic community graph. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. The goal of the work is to detect the time at which the graph dynamics switches from a normal evolution -- where balanced communities grow at the same rate -- to an abnormal behavior -- where communities start merging.
In order to circumvent the problem of identifying the communities, we use a metric to quantify structural changes as a function of time.  The detection of anomalies becomes one of testing the hypothesis that the graph is undergoing a significant structural change.
This is work in collaboration with Peter Wills.

CosmoStat_Twitter_Profile

CosmoClub: 17-02-2017

Date: February 17th 2017

Speaker: Vivien Scottez (IAP)

Title: Clustering-based Redshift estimation


Abstract

The clustering of galaxies has emerged as a powerful way to estimate the redshift distribution of a sample. I will briefly review how we can get access to redshift information from the crosscorrelation function. Then, using the MICE2 simulation I will present how we can get individual redshifts for each galaxy as well as the corresponding accuracy.

CosmoStat_Twitter_Profile

CosmoClub: 02-02-2017

Date: February 2nd 2017

Speaker: Virginie Ollier (ENS Cachan/Supélec)

Title: Robust Calibration of Radio Interferometers in Non-Gaussian Environment [slides]


Abstract

The development of new phased array systems in radio astronomy, as the low frequency array (LOFAR) and the square kilometre array (SKA), formed of a large number of small and flexible elementary antennas, has led to significant challenges. Among them, calibration is a crucial step in order to provide meaningful high dynamic range images and is commonly performed under the assumption of Gaussianity of the noise.
Nevertheless,  observations in the context of radio astronomy are known to be affected by the presence of outliers which are due to several causes, e.g., weak non-calibrator sources or man made radio frequency interferences. In order to take into account the outlier effects, the noise  can be assumed to follow a spherically invariant random distribution.
Based on this modeling, a robust calibration algorithm is exposed in this presentation. More precisely, this new scheme is based on the design of an iterative relaxed concentrated maximum likelihood estimation procedure and allows to obtain closed-form expressions for the unknown parameters with a reasonable computational cost.

CosmoStat_Twitter_Profile

CosmoClub: 01-12-2016

Date: December 1st 2017

Speaker: Emille Ishida (Université Clermont-Auvergne)

Title: The Cosmostatistics Initiative (COIN) - reshaping scientific interdisciplinary collaborations [slides]


Abstract

The Cosmostatistics Initiative (COIN) is an international working group built under the auspices of the International Astrostatistics Association (IAA). Its goal is to propose an alternative approach to scientific collaboration while contributing to the establishment of Astrostatistics as a discipline on its own. In this talk  I will describe the motivation and the logistic behind the COIN collaboration, and its Residence Programs, and provide a couple of examples on how interdisciplinary collaboration can shed light on long standing astronomical problems.