CosmoSClub: 13-04-2018

Date: April 13th 2018

Speaker: Benjamin Joachimi (University College London)

Title: Cosmic shear cosmology - where we stand


I will review the recent weak lensing cosmology results obtained by the ESO Kilo-Degree Survey, which display an intriguing, marginal discrepancy with the primary Planck CMB constraints on structure growth. Key analysis choices and challenges will be highlighted, and new approaches to validating the measurements presented. I will also briefly discuss the relation to the Dark Energy Survey Year 1 results and some lessons learnt for the forthcoming generation of cosmological galaxy surveys.


CosmoSClub: 05-04-2018

Date: April 5th 2018

Speaker: Elena Sellentin (University of Geneva)

Title: The skewed weak lensing likelihood: why biases arise, despite data and theory being sound


We derive the essentials of the skewed weak lensing likelihood via a simple Hierarchical Model. Our likelihood passes four objective and cosmology-independent tests which a standard Gaussian likelihood fails. We demonstrate that sound weak lensing analyses are naturally biased low, and this does not indicate any new physics such as deviations from ΛCDM. Mathematically, the biases arise because noisy two-point functions follow skewed distributions. This form of bias is already known from CMB analyses, where the low multipoles have asymmetric error bars. Weak lensing is more strongly affected by this asymmetry as galaxies form a discrete set of shear tracer particles, in contrast to a smooth shear field. We demonstrate that the biases can be up to 30 percent of the standard deviation per data point, dependent on the properties of the weak lensing survey. Our likelihood provides a versatile framework with which to address this bias in future weak lensing analyses.


CosmoSClub: 28-03-2018

Date: March 28th 2018

Speaker: Anais Moller (Australian National University)

Title: The next-generation of type Ia supernovae Dark Energy studies


Currently Dark Energy studies with type Ia supernovae rely on a spectroscopically classified sample. The Dark Energy Survey (DES) is entering its last year of observations and a first cosmological analysis with the spectroscopically confirmed supernova sample is on the way. However, present and future surveys as DES and LSST will do cosmological analysis with a photometrically classified type Ia supernova sample. For this, a reliable photometric classification is necessary, which can process large number of candidates and obtain a high-purity sample. 
In this talk, I will first present preliminary cosmological parameter constraints from the first 3-years of the DES supernova survey. The sample is composed by  251 spectroscopically confirmed Type Ia Supernovae (0.02 < z < 0.9) discovered during the first 3 years of the Dark Energy Survey Supernova Program. I will also discuss about the future analysis with the DES 5-year photometric supernova sample. In particular, I will discuss a photometric classification method based on recurrent neural networks that can classify quickly large number of supernovae with high accuracy using only photometric measurements and time as input. This method includes a bayesian interpretation of classification probabilities which will be fundamental for a cosmology analysis. In addition, this method also classifies partial light-curves with high accuracy and speed which will allow to distribute resources towards promising candidates and can be applied to other transients.

CosmoSClub: 07-03-2018

Date: March 7th 2018

Speaker: Nicolas Martinet (Universität Bonn)

Title: Cosmological constraints from shear peaks in the Kilo Degree Survey (KiDS)


The peak statistic in weak lensing reconstructed mass maps is a new powerful tool to probe cosmology. Peaks trace the Universe large scale structure, but on the contrary to the two-point correlation function of the shear, they are also sensitive to the non-Gaussian part of the matter distribution. In that sense they are similar to galaxy clusters. In this talk, I will introduce weak lensing peak statistics and review its application to the Kilo Degree Survey (Martinet, Schneider, Hildebrandt et al. 2018, MNRAS 474, 712). I will in particular discuss how combining shear peaks with the two-point correlation function of the shear can improve cosmological constraints.



CosmoSClub: 01-02-2018

Date: February 1st 2018

Speaker: Sandrine Codis (IAP)

Title: Intrinsic alignments : theoretical and numerical insights


In this talk, I will show how both dark matter and hydrodynamical simulations predict that the morphology of galaxies is correlated with the cosmic web. This large-scale coherence of galaxy shapes could possibly induce some non-negligible level of contamination for future cosmic shear experiments. Because this effect is very sensitive to the small scale baryonic physics, it is difficult to use dark matter-only simulations as the sole resource to predict and control intrinsic alignments. I will show how state-of-the-art hydrodynamical simulations like Horizon-AGN can be used to shed light on the level of intrinsic alignment we should expect for future weak lensing measurements.  On the theoretical side, I will describe an analytical Lagrangian model that reproduces qualitatively the correlations between the intrinsic angular momentum of galaxies and the cosmic filaments. The key ingredient is to take into account the anisotropy of the cosmic web in the standard theory of spin acquisition by tidal torquing.


CosmoClub: 17-01-2018

Date: January 17th 2018

Speaker: François Orieux (L2S, Centrale-Supélec)

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


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.