CosmosClub: Jean-Baptise Bayle (23/05/2019)

Date: May 23rd 2019, 11am

Speaker: Jean-Baptise Bayle (APC)

Title: Space-based Detection of Gravitational Waves with LISA: Opening the Low-Frequency Part of the Gravitational Spectrum

Room: Cassini


Abstract

Einstein’s theory of General Relativity offers a powerful picture of the world, where the gravitational force is only the consequence of the curvature of spacetime. Gravitational waves are very tiny ripples in the fabric of spacetime, propagating at the speed of light. They do not interact much with the matter encountered on their way, making them very good messengers to probe astrophysical events at a distance... but also very difficult to detect. Scientists have looked for gravitational waves on Earth for more than 40 years and have developed incredibly precise instruments. In 2015, the first direct detection of such waves was announced by the LIGO and Virgo community: gravitational astronomy had opened a new observational window on the distant Universe. Unfortunately the limited sensitivities of such ground-based detectors does not enable the observation of the most massive object.

In two decades, the Laser Interferometer Space Antenna (LISA) mission will join the network of ground detectors. LISA aims to measure gravitational waves in the millihertz range, to help answer numerous astrophysical, cosmological and theoretical questions. Three spacecraft will orbit the Sun in a nearly equilateral triangular formation, with arm lengths of about 2.5 million kilometers. Each spacecraft will host two spacetime probes. Laser beams, exchanged between the spacecraft, will be reflected upon the probes and then made to interfere, in order to detect passing gravitational waves.

During this conversation, I will briefly recap how gravitational waves arise from Einstein equations, and present the astrophysical and cosmological sources expected throughout the gravitational spectrum. I will then describe the main detection principles, from ground-based interferometry to pulsar timing. I will then focus on the LISA mission, and discuss my PhD work on data pre-processing and instrumental modeling.

 

CosmosClub: Niall Jeffrey (25/04/2019)

Date: April 25th 2019, 11am

Speaker: Nial Jeffrey (UCL, currently visiting CosmoStat)

Title: Deep Learning dark matter maps from Dark Energy Survey (DES) weak lensing data

Room: Cassini


Abstract

Reconstructed density fields from weak lensing are rich in information about cosmological parameters and models of the Universe — including a large non-Gaussian component that cannot be accessed using traditional 2-point statistics. I will present a new method based on Deep Learning to reconstruct dark matter maps from weak lensing data with higher accuracy. Weak lensing map reconstruction is “ill-posed”, troubled by survey masks and galaxy “shape noise”. With DES SV data we showed that by implementing physically-motivated priors (Gaussian field or halo model), substantial improvements are made over standard approaches. Such advanced methods are still limited due to their prior distributions; non-linear density fields have no simple closed form that can be used as a prior. Deep Learning methods can directly learn the underlying structure of the signal, noise and mask from realistic simulations. By combining Deep Learning methods with a physically motivated closed-form prior, improved reconstruction is guaranteed.

Journal Club#5: Image reconstruction, H0 measurements and animated objects

CosmosClub: Antoine Labatie (11/04/2019)

Date: April 11th 2019, 11am

Speaker: Antoine Labatie

Title: Characterizing Well-behaved vs. Pathological Deep Neural Networks [paper]

Room: Kepler


Abstract

We introduce a novel approach, requiring only mild assumptions, for the characterization of deep neural networks at initialization. Our approach applies both to fully-connected and convolutional networks and easily incorporates the commonly used techniques of batch normalization and skip-connections. Our key insight is to consider the evolution with depth of statistical moments of signal and noise, thereby characterizing the presence or the absence of pathologies in the hypothesis space encoded by the choice of hyperparameters. We establish: (i) for feedforward networks with and without batch normalization, depth multiplicativity inevitably leads to ill-behaved moments and pathologies; (ii) for residual networks with batch normalization, on the other hand, skip-connections induce power-law rather than exponential behaviour, leading to well-behaved moments and no pathology.

CosmosClub: Adrien Picquenot (14/03/2019)

Date: March 14th 2019, 11am

Speaker: Adrien Picquenot (CEA Saclay)

Title: Applying the GMCA to extended sources in X-Ray Astronomy

Room: Kepler


Abstract

In high-energy astronomy, spectro-imaging instruments such as X-ray detectors allow investigation of the spatial and spectral properties of extended sources including galaxy clusters, galaxies, diffuse interstellar medium, supernova remnants and pulsar wind nebulae. In these sources, each physical component possesses a different spatial and spectral signature, but the components are entangled. Extracting the intrinsic spatial and spectral information of the individual components from this data is a challenging task. Current analysis methods in this field do not fully exploit the 2D-1D (x,y,E) nature of the data, as the spatial and spectral information are considered separately. Here we investigate the application of the GMCA, initially developed to extract an image of the Cosmic Microwave Background from Planck data, in an X-ray context. 
The performance of the GMCA on X-ray data is tested using Monte-Carlo simulations of supernova remnant toy models, designed to represent typical science cases. We find that the GMCA is able to separate highly entangled components in X-ray data even in high contrast scenarios, and can extract with high accuracy the spectrum and map of each physical component. A modification of the algorithm is proposed in order to improve the spectral fidelity in the case of strongly overlapping spatial components, and we investigate a resampling method to derive realistic uncertainties associated to the results of the algorithm. Applying the modified algorithm to the deep Chandra observations of Cassiopeia A, we are able to produce detailed maps of the synchrotron emission at low energies (0.6-2.2 keV), and of the red/blue shifted distributions of a number of elements including  Si and Fe K.
We also tested pGMCA, a new version of the GMCA taking Poisson noise into account, more adapted to the X-ray nature of the data. A first application on the Perseus galaxy cluster shows impressive results, retrieving components that the original GMCA could not find.