Deep Learning on the Sphere and Reconstruction of Dark Matter Mass Maps
Candidature avant le
03/05/2020
Durée
6 mois
Poursuite possible en thèse
oui
Contact
STARCK Jean-luc
+33 1 69 08 57 64
Autre lien
Résumé/Summary
Le sujet du stage consiste à developper une méthode de reconstruction de carte de matière noire pour traiter des donner sur la sphere, en utilisant une méthodologie Deep Learning.
The project consists in developing a mass mapping method for data on the sphere, using deep learning techniques,.
Sujet détaillé/Full description
The Euclid satellite, to be launched in 2022, will observe the sky in the optical and infrared, and will
be able to map large scale structures and weak lensing distortions out to high redshifts. Weak gravitational
lensing is thought to be one of the most promising tools of cosmology to constrain models. Weak lensing
probes the evolution of dark-matter structures and can help distinguish between dark energy and models of
modified gravity. Thanks to the shear measurements, we will be able to reconstruct a dark matter mass map of
15000 square degrees. Mass mapping entails the construction of two-dimensional maps using galaxy shape
measurements, which represent the integrated total matter density along the line of sight. Small- field mass
maps have been frequently used to study the structure and mass distribution of galaxy clusters, whereas widefield maps have only more recently become possible given the broad observing strategies of surveys like
CFHTLenS, HSC, DES, and KiDS. Mass maps contain significant non-Gaussian cosmological information
and can be used to identify massive clusters as well as to cross-correlate the lensing signal with foreground
structures.
A standard method to derive mass maps from weak-lensing observations is an inversion technique formulated
by Kaiser & Squires [2]. It has many limitations, however, including the need to smooth the data before (and
often after) inversion, thereby losing small-scale information. An alternative method called GLIMPSE has
been developed in the CosmoStat laboratory based on sparse reconstruction that avoids this problem and
improves the recovery of non-Gaussian features [3, 4]. The algorithm has been tested on simulations and was
also recently used to study the A520 merging galaxy cluster with Hubble Space Telescope data [5]. More
recently, machine learning has emerged as a promising technique for mass map recovery [6].
The goal of this project is to first compare this technique to the state of the art and extend the method for
spherical data.
References
1. Bartelmann, M. & Schneider, P. 2001, Phys. Rep., 340, 291.
2. Kaiser, N. & Squires, G. 1993, ApJ, 404, 441.
3. Leonard, A., Lanusse, F., & Starck, J.-L. 2014, MNRAS, 440, 1281.
4. Lanusse, F., Starck, J.-L., Leonard, A., & Pires, S. 2016, A&A, 591, A2.
5. Peel, A., Lanusse, F., & Starck, J.-L. 2017, ApJ, 847, 23.
6. Niall Jeffrey et al, submitted. https://arxiv.org/abs/1908.00543
be able to map large scale structures and weak lensing distortions out to high redshifts. Weak gravitational
lensing is thought to be one of the most promising tools of cosmology to constrain models. Weak lensing
probes the evolution of dark-matter structures and can help distinguish between dark energy and models of
modified gravity. Thanks to the shear measurements, we will be able to reconstruct a dark matter mass map of
15000 square degrees. Mass mapping entails the construction of two-dimensional maps using galaxy shape
measurements, which represent the integrated total matter density along the line of sight. Small- field mass
maps have been frequently used to study the structure and mass distribution of galaxy clusters, whereas widefield maps have only more recently become possible given the broad observing strategies of surveys like
CFHTLenS, HSC, DES, and KiDS. Mass maps contain significant non-Gaussian cosmological information
and can be used to identify massive clusters as well as to cross-correlate the lensing signal with foreground
structures.
A standard method to derive mass maps from weak-lensing observations is an inversion technique formulated
by Kaiser & Squires [2]. It has many limitations, however, including the need to smooth the data before (and
often after) inversion, thereby losing small-scale information. An alternative method called GLIMPSE has
been developed in the CosmoStat laboratory based on sparse reconstruction that avoids this problem and
improves the recovery of non-Gaussian features [3, 4]. The algorithm has been tested on simulations and was
also recently used to study the A520 merging galaxy cluster with Hubble Space Telescope data [5]. More
recently, machine learning has emerged as a promising technique for mass map recovery [6].
The goal of this project is to first compare this technique to the state of the art and extend the method for
spherical data.
References
1. Bartelmann, M. & Schneider, P. 2001, Phys. Rep., 340, 291.
2. Kaiser, N. & Squires, G. 1993, ApJ, 404, 441.
3. Leonard, A., Lanusse, F., & Starck, J.-L. 2014, MNRAS, 440, 1281.
4. Lanusse, F., Starck, J.-L., Leonard, A., & Pires, S. 2016, A&A, 591, A2.
5. Peel, A., Lanusse, F., & Starck, J.-L. 2017, ApJ, 847, 23.
6. Niall Jeffrey et al, submitted. https://arxiv.org/abs/1908.00543
Mots clés/Keywords
machine learning
Compétences/Skills
Tensor flow, python
Logiciels
Tensor flow, python
