4 sujets IRFU/DEDIP

Dernière mise à jour : 08-08-2020


• Astrophysics

• Mathematics - Numerical analysis - Simulation

• Particle physics

 

Galaxy Cluster detection with Weak Lensing: Towards a Euclid Weak Lensing-selected galaxy cluster catalogue

SL-DRF-20-0566

Research field : Astrophysics
Location :

Département d’Electronique, des Détecteurs et d’Informatique pour la physique (DEDIP)

Laboratoire de cosmologie et statistiques (LCS)

Saclay

Contact :

Sandrine Pires

Gabriel Pratt

Starting date : 01-10-2010

Contact :

Sandrine Pires
CEA - DRF/IRFU/DEDIP/LCS

01 69 08 92 63

Thesis supervisor :

Gabriel Pratt
CEA - DRF/IRFU/DAP/LCEG

0169084706

Personal web page : http://www.cosmostat.org/people/sandrine-pires

Laboratory link : http://irfu.cea.fr/dap/

More : https://www.euclid-ec.org

Euclid is the major cosmological mission led by the European Space Agency, planned to be launched in 2022. The AIM laboratory, initiator of the project, occupies a number of key Euclid positions in management, instrumental development, ground segment and related science. The sensitivity of Euclid should allow blind detection of clusters through their lensing signal i.e. directly through their total projected mass. Combined with the sky coverage, this will allow the construction of a significant galaxy cluster catalogue that is for the first time truly representative of the true cluster population. Indeed, up to now all galaxy cluster catalogues rely on detection through their baryonic signal (e.g. through the intra-cluster gas content in X-rays and the Sunyaev-Zeldovich effect (SZE) at millimetre wavelengths, or through the optical light in the galaxies). This will provide new constraints on galaxy cluster abundances in the Universe, which has important implications for cosmology. In this context, AIM is also deeply involved in the ongoing CFIS survey (PI: Jean-Charles Cuillandre) that has to provide some of the ground-based data necessary for the Euclid mission. CFIS data in hand are sufficient for testing blind detection of clusters through lensing signal particularly at high masses. AIM is also deeply involved in the ongoing XMM-Heritage project (PI: Monique Arnaud) that is a multi-year programme to obtain X-ray observations with XMM-Newton of 118 SZ-selected galaxy clusters at 0.05 < z < 0.6. A key project goal is to obtain X-ray data with homogeneous quality for the first time for such a large number of objects. Crucially, object selection was tailored specifically to the CFIS and Euclid survey areas, allowing comparison of clusters detection methods through both dark and baryonic signals. The thesis project takes place in this stimulating context and aims at exploring innovative methods to build a weak lensing selected galaxy cluster catalogue. This will be undertaken using initially numerical simulations, and subsequently CFIS data. Scientific exploitation will be enhanced by combining the resulting catalogue with the XMM-Heritage sample. The ultimate goal will be to apply the methods to Euclid.
Microcalorimeter with high resistivity transition edge sensors (TES) for X-ray spectro-imagers for spatial astrophysics, and development of the associated multiplexing cryogenic microelectronics

SL-DRF-20-0664

Research field : Astrophysics
Location :

Département d’Electronique, des Détecteurs et d’Informatique pour la physique (DEDIP)

Laboratoire d’Intégration des Systèmes Electroniques de Traitement et d’Acquisition

Saclay

Contact :

Xavier de la BROÏSE

Jean-Luc SAUVAGEOT

Starting date : 01-10-2020

Contact :

Xavier de la BROÏSE
CEA - DSM/IRFU/SEDI/LISETA

0169084093

Thesis supervisor :

Jean-Luc SAUVAGEOT
CEA - DRF/IRFU/DAP/LSIS

0169088052

Laboratory link : irfu.cea.fr

Astrophysical research requires the development of very high performance cameras embedded in space observatories. The observation of the universe in the X-ray range (X-ray spectro-imagery) needs detectors made of matrices of micro-calorimeters operating at very low temperature (50 mK). The absorption by the detector of an X-ray photon coming from the observed celestial object causes a micro-rise in the temperature of the detector. The measurement of this temperature rise, which makes possible to determine the energy of the photon, requires ultra-sensitive micro-thermometers, and a cryogenic electronics, with very low noise, capable of reading them.

Two technologies of thermometers have been used so far : high-impedance silicon-doped metal insulator sensors (MIS), and very low impedance transition edge sensors (TES). Each requires a very specific electronics, either based on HEMT transistors for adapting to high impedances, or based on SQUIDs for adapting to very low impedances. The high impedances have the advantage of an extremely reduced heat dissipation on the detection stage, which allows a large number of pixels, while the very low impedance TES, more sensitive than the MIS, make easier to obtain excellent spectral resolutions.

A few years ago, a new type of thermometer has been developed by the CNRS/CSNSM : this is high impedance TES, potentially allowing to combine the advantages of one and the other types of detectors. A first thesis was carried out in our laboratory (2016 - 2019), with the aim of evaluating this new path by implementing it for the first time, and by associating it with an innovative readout electronics architecture that performs an active electro-thermal feedback. This thesis has highlighted the extremely promising nature of the device, by obtaining very interesting first experimental measurements.

The objective of the new thesis, proposed here, is to continue this exploratory work by going one new major stage further : validate from this new technology the feasibility of a matrix of several thousand pixels. For this, the work will focus on two parallel axes : on the one hand carry out a complete work of improvement and optimization, in order to draw from the device its best performances, and on the other hand design and test the integrated electronic system (ASIC) essential for the realization of the future large matrices.

The main difficulty lies in the conditions of implementation of the system : the detector must be placed in a cryo-generator to be cooled to very low temperature (50 mK), and equipped with a cryogenic electronics, to be designed, operating at 4 K. This one will have to ensure not only the multiplexing and the amplification of the signal but also, despite this multiplexing, the maintenance of the active electro-thermal feedback of the detectors, and this while satisfying the extremely severe noise and thermal dissipation constraints required by space cryogenics.

Sparse spectral unmixing for the spatial and temporal data fusion in gamma spectrometry

SL-DRF-20-1012

Research field : Mathematics - Numerical analysis - Simulation
Location :

Département d’Electronique, des Détecteurs et d’Informatique pour la physique (DEDIP)

Laboratoire de cosmologie et statistiques (LCS)

Saclay

Contact :

Jérôme Bobin

Starting date : 01-10-2020

Contact :

Jérôme Bobin
CEA - DRF/IRFU/SEDI/LCS

0169084463

Thesis supervisor :

Jérôme Bobin
CEA - DRF/IRFU/SEDI/LCS

0169084463

Personal web page : http://jbobin.cosmostat.org

Laboratory link : http://www.cosmostat.org

The general objective of the proposed thesis is the development of new algorithms for data analysis in gamma spectrometry allowing the joint processing of multiple data, taking into account both spatial and temporal information. To this end, the thesis will be composed of following three stages: i) Joint analysis of spectrometric data of measurements made on different aerosol sampling stations distributed in France, using a joint sparse modeling of the spectra to be analyzed in order to take into account the correlations between the measurements. These developments will be tested on measurements made during European-wide events of abnormal detection of radionuclides in the air (I-131, Ru-106, Se-75 ...), ii) Temporal fusion: joint processing of successive spectrometric data making it possible to use the a priori knowledge of the decay of radionuclides. The previously developed method will be extended to the case of unmixing with energy and time signatures in order to allow early detection of anomalies on continuous measurements. This approach will be tested on continuous measurements of aerosol sampling filters collected at Orsay, iii) Spatial and temporal fusion: joint processing of spectrometric data in situ continuously. In this case, an approach based on statistical learning, in particular via the use of recurrent networks (reproducing the inversion process of classic optimization algorithms in order to learn the regularization term from a training set data) will be implemented to capture time dependencies of background noise.
Advanced artificial intelligence techniques for the event filtering at the CMS detector

SL-DRF-20-1004

Research field : Particle physics
Location :

Département d’Electronique, des Détecteurs et d’Informatique pour la physique (DEDIP)

Systèmes Temps Réel, Electronique d’Acquisition et Microélectronique

Saclay

Contact :

Mehmet Ozgur SAHIN

Fabrice COUDERC

Starting date : 01-10-2020

Contact :

Mehmet Ozgur SAHIN
CEA - DRF/IRFU/DEDIP/STREAM

01 69 08 14 67

Thesis supervisor :

Fabrice COUDERC
CEA - DRF/IRFU/DPHP/CMS

01 69 08 86 83

After a very successful operation period crowned with the discovery of the Higgs boson, the Large Hadron Collider will undergo a luminosity upgrade where it is planned to increase the collision rate by a factor of ten. The CMS detector will also be upgraded to cope with these challenging environment and to enable a better event reconstruction, particularly with the new high-granularity calorimeters. Collecting, filtering and processing the data from these detectors will pose a significant challenge. In order to make the most out of it, the modern artificial intelligence techniques will be essential. We are looking for an enthusiastic student who will study the possible machine learning techniques that can be implemented in the Field Programable Gate Arrays (FPGA) to enable very high-speed reconstruction and filtering of this immense amount of data.



 

Retour en haut