Department Events


Cosmology Seminar

Tue, Jan 23, 2018

 

Note two talks, two locations:


1:10 pm (Cosmology/ BCCP) 
Chihway Chang, Chicago 
Campbell 131 
Mapping the Cosmos with the Dark Energy Survey 
The first year data from the Dark Energy Survey (DES Y1) provides the most powerful optical survey dataset to date. In this talk I will first give an overall summary of the cosmology results from the DES Y1 dataset combining galaxy clustering and weak gravitational lensing. Next, I will describe our work in generating and testing the wide-field weak lensing mass maps from the galaxy shape measurements and some exciting applications for the maps. I will end with thoughts on how weak lensing could also inform us on various topics of galaxy formation, which is essential for completing the story behind the Universe we see today.


4:00 pm (RPM) 
Chihway Chang, Chicago 
LBL 50-5132 
Cosmic Surveys in the Next Decade: Mapping the Landscape of the Universe 
Cosmology in the next decade will be driven by data. Exploiting the information one can extract from the ongoing and upcoming large surveys will give us the power to stress-test the LCDM model with unprecedented precision and open up windows for new physics. In this talk I will present some of our work in the Dark Energy Survey Collaboration and the Large Synoptics Survey Telescope Dark Energy Science Collaboration, to analyse state-of-the-art galaxy survey data as well as getting ready for the next generation of data. I will focus on topics surrounding weak lensing analyses, including cosmology from 2-point functions, generating weak lensing mass maps, and measuring the mass profiles at the outskirts of galaxy clusters.

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Colloquium

Thu, Jan 18, 2018

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Lunch Talk

Thu, Jan 18, 2018

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Cosmology Seminar

Tue, Jan 16, 2018

1:10 pm (Cosmology/ BCCP) 
Francois Lanusse, CMU 
Campbell 131 

Facing the challenges of modern cosmological surveys with deep learning 
The upcoming generation of cosmological surveys such as LSST or DESI will aim at shedding some much needed light on the physical nature of dark energy and dark matter by mapping the Universe in great detail and on an unprecedented scale. While this implies a great potential for discoveries, it also involves new and outstanding challenges at every step of the science analysis, from image processing to the modeling of astrophysical systematics. In this talk I will illustrate how recent advances in Deep Learning open new perspectives for addressing some of theses challenges and for exploiting this wealth of data in new and exciting ways. As a first example, I will present our work on automated strong gravitational lens detection, a problem where deep learning essential eliminates the need for human visual inspection (which would have intractable at the scale of LSST). In a second example of applications, I will illustrate how data driven deep generative models can be used to complement a physical modeling in two different cases: image simulations with realistic galaxy morphologies for the calibration of weak lensing shape measurement algorithms, and the production of mock galaxy catalogs with realistic intrinsic alignments learned from hydrodynamical simulations.

 

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