CCAPP Seminar: Jose Manuel Zorrilla Matilla (Columbia) 2018 Price Prize Winner

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September 11, 2018
11:30AM - 12:30PM
Location
PRB 4138

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Add to Calendar 2018-09-11 11:30:00 2018-09-11 12:30:00 CCAPP Seminar: Jose Manuel Zorrilla Matilla (Columbia) 2018 Price Prize Winner One of two 2018 Price Prize Winners: Jose Manuel Zorilla Matilla will be presenting: Extracting Cosmological Information from Weak Lensing Surveys Large-scale cosmic structures gravitationally lens the images of background galaxies, distorting their shapes. This weak lensing (WL) signal depends on the growth of the foreground structures and the cosmic expansion history. As a result, it can be used to reconstruct the evolution of the late Universe. Upcoming stage IV experiments such as Euclid, WFIRST or LSST include WL as part of their core mission with the promise to improve constraints in the standard cosmological model by an order of magnitude. It is therefore important to recover all the information encoded in these future datasets. I will review different strategies to do so with emphasis on the use of non-Gaussian statistics. I will also discuss how deep neural networks developed for image analysis can be used to extract information not encoded in two-point, and even higher-order statistics. I will share some encouraging results from proof-of-concept studies, what have we learned in the process and some prospects for the future. price-prize-award PRB 4138 Center for Cosmology and AstroParticle Physics (CCAPP) ccapp@osu.edu America/New_York public
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One of two 2018 Price Prize Winners: Jose Manuel Zorilla Matilla will be presenting: Extracting Cosmological Information from Weak Lensing Surveys

Large-scale cosmic structures gravitationally lens the images of background galaxies, distorting their shapes. This weak lensing (WL) signal depends on the growth of the foreground structures and the cosmic expansion history. As a result, it can be used to reconstruct the evolution of the late Universe. Upcoming stage IV experiments such as Euclid, WFIRST or LSST include WL as part of their core mission with the promise to improve constraints in the standard cosmological model by an order of magnitude. It is therefore important to recover all the information encoded in these future datasets. I will review different strategies to do so with emphasis on the use of non-Gaussian statistics. I will also discuss how deep neural networks developed for image analysis can be used to extract information not encoded in two-point, and even higher-order statistics. I will share some encouraging results from proof-of-concept studies, what have we learned in the process and some prospects for the future.

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