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报告题目:
Non-Negative Matrix Factorization for Self-Calibration of Photometric Redshift Scatter in Weak Lensing Surveys
 报告人:
Le Zhang
SJU
报告时间:
2017-05-18 14:00
报告地点:
South 727, Mong Man-wai Science Technology Building (蒙民伟科技南楼S727)
主办单位:
物理系-天体物理研究所
  简介:

Abstract: In the next generation of proposed weak lensing surveys, it is necessary to calibrate the true redshift of galaxies at the level of 10^{-3} in order to reach their full potential for cosmology. However, the photo-z error, one of the major sources of systematics, can significantly degrade the accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed a self-calibration method to eliminate such error by combining galaxy-galaxy correlations and galaxy-shear correlations between different photo-z bins. But implementing such calibration process suffers a numerical challenge, which requires solving a set of constrained nonlinear matrix equations. In this talk, I will introduce a new algorithm to overcome such numerical problem. Based on the techniques of fixed-point iteration and non-negative matrix factorization, the proposed algorithm can efficiently and robustly reconstruct the scattering probabilities between the true-z and photo-z bins. The algorithm can provide a successful recovery of the scatter rates at the level of 0.01-1%, and the true mean redshifts of photo-z bins at the level of 0.001, which may satisfy the requirements in future lensing surveys.

Bio:

Biographical Sketch:

2015 – present  Distinguished associate researcher at Shanghai Jiao Tong University
2011 – 2015  Research Associate in the Observational Cosmology group at the University of Wisconsin-Madison
2011  Ph.D. in Astroparticle physics, Hamburg University.
2007  M.A. in Theoretical Physics, Shandong University and joint training with NAOC
2004  B.Sc. in Theoretical Physics, Shandong University

Research Interests:
Dark matter signatures in astrophysical and cosmological observations;
Simulation of systematic effects in interferometric observations of the Cosmic Microwave Background (CMB);
Bayesian inference techniques;
Foreground removal in HI 21-cm signal; Blind source separation; Image reconstructions.

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