Monday, March 9th, 2020 @ 12:30pm Where: PAB, 6th Floor, eScience Studio, Seminar Rm.
Optimising Direct Redshift Calibration for Tomographic Cosmic Shear
Comparisons between cosmological parameters from tomographic cosmic shear measurements and the cosmic microwave background reveal some tension between the amount and clustering strength of (predominantly dark) matter. Furthermore, analyses of cosmological weak lensing by different surveys also reveals slight differences in the values of derived parameters. As a result, tomographic weak lensing collaborations have been increasingly focused on the identification and mitigation of systematic biases in their analyses. Arguably the most significant systematic uncertainty arises from the method of estimating tomographic redshift distributions from purely photometric data. In this talk I will explore the methods behind redshift calibration currently being employed by the Kilo Degree Survey (KiDS), discuss the sources of systematic bias in this calibration, and explore some novel methods used to mitigate said biases. In particular, I will focus my discussion on the applications of unsupervised machine learning methods and how they can be used to tackle these problems.
About Angus Wright
Angus is a Research Fellow in the German Centre for Cosmological Lensing at the Ruhr Universität Bochum, Germany. His undertook his PhD at the University of Western Australia, where he studied the growth and evolution of baryonic mass as a member of the Galaxy and Mass Assembly (GAMA) collaboration. Following his PhD, Angus worked for the Kilo Degree Survey (KiDS) at the Argelander Institute for Astronomy at the Universität Bonn, where he began his work within weak gravitational lensing. For the last 3 years his research has focused on weak lensing survey science, and particularly on optimisation of photometric image reduction and analysis methods, systematics mitigation, and statistical analyses. Beyond weak lensing, Angus is a keen astrostatistician, a passable astronomy outreach presenter, and an enthusiastic but nonetheless mediocre golfer.