Machine learning in astronomy: time delay estimation in gravitational lensing
- When?
- Wednesday 15 October 2008, 14:00 to 15:00
- Where?
- 39BB02
- Open to:
- Students, Staff
Dr Peter Tino, Computer Science Department, University of Birmingham
Abstract:
A ray of light (or any other form of electromagnetic radiation, e.g. radio or x-rays) travels along a geodesic, which could be locally curved due to the gravitational effect of clumps of matter like stars or galaxies. This is known as gravitational lensing. Gravitational lensing, caused by intervening matter along the line of sight, can give rise to interesting cosmic illusions like magnified and seriously distorted images of distant sources, sometimes splitting into multiple images. Since the distortion of the images depends on the distribution of matter in the lensing object, this is the most direct method of measuring matter (which is often dark) in the Universe. Quasar Q0957+561, an ultra-bright galaxy with a super massive central black hole was the first lensed source to be discovered and it is the most studied so far. Gravitational lens creates two distinct images of Q0957+561. We attempt to recover the phase shift in the 2 lensed images of Q0957+561 using a model based approach formulated within the framework of kernel regression. In a set of controlled experiments emulating presence of realistic observational gaps, irregular observation times and noisy observations, we compare our method with other state-of-art statistical methods currently used in astrophysics. We then apply the method to actual observations doubly imaged quasar Q0957+561 at several radio and optical frequencies.
Notes:
Peter Tino obtained his PhD from the Slovak Academy of Sciences. He worked at NEC Research Institute in Princeton, New Jersey (Fulbright fellow), USA, Austrian Research Institute for AI in Vienna, Austria, Neural Computation Research Group at Aston University, UK and City University of Hong Kong. He is currently a senior lecturer at the School of Computer Science, University of Birmingham, UK. His scientific interests include dynamical systems, probabilistic modelling and visualization of structured data, statistical pattern analysis and fractal analysis. He has won the Outstanding Paper of the Year award for IEEE Transactions on Neural Networks and the best paper award at ICANN 2002. He was a technical program committee chair of IDEAL 2007, programme committee member of numerous international conferences and serves on editorial boards of Pattern Analysis and Applications and Neural Processing Letters.
Notes:
Peter Tino obtained his PhD from the Slovak Academy of Sciences. He worked at NEC Research Institute in Princeton, New Jersey (Fulbright fellow), USA, Austrian Research Institute for AI in Vienna, Austria, Neural Computation Research Group at Aston University, UK and City University of Hong Kong. He is currently a senior lecturer at the School of Computer Science, University of Birmingham, UK. His scientific interests include dynamical systems, probabilistic modelling and visualization of structured data, statistical pattern analysis and fractal analysis. He has won the Outstanding Paper of the Year award for IEEE Transactions on Neural Networks and the best paper award at ICANN 2002. He was a technical program committee chair of IDEAL 2007, programme committee member of numerous international conferences and serves on editorial boards of Pattern Analysis and Applications and Neural Processing Letters.
Notes:
Peter Tino obtained his PhD from the Slovak Academy of Sciences. He worked at NEC Research Institute in Princeton, New Jersey (Fulbright fellow), USA, Austrian Research Institute for AI in Vienna, Austria, Neural Computation Research Group at Aston University, UK and City University of Hong Kong. He is currently a senior lecturer at the School of Computer Science, University of Birmingham, UK. His scientific interests include dynamical systems, probabilistic modelling and visualization of structured data, statistical pattern analysis and fractal analysis. He has won the Outstanding Paper of the Year award for IEEE Transactions on Neural Networks and the best paper award at ICANN 2002. He was a technical program committee chair of IDEAL 2007, programme committee member of numerous international conferences and serves on editorial boards of Pattern Analysis and Applications and Neural Processing Letters.
