Challenges in Source Parameter Estimation in GW Astronomy by Rajesh Nayak

Описание: 20 March 2017 to 25 March 2017

VENUE: Madhava Lecture Hall, ICTS, Bengaluru

This joint program is co-sponsored by ICTS and SAMSI (as part of the SAMSI yearlong program on Astronomy; ASTRO). The primary goal of this program is to further enrich the international collaboration in the area of synoptic time domain surveys and time series analysis of gravitational wave data. The participants will focus on advancing the current understanding of these research topics by incorporating expertise of researchers from India and US who are working on identifying electromagnetic counterparts to gravitational wave sources. In essence, this program would enable US researchers to learn from the expertise of Indian researchers and also enable US researchers to exchange and share the methodologies developed by two of the five working groups of the SAMSI ASTRO program.

The program will begin with a few overview lectures designed to familiarize attendees with current trends in time domain astronomy and modern methodologies in statistics and applied mathematics. The subsequent part of the program will follow with specialized research lectures on the existing subgroups, panel discussions about collaboration possibilities between different groups with specific end-points in mind through collaborative research sessions.

Participation in this program is by invitation only and will involve about 35-40 participants only. If you are interested to participate, please contact one of the organizers.

CONTACT US:
[email protected]

PROGRAM LINK:
https://www.icts.res.in/program/TASSG...

Table of Contents (powered by https://videoken.com)
0:00:00 Start
0:00:10 Challenges in Source Parameter Estimation in GW Astronomy
0:00:37 Plan of the talk
0:01:13 The Dawn of Gravitational Wave Astronomy
0:02:04 Detection Problem
0:03:20 Detection Neyman-Pearson Approach
0:04:42 Detection vs Parameter Estimation
0:05:57 Signal Model
0:08:06 Parameter Estimation Problem
0:08:53 Parameter Estimation Bayesian Approach
0:11:03 Monte Carlo Methods
0:12:28 101 MCMC
0:13:34 Convergence of MCMC
0:16:00 Lal Inference in LIGO
0:16:31 Beyond Markovian Sampler
0:17:47 Particle swarm optimisation (PSO)
0:18:24 PSO Algorithm
0:21:58 Simple results, 1-D Case
0:27:30 Simple results