S. V. DHURANDHAR

30
30 th January 2009 25 th IAGRG meeting Kolkata S. V. DHURANDHAR IUCAA PUNE

description

THE CHANGING FACE OF GENERAL RELATIVITY. S. V. DHURANDHAR. IUCAA PUNE. General Relativity and other disciplines. GR finds its home in astronomy & astrophysics The binary blackhole problem No exact two body solution : PN + Numerical Relativity - PowerPoint PPT Presentation

Transcript of S. V. DHURANDHAR

Page 1: S.  V.  DHURANDHAR

30th January 2009 25th IAGRG meeting Kolkata

S. V. DHURANDHAR

IUCAA

PUNE

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General Relativity and other disciplines

• GR finds its home in astronomy & astrophysics

- The binary blackhole problem

- No exact two body solution: PN + Numerical Relativity

- Cosmology: observational data, COBE, WMAP, PLANCK

• Mathematics: Commutative algebra, differential

geometry in statistics

• Statistics: Hypothesis testing, statistical tests, maximum likelihood, etc. - Signal processing

• Gravitation has gone experimental: Gravitational waves

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Effect on a ring of test particles

01

222

ikhtc

Metric:

ikikik ecztheczthh )()(General Wave:

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But h is awfully small !

L ~ h L

Quadrupole formula :

21

1

2410

1001.0~

..4~

Mpc

R

cM

E

R

EK

c

Gh

nskinns

Change in arm-length:

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LIGO Louisiana 4 km armlength (US)

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We did it !

*http://www.ligocaltech.edu/~lazz/distribution/LSC_Data

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LISA: Space based detector for detecting low frequency GW

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Laser frequency noise

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into, Eastabrook, Armstrong; SVD, Nayak, Vinet, Pai

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Open Problems

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Inspiraling compact binaries

GW

• Broadband source best for interferometric detectors

• Waveform is well modeled by PN approximations – waveform obtained to 3.5 PN

• Numerical Relativity: great advances – merger

• Signal is way below the noise – data analysis: filtering

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Breakthrough in Numerical Relativity

Numerical Relativity

solves

• merger waveform

• 3.5% of total restmass energy as compared to 1.5 % in inspiral waveform!

Work in progress on stitching together waveformsIndia has the right talent/aptitude for NR

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Example of a filter

A sinusoidal signal is embedded in noise

0 0

( ) ( ) ( )

( ) cos 2

x t s t n t

s t A f t

Data:

Signal:

0 256f Hz

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Filtering the data

2( ) ( )

( ) | ( )|

iftx f x t e dt

C f x f

Best filter is the Fourier Transform:

Statistic:

Generalisation: Matched Filter

Provides parameters of the signal: eg. frequency

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Matched filtering the inspiraling binary signal

)(

)()(

)()()(

~~

fS

fhfq

dttqtxc

h

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Statistics and geometry

• Statistic: c()

• Where does one set the threshold? – False alarm probability – choose threshold such that this is small

• Detection probability: choose this high

• Signal depends on many parameters: masses, kinematical parameters: initial phase, time of arrival …

• Parameter space

• Parameter space can be viewed as a manifold with the

parameters as coordinates

• Metric: mismatch between signal and template

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Parameter space metric

Coordinates: i

gik

ik

Choose coordinates so that metric is simplest: Cartesian coordinates

For inspiral choose chirp times instead of masses: 03

Metric const: Uniform placement of templates

Number of templates = volume of parameter space / template size

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International Network of GW InterferometersInternational Network of GW Interferometers

LIGO-LLO: 4km

LIGO-LHO: 2km, 4kmGEO: 0.6km VIRGO: 3km

TAMA: 0.3km

AIGO: (?)km

1. Detection confidence 2. Source direction 3. Polarisation info1. Detection confidence 2. Source direction 3. Polarisation info

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eed for a (or two) detector(s) in Asia/Australia

• AIGO: Australian project

• INDIGO: Indian detector?

• Advantages: sky coverage, resolution of sources

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Optimum location of a detector

Courtesy A. Sengupta & S. Mitra

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ptimum location

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Network Sensitivity

IGO/INDIGO network doubles sensitivity

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Angular Resolution

An order of magnitude improvement

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Summary

Connection of GR to several fields:

Astrophysics, Mathematics, Statistics, Engineering …

Numerical Relativity

Gravitation going experimental