Status of VIRGO Sipho van der Putten

2 Contents Introduction to gravitational waves VIRGO Pulsars: gravitational waves from periodic sources Pulsars in binary systems Analysis approach

3 Introduction to Gravitational Waves ‘Ripples’ in space-time due to accelerating masses distorting space- time  Two polarizations: ‘+’ & ‘x’  Measured in strain: h=dL/L Extremely weak effects:  Supernova (10 kpc, ~10 M solar ): h~  Rotating deformed neutron star (~10 kpc, ~1 M solar, 100 Hz): h~ Ring of free falling masses L

4 VIRGO Science Run VSR1 complete Oct ‘07  Combined LIGO & VIRGO data taking  ~5 months data Current upgrade (VIRGO+) underway  5x better at low freq & 2x better at high freq  Nikhef: Electronics, IMC  VSR2 in mid 2009: Combined run with eLIGO 1 year of data Next upgrade AdvVirgo  10x better in sensitivity than initial VIRGO design  2010 to 2012

5 Periodic sources of gravity waves Pulsars: spinning neutron stars Emitting GWs requires quadrupole moment; symmetry axis is not rotation axis Neutron stars:  Isolated  Binary systems

6 Neutron stars in binary systems 2/3 of NS (f>10Hz) in a binary system Mass transfer:  Spin-up: f increases Many parameters :  Orbital:  Sky Position:  Source:  …. Doppler shift due to orbit of binary system: non stationary frequency Our goal: All-sky search for neutron stars in binary systems

7 Doppler shift Fixed point emits stationary frequency Rotation of Earth (daily motion)  df/f~10 -6 Earth’s orbit (yearly motion)  df/f~10 -4 Pulsars in binary system  df/f~10 -3 Hulse-Taylor system  All shifts included

8 Analysis binaries: spectral filtering Non stationary frequency: FFT → power spread out, bad S/N Spectral filtering: Identify the signal in the data f sig = Hz f sig = – 5*10 -4 t Hz

9 Conceptual approach Split the data in time stretches Second order spectral filter  Each matching filter:  f 0,a,b,t avg Pattern recognition using all the information available

10 Simulations Simulated waveform: Hulse-Taylor system Divide in time slices  1286 s Simple FFT each slice Time (s) Frequency (Hz) h rec Time (s) Frequency (Hz) h re c P(noise)=100 P(sig)

11 Spectral filtering Spectral filters:  2607 filters, applied ~10 9 times  Threshold 4x noise level CPU time: 1 day for ~10 h data (0-400 Hz)  Many improvements in efficiency possible Non-stationary frequency not an issue anymore To do:  Investigate higher order filters→ longer FFTs  Pattern recognition Time (s) Frequency (Hz) h re c P(noise)=100 P(sig)

12 Conclusions VIRGO & LIGO have 5 months of science-grade data VIRGO+ & eLIGO upgrade underway and will be ready in 2009 Analysis of binary pulsars:  Conceptual approach to analysis: spectral filtering  S/N 0.1 easy with 2 nd order filter  Time dependent frequency no problem  Todo: test idea on simulations and real data (higher frequency signal, more noise)  Todo: implement pattern recognition

13 P(noise)=10 4 P(sig) Time (s) Frequency (Hz) h re c