Best network chirplet chain: Near-optimal coherent detection of unmodeled gravitational wave chirps with a network of detectors

The searches of impulsive gravitational waves (GW) in the data of the ground-based interferometers focus essentially on two types of waveforms: short unmodeled bursts from supernova core collapses and frequency modulated signals (or chirps) from inspiralling compact binaries. There is room for other types of searches based on different models. Our objective is to fill this gap. More specifically, we are interested in GW chirps ‘‘in general,’’ i.e., with an arbitrary phase/frequency vs time evolution. These unmodeled GW chirps may be considered as the generic signature of orbiting or spinning sources. We expect the quasiperiodic nature of the waveform to be preserved independently of the physics which governs the source motion. Several methods have been introduced to address the detection of unmodeled chirps using the data of a single detector. Those include the best chirplet chain (BCC) algorithm introduced by the authors. In the next years, several detectors will be in operation. Improvements can be expected from the joint observation of a GW by multiple detectors and the coherent analysis of their data, namely, a larger sight horizon and the more accurate estimation of the source location and the wave polarization angles. Here, we present an extension of the BCC search to the multiple detector case. This work is based on the coherent analysis scheme proposed in the detection of inspiralling binary chirps. We revisit the derivation of the optimal statistic with a new formalism which allows the adaptation to the detection of unmodeled chirps. The method amounts to searching for salient paths in the combined time-frequency representation of two synthetic streams. The latter are time series which combine the data from each detector linearly in such a way that all the GW signatures received are added constructively. We give a proof of principle for the full-sky blind search in a simplified situation which shows that the joint estimation of the source sky location and chirp frequency is possible.

[1]  Coherent Network Detection of Gravitational Waves: The Redundancy Veto , 2005, gr-qc/0508042.

[2]  K. Thorne Multipole expansions of gravitational radiation , 1980 .

[3]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[4]  J. W. Humberston Classical mechanics , 1980, Nature.

[5]  P. Flandrin,et al.  On the Time–Frequency Detection of Chirps1 , 1999 .

[6]  Measuring gravitational waves from binary black hole coalescences. I. Signal to noise for inspiral, merger, and ringdown , 1997, gr-qc/9701039.

[7]  R. Balasubramanian,et al.  Time-frequency detection of gravitational waves , 1999 .

[8]  Owen P. Leary,et al.  40: PATIENT-SPECIFIC PROGNOSTICATION AFTER TBI IS RELATED TO BLEED PHENOTYPE AND ANATOMIC LOCATION , 2006, Testament d'un patriote exécuté.

[9]  Emmanuel J. Candes,et al.  Detecting highly oscillatory signals by chirplet path pursuit , 2006, gr-qc/0604017.

[10]  Lee Samuel Finn Aperture synthesis for gravitational-wave data analysis: Deterministic sources , 2001 .

[11]  Julien Sylvestre Optimal generalization of power filters for gravitational wave bursts from single to multiple detectors , 2003 .

[12]  Soumya D. Mohanty,et al.  Variability of signal-to-noise ratio and the network analysis of gravitational wave burst signals , 2006, gr-qc/0601076.

[13]  S. Dhurandhar,et al.  Astronomical observations with a network of detectors of gravitational waves. I - Mathematical framework and solution of the five detector problem , 1988 .

[14]  Sanjeev Dhurandhar,et al.  A data-analysis strategy for detecting gravitational-wave signals from inspiraling compact binaries with a network of laser-interferometric detectors , 2001 .

[15]  D. F. Johnston,et al.  Representations of the Rotation and Lorentz Groups and Their Applications , 1965 .

[16]  Harry L. Van Trees,et al.  Optimum Array Processing , 2002 .

[17]  Gainesville,et al.  Constraint Likelihood analysis for a network of gravitational wave detectors , 2005 .

[18]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[19]  Leo Stein,et al.  Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise , 2006 .

[20]  Andrew G. Glen,et al.  APPL , 2001 .

[21]  S. Dhurandhar,et al.  0 60 81 03 v 1 2 2 A ug 2 00 6 Detecting gravitational waves from inspiraling binaries with a network of detectors : coherent versus coincident strategies , 2010 .

[22]  A. Pai,et al.  Optimizing the directional sensitivity of LISA , 2003, gr-qc/0306050.

[23]  M. Tinto,et al.  Near optimal solution to the inverse problem for gravitational-wave bursts. , 1989, Physical review. D, Particles and fields.

[24]  S. Hughes,et al.  The basics of gravitational wave theory , 2005, gr-qc/0501041.

[25]  Arnold Neumaier,et al.  Solving Ill-Conditioned and Singular Linear Systems: A Tutorial on Regularization , 1998, SIAM Rev..

[26]  Eric Chassande-Mottin,et al.  Best chirplet chain: near-optimal detection of gravitational wave chirps , 2006 .

[27]  José A. González,et al.  Inspiral, merger, and ringdown of unequal mass black hole binaries: A multipolar analysis , 2007, gr-qc/0703053.

[28]  Gene H. Golub,et al.  Matrix computations , 1983 .

[29]  Stephane Kreckelbergh,et al.  Coincidence and coherent data analysis methods for gravitational wave bursts in a network of interferometric detectors , 2003 .

[30]  Bruno Torrésani,et al.  Wavelets and Binary Coalescences Detection , 1997 .

[31]  E. Chassande-Mottin,et al.  Discrete time and frequency Wigner-Ville distribution: Moyal's formula and aliasing , 2005, IEEE Signal Processing Letters.

[32]  Rank deficiency and Tikhonov regularization in the inverse problem for gravitational-wave bursts , 2006, gr-qc/0604005.

[33]  F. A. Jenet,et al.  Detection of variable frequency signals using a fast chirp transform , 2000 .

[34]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .