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LISA Data Analysis Methods

    Principal Investigators: B. Schutz, M. A. Papa

The ESA-NASA joint space detector LISA, which is expected to be launched in 2015, will have great sensitivity in detecting gravitational waves. But LISA presents new challenges for data analysis. It will return an immense amount of data. However, LISA's high sensitivity creates a problem: signals have to be extracted from a background dominated by other signals. The problem for LISA is a {it source confusion} problem.

The ground-based interferometers like LIGO, GEO, and VIRGO that are now operating are expected to make the first direct detections of gravitational waves, and to open the new field of gravitational wave astronomy. But the ESA-NASA joint space detector LISA, which is expected to be launched in 2015, will have such great sensitivity that it will return an immense amount of science: fundamental tests of general relativity, detailed studies of black hole mergers, new insight into the formation of the giant black holes in the centers of galaxies, and a detailed picture of the end-phase of binary stellar evolution.

The body of gravitational wave data analysis research has until now mostly focused on the detection problem for the ground-based detectors. In this case signals can be classified in 4 categories -- inspiral, continuous, burst and stochastic background signals -- and the search problem may be tackled for one class of signals virtually independently of the other classes.

The reason for this is that the signal to noise ratio (SNR) of the expected signals is low and their occurence rare: The problem of ground based detectors is a _detection_ problem.

LISA presents new challenges for data analysis. The frequency range visible to LISA lies between a few tenths of Hz to the ^{-4} region, complementary to the frequency band of ground based detectors which is between 50 and 1000 Hz. However, LISA's high sensitivity creates a problem: signals have to be extracted from a background dominated by other signals. The problem for LISA is a _source confusion_ problem.

The data analysis problem for LISA is therefore very different in many respects from that of ground based interferometers and presents unique challenges that so far have not been adequately addressed. These challenges must be met in the next four or five years if the current development schedule of LISA is to be met. Scientists at the AEI are playing key roles in developing the ESA-NASA plan for the analysis of LISA data. With this proposal we wish to bring detailed algorithm development for LISA data analysis into the SFB activities so that German scientists are well prepared to take advantage of the opportunities open to them for this flagship international mission.

Researchers

Publications

[1] The search for spinning black hole binaries in mock LISA data using a genetic algorithm
A. Petiteau, Yu Shang, S. Babak, Phys. Rev. D

[2] A detection algorithm for extreme mass ratio inspirals in LISA data
S. Babak, J. Gair, E. Porter, Class. Quant. Grav

[3] Massive Black Hole Binary Inspirals: Results from the LISA Parameter Estimation Taskforce
K. G. Arun, S. Babak, et al., Class. Quant. Grav.

[4] The search for black hole binaries using a genetic algorithm
A. Petiteau; Yu Shang; S. Babak, Class. Quant. Grav.

[5] Detecting White Dwarf Binaries in Mock LISA Data Challenge 3
A. Blaut, A. Krolak, S. Babak, Class. Quant. Grav.

[6] The Mock LISA Data Challenges: from Challenge 3 to Challenge 4
S. Babak, et al., accepted in CQG

[7] A template bank for gravitational waveforms from coalescing binary black holes: I. non-spinning binaries
P. Ajith, S. Babak, Y. Chen, M. Hewitson, B. Krishnan, A. M. Sintes, J. T. Whelan, B. Bruegmann, P. Diener, N. Dorband, J. Gonzalez, M. Hannam, S. Husa, D. Pollney, L. Rezzolla, L. Santamaria, U. Sperhake, J. Thornburg, Phys. Rev. D

[8] Building a stochastic template bank for detecting massive black hole binaries
S. Babak, Class. Quant. Grav.

[9] A constrained Metropolis-Hastings search for EMRIs in the mock LISA data challenge 1B
J. Gair, S. Babak, E. Porter, L. Barack, Class. Quant. Grav.

[10] The Mock LISA Data Challenges: from Challenge 1B to Challenge 3
S. Babak., et al., Class. Quant. Grav.

[11] Python and XML for Agile Scientific Computing
M. Vallisneri and S. Babak, Computing in Science and Engineerin

[12] Kludge gravitational waveforms for a test-body orbiting a Kerr black hole
Babak S., Fang H., Gair J., Glampedakis K., Hughes S., Phys. Rev. D

[13] Phenomenological template family for black-hole coalescence waveforms
P. Ajith, S. Babak, Y. Chen, M. Hewitson, B. Krishnan, J. T. Whelan, B. Bruegmann, P. Diener, J. Gonzalez, M. Hannam, S. Husa, M. Koppitz, D. Pollney, L. Rezzolla, L. Santamaria, A. M. Sintes, U. Sperhake, J. Thornburg, Class. Quantum Grav.

[14] An overview of the second round of the Mock LISA Data Challenges
K A Arnaud, S Babak, J Baker, M J Benacquista, N J Cornish, C Cutler, L S Finn, S L Larson, T Littenberg, E K Porter, B S Sathyaprakash, M Vallisneri, A Vecchio, J-Y Vinet, Class. Quantum Grav.

[15] Report on the first round of the Mock LISA Data Challenges
K.A. Arnaud, G. Auger, S. Babak, et al., Class. Quantum Grav.

[16] Astrophysics, detection and science applications of intermediate- and extreme mass-ratio inspirals
P. Amaro-Seoane, J. R. Gair, M. Freitag, C.M. Miller, I. Mandel, C. Cutler, S. Babak, Class. Quantum Grav.