70,511 research outputs found
Signal Processing in Large Systems: a New Paradigm
For a long time, detection and parameter estimation methods for signal
processing have relied on asymptotic statistics as the number of
observations of a population grows large comparatively to the population size
, i.e. . Modern technological and societal advances now
demand the study of sometimes extremely large populations and simultaneously
require fast signal processing due to accelerated system dynamics. This results
in not-so-large practical ratios , sometimes even smaller than one. A
disruptive change in classical signal processing methods has therefore been
initiated in the past ten years, mostly spurred by the field of large
dimensional random matrix theory. The early works in random matrix theory for
signal processing applications are however scarce and highly technical. This
tutorial provides an accessible methodological introduction to the modern tools
of random matrix theory and to the signal processing methods derived from them,
with an emphasis on simple illustrative examples
The Mock LISA Data Challenges: from Challenge 3 to Challenge 4
The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis
capabilities and to encourage their development. Each round of challenges
consists of one or more datasets containing simulated instrument noise and
gravitational waves from sources of undisclosed parameters. Participants
analyze the datasets and report best-fit solutions for the source parameters.
Here we present the results of the third challenge, issued in Apr 2008, which
demonstrated the positive recovery of signals from chirping Galactic binaries,
from spinning supermassive--black-hole binaries (with optimal SNRs between ~ 10
and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10-50), from
cosmic-string-cusp bursts (SNRs of 10-100), and from a relatively loud
isotropic background with Omega_gw(f) ~ 10^-11, slightly below the LISA
instrument noise.Comment: 12 pages, 2 figures, proceedings of the 8th Edoardo Amaldi Conference
on Gravitational Waves, New York, June 21-26, 200
The Mock LISA Data Challenges: from challenge 3 to challenge 4
The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of one or more datasets containing simulated instrument noise and gravitational waves from sources of undisclosed parameters. Participants analyze the datasets and report best-fit solutions for the source parameters. Here we present the results of the third challenge, issued in April 2008, which demonstrated the positive recovery of signals from chirping galactic binaries, from spinning supermassive-black-hole binaries (with optimal SNRs between ~10 and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10–50), from cosmic-string-cusp bursts (SNRs of 10–100), and from a relatively loud isotropic background with Ω_(gw)(f) ~ 10^(−11), slightly below the LISA instrument noise
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