602 research outputs found
Best chirplet chain: near-optimal detection of gravitational wave chirps
The list of putative sources of gravitational waves possibly detected by the
ongoing worldwide network of large scale interferometers has been continuously
growing in the last years. For some of them, the detection is made difficult by
the lack of a complete information about the expected signal. We concentrate on
the case where the expected GW is a quasi-periodic frequency modulated signal
i.e., a chirp. In this article, we address the question of detecting an a
priori unknown GW chirp. We introduce a general chirp model and claim that it
includes all physically realistic GW chirps. We produce a finite grid of
template waveforms which samples the resulting set of possible chirps. If we
follow the classical approach (used for the detection of inspiralling binary
chirps, for instance), we would build a bank of quadrature matched filters
comparing the data to each of the templates of this grid. The detection would
then be achieved by thresholding the output, the maximum giving the individual
which best fits the data. In the present case, this exhaustive search is not
tractable because of the very large number of templates in the grid. We show
that the exhaustive search can be reformulated (using approximations) as a
pattern search in the time-frequency plane. This motivates an approximate but
feasible alternative solution which is clearly linked to the optimal one.
[abridged version of the abstract]Comment: 23 pages, 9 figures. Accepted for publication in Phys. Rev D Some
typos corrected and changes made according to referee's comment
The Berk-Breizman Model as a Paradigm for Energetic Particle-driven Alfven Eigenmodes
The achievement of sustained nuclear fusion in magnetically confined plasma
relies on efficient confinement of high-energy ions produced by the fusion
reaction. Such particles can excite Alfven Eigenmodes (AEs), which
significantly degrade their confinement and threatens the vacuum vessel of
future reactors. To develop diagnostics and control schemes, a better
understanding of linear and nonlinear features of resonant interactions between
plasma waves and high-energy particles, is required. In the case of an isolated
single resonance, the problem is homothetic to the so-called Berk-Breizman (BB)
problem, which is an extension of the classic bump-on-tail electrostatic
problem, including external damping to a thermal plasma, and collisions. A
semi-Lagrangian simulation code, COBBLES, is developed to solve the
initial-value BB problem. The nonlinear behavior of instabilities in
experimentally-relevant conditions is categorized into steady-state, periodic,
chaotic, and frequency-sweeping (chirping) regimes. The chaotic regime is shown
to extend into a linearly stable region, and a mechanism for such subcritical
instabilities is proposed. Analytic and semi-empirical laws for nonlinear
chirping characteristics, such as sweeping-rate, lifetime, and asymmetry, are
developed and validated. Long-time simulations demonstrate the existence of a
quasi-periodic chirping regime. Collisional drag and diffusion are shown to be
essential to reproduce the alternation between major chirping events and
quiescent phases, which is observed in experiments. Based on these findings, a
fitting procedure between COBBLES simulations and chirping AE experiments is
developped. This procedure, which yields local linear drive and external
damping rate, is applied to Toroidicity-induced AEs (TAEs) on JT-60U and MAST
tokamaks. This suggests the existence of TAEs relatively far from marginal
stability
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Optically Multiplexed Systems: Wavelength Division Multiplexing
Optical multiplexing is the art of combining multiple optical signals into one to make full use of the immense bandwidth potential of an optical channel. It can perform additional roles like providing redundancy, supporting advanced topologies, reducing hardware and cost, etc. The idea is to divide the huge bandwidth of optical fiber into individual channels of lower bandwidth, so that multiple access with lower-speed electronics is achieved. This chapter focuses on one of the most common and important optical multiplexing techniques, wavelength division multiplexing (WDM). The chapter begins with a quick historical account of the origin of optical communication and its exponential growth following the invention of erbium-doped fiber amplifier (EDFA) leading to the widespread adoption of WDM. Alternate multiplexing schemes are also briefly discussed, including time-division multiplexing (TDM), space-division multiplexing (SDM), etc. A typical WDM link and its components are then discussed with special focus on WDM Mux/demultiplexer (DeMux). Further, certain challenges in this field are addressed along with some potential solutions. The chapter concludes by highlighting some features and limitations of optically multiplexed WDM systems
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