5,234 research outputs found
Lensing reconstruction from line intensity maps: the impact of gravitational nonlinearity
We investigate the detection prospects for gravitational lensing of
three-dimensional maps from upcoming line intensity surveys, focusing in
particular on the impact of gravitational nonlinearities on standard quadratic
lensing estimators. Using perturbation theory, we show that these
nonlinearities can provide a significant contaminant to lensing reconstruction,
even for observations at reionization-era redshifts. However, we show how this
contamination can be mitigated with the use of a "bias-hardened" estimator.
Along the way, we present an estimator for reconstructing long-wavelength
density modes, in the spirit of the "tidal reconstruction" technique that has
been proposed elsewhere, and discuss the dominant biases on this estimator.
After applying bias-hardening, we find that a detection of the lensing
potential power spectrum will still be challenging for the first phase of
SKA-Low, CHIME, and HIRAX, with gravitational nonlinearities decreasing the
signal to noise by a factor of a few compared to forecasts that ignore these
effects. On the other hand, cross-correlations between lensing and galaxy
clustering or cosmic shear from a large photometric survey look promising,
provided that systematics can be sufficiently controlled. We reach similar
conclusions for a single-dish survey inspired by CII measurements planned for
CCAT-prime, suggesting that lensing is an interesting science target not just
for 21cm surveys, but also for intensity maps of other lines.Comment: 40+18 pages, 13 figures, 5 tables. v2: JCAP published version, with
typos fixed and clarifications adde
Infrastructure for Detector Research and Development towards the International Linear Collider
The EUDET-project was launched to create an infrastructure for developing and
testing new and advanced detector technologies to be used at a future linear
collider. The aim was to make possible experimentation and analysis of data for
institutes, which otherwise could not be realized due to lack of resources. The
infrastructure comprised an analysis and software network, and instrumentation
infrastructures for tracking detectors as well as for calorimetry.Comment: 54 pages, 48 picture
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
In this paper, we present a distributed algorithm for the reconstruction of
large-scale nonlinear networks. In particular, we focus on the identification
from time-series data of the nonlinear functional forms and associated
parameters of large-scale nonlinear networks. Recently, a nonlinear network
reconstruction problem was formulated as a nonconvex optimisation problem based
on the combination of a marginal likelihood maximisation procedure with
sparsity inducing priors. Using a convex-concave procedure (CCCP), an iterative
reweighted lasso algorithm was derived to solve the initial nonconvex
optimisation problem. By exploiting the structure of the objective function of
this reweighted lasso algorithm, a distributed algorithm can be designed. To
this end, we apply the alternating direction method of multipliers (ADMM) to
decompose the original problem into several subproblems. To illustrate the
effectiveness of the proposed methods, we use our approach to identify a
network of interconnected Kuramoto oscillators with different network sizes
(500~100,000 nodes).Comment: To appear in the Preprints of 19th IFAC World Congress 201
Nonlinear time-series analysis revisited
In 1980 and 1981, two pioneering papers laid the foundation for what became
known as nonlinear time-series analysis: the analysis of observed
data---typically univariate---via dynamical systems theory. Based on the
concept of state-space reconstruction, this set of methods allows us to compute
characteristic quantities such as Lyapunov exponents and fractal dimensions, to
predict the future course of the time series, and even to reconstruct the
equations of motion in some cases. In practice, however, there are a number of
issues that restrict the power of this approach: whether the signal accurately
and thoroughly samples the dynamics, for instance, and whether it contains
noise. Moreover, the numerical algorithms that we use to instantiate these
ideas are not perfect; they involve approximations, scale parameters, and
finite-precision arithmetic, among other things. Even so, nonlinear time-series
analysis has been used to great advantage on thousands of real and synthetic
data sets from a wide variety of systems ranging from roulette wheels to lasers
to the human heart. Even in cases where the data do not meet the mathematical
or algorithmic requirements to assure full topological conjugacy, the results
of nonlinear time-series analysis can be helpful in understanding,
characterizing, and predicting dynamical systems
Volterra-assisted Optical Phase Conjugation: a Hybrid Optical-Digital Scheme For Fiber Nonlinearity Compensation
Mitigation of optical fiber nonlinearity is an active research field in the
area of optical communications, due to the resulting marked improvement in
transmission performance. Following the resurgence of optical coherent
detection, digital nonlinearity compensation (NLC) schemes such as digital
backpropagation (DBP) and Volterra equalization have received much attention.
Alternatively, optical NLC, and specifically optical phase conjugation (OPC),
has been proposed to relax the digital signal processing complexity. In this
work, a novel hybrid optical-digital NLC scheme combining OPC and a Volterra
equalizer is proposed, termed Volterra-Assisted OPC (VAO). It has a twofold
advantage: it overcomes the OPC limitation in asymmetric links and
substantially enhances the performance of Volterra equalizers. The proposed
scheme is shown to outperform both OPC and Volterra equalization alone by up to
4.2 dB in a 1000 km EDFA-amplified fiber link. Moreover, VAO is also
demonstrated to be very robust when applied to long-transmission distances,
with a 2.5 dB gain over OPC-only systems at 3000 km. VAO combines the
advantages of both optical and digital NLC offering a promising trade-off
between performance and complexity for future high-speed optical communication
systems
Frame Theory for Signal Processing in Psychoacoustics
This review chapter aims to strengthen the link between frame theory and
signal processing tasks in psychoacoustics. On the one side, the basic concepts
of frame theory are presented and some proofs are provided to explain those
concepts in some detail. The goal is to reveal to hearing scientists how this
mathematical theory could be relevant for their research. In particular, we
focus on frame theory in a filter bank approach, which is probably the most
relevant view-point for audio signal processing. On the other side, basic
psychoacoustic concepts are presented to stimulate mathematicians to apply
their knowledge in this field
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
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