7,582 research outputs found
TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys
There have been many efforts to correct systematic effects in astronomical
light curves to improve the detection and characterization of planetary
transits and astrophysical variability. Algorithms like the Trend Filtering
Algorithm (TFA) use simultaneously-observed stars to remove systematic effects,
and binning is used to reduce high-frequency random noise. We present TFAW, a
wavelet-based modified version of TFA. TFAW aims to increase the periodic
signal detection and to return a detrended and denoised signal without
modifying its intrinsic characteristics. We modify TFA's frequency analysis
step adding a Stationary Wavelet Transform filter to perform an initial noise
and outlier removal and increase the detection of variable signals. A wavelet
filter is added to TFA's signal reconstruction to perform an adaptive
characterization of the noise- and trend-free signal and the noise contribution
at each iteration while preserving astrophysical signals. We carried out tests
over simulated sinusoidal and transit-like signals to assess the effectiveness
of the method and applied TFAW to real light curves from TFRM. We also studied
TFAW's application to simulated multiperiodic signals, improving their
characterization. TFAW improves the signal detection rate by increasing the
signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light
curves. For simulated transits, the transit detection rate improves by a factor
~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs
up to a factor ~2x better than bin averaging for planetary transits. The
standard deviations of simulated and real TFAW light curves are ~40x better
than TFA. TFAW yields better MCMC posterior distributions and returns lower
uncertainties, less biased transit parameters and narrower (~10x) credibility
intervals for simulated transits. We present a newly-discovered variable star
from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table
Self-consistent calculation of particle-hole diagrams on the Matsubara frequency: FLEX approximation
We implement the numerical method of summing Green function diagrams on the
Matsubara frequency axis for the fluctuation exchange (FLEX) approximation. Our
method has previously been applied to the attractive Hubbard model for low
density. Here we apply our numerical algorithm to the Hubbard model close to
half filling (), and for , in order to study the
dynamics of one- and two-particle Green functions. For the values of the chosen
parameters we see the formation of three branches which we associate with the a
two-peak structure in the imaginary part of the self-energy. From the imaginary
part of the self-energy we conclude that our system is a Fermi liquid (for the
temperature investigated here), since Im
around the chemical potential. We have compared our fully self-consistent FLEX
solutions with a lower order approximation where the internal Green functions
are approximated by free Green functions. These two approches, i.e., the fully
selfconsistent and the non-selfconsistent ones give different results for the
parameters considered here. However, they have similar global results for small
densities.Comment: seven pages, nine figures as ps files. Accepted in Int. J. Modern
Phys. C (1997
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