925 research outputs found
The Galactic Isotropic -ray Background and Implications for Dark Matter
We present an analysis of the radial angular profile of the galacto-isotropic
(GI) -ray flux--the statistically uniform flux in circular annuli about
the Galactic center. Two different approaches are used to measure the GI flux
profile in 85 months of Fermi-LAT data: the BDS statistic method which
identifies spatial correlations, and a new Poisson ordered-pixel method which
identifies non-Poisson contributions. Both methods produce similar GI flux
profiles. The GI flux profile is well-described by an existing model of
bremsstrahlung, production, inverse Compton scattering, and the
isotropic background. Discrepancies with data in our full-sky model are not
present in the GI component, and are therefore due to mis-modeling of the
non-GI emission. Dark matter annihilation constraints based solely on the
observed GI profile are close to the thermal WIMP cross section below 100 GeV,
for fixed models of the dark matter density profile and astrophysical
-ray foregrounds. Refined measurements of the GI profile are expected
to improve these constraints by a factor of a few.Comment: 20 pages, 15 figures, references adde
The Galactic Isotropic -ray Background and Implications for Dark Matter
We present an analysis of the radial angular profile of the galacto-isotropic
(GI) -ray flux--the statistically uniform flux in circular annuli about
the Galactic center. Two different approaches are used to measure the GI flux
profile in 85 months of Fermi-LAT data: the BDS statistic method which
identifies spatial correlations, and a new Poisson ordered-pixel method which
identifies non-Poisson contributions. Both methods produce similar GI flux
profiles. The GI flux profile is well-described by an existing model of
bremsstrahlung, production, inverse Compton scattering, and the
isotropic background. Discrepancies with data in our full-sky model are not
present in the GI component, and are therefore due to mis-modeling of the
non-GI emission. Dark matter annihilation constraints based solely on the
observed GI profile are close to the thermal WIMP cross section below 100 GeV,
for fixed models of the dark matter density profile and astrophysical
-ray foregrounds. Refined measurements of the GI profile are expected
to improve these constraints by a factor of a few.Comment: 20 pages, 15 figures, references adde
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-maximization (EM). In this paper we propose a new mixture
model that associates a weight with each observed point. We introduce the
weighted-data Gaussian mixture and we derive two EM algorithms. The first one
considers a fixed weight for each observation. The second one treats each
weight as a random variable following a gamma distribution. We propose a model
selection method based on a minimum message length criterion, provide a weight
initialization strategy, and validate the proposed algorithms by comparing them
with several state of the art parametric and non-parametric clustering
techniques. We also demonstrate the effectiveness and robustness of the
proposed clustering technique in the presence of heterogeneous data, namely
audio-visual scene analysis.Comment: 14 pages, 4 figures, 4 table
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization
This work theoretically studies the problem of estimating a structured
high-dimensional signal from noisy -bit Gaussian
measurements. Our recovery approach is based on a simple convex program which
uses the hinge loss function as data fidelity term. While such a risk
minimization strategy is very natural to learn binary output models, such as in
classification, its capacity to estimate a specific signal vector is largely
unexplored. A major difficulty is that the hinge loss is just piecewise linear,
so that its "curvature energy" is concentrated in a single point. This is
substantially different from other popular loss functions considered in signal
estimation, e.g., the square or logistic loss, which are at least locally
strongly convex. It is therefore somewhat unexpected that we can still prove
very similar types of recovery guarantees for the hinge loss estimator, even in
the presence of strong noise. More specifically, our non-asymptotic error
bounds show that stable and robust reconstruction of can be achieved with
the optimal oversampling rate in terms of the number of
measurements . Moreover, we permit a wide class of structural assumptions on
the ground truth signal, in the sense that can belong to an arbitrary
bounded convex set . The proofs of our main results
rely on some recent advances in statistical learning theory due to Mendelson.
In particular, we invoke an adapted version of Mendelson's small ball method
that allows us to establish a quadratic lower bound on the error of the first
order Taylor approximation of the empirical hinge loss function
Advanced Denoising for X-ray Ptychography
The success of ptychographic imaging experiments strongly depends on
achieving high signal-to-noise ratio. This is particularly important in
nanoscale imaging experiments when diffraction signals are very weak and the
experiments are accompanied by significant parasitic scattering (background),
outliers or correlated noise sources. It is also critical when rare events such
as cosmic rays, or bad frames caused by electronic glitches or shutter timing
malfunction take place.
In this paper, we propose a novel iterative algorithm with rigorous analysis
that exploits the direct forward model for parasitic noise and sample
smoothness to achieve a thorough characterization and removal of structured and
random noise. We present a formal description of the proposed algorithm and
prove its convergence under mild conditions. Numerical experiments from
simulations and real data (both soft and hard X-ray beamlines) demonstrate that
the proposed algorithms produce better results when compared to
state-of-the-art methods.Comment: 24 pages, 9 figure
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