37,479 research outputs found
The ILIUM forward modelling algorithm for multivariate parameter estimation and its application to derive stellar parameters from Gaia spectrophotometry
I introduce an algorithm for estimating parameters from multidimensional data
based on forward modelling. In contrast to many machine learning approaches it
avoids fitting an inverse model and the problems associated with this. The
algorithm makes explicit use of the sensitivities of the data to the
parameters, with the goal of better treating parameters which only have a weak
impact on the data. The forward modelling approach provides uncertainty (full
covariance) estimates in the predicted parameters as well as a goodness-of-fit
for observations. I demonstrate the algorithm, ILIUM, with the estimation of
stellar astrophysical parameters (APs) from simulations of the low resolution
spectrophotometry to be obtained by Gaia. The AP accuracy is competitive with
that obtained by a support vector machine. For example, for zero extinction
stars covering a wide range of metallicity, surface gravity and temperature,
ILIUM can estimate Teff to an accuracy of 0.3% at G=15 and to 4% for (lower
signal-to-noise ratio) spectra at G=20. [Fe/H] and logg can be estimated to
accuracies of 0.1-0.4dex for stars with G<=18.5. If extinction varies a priori
over a wide range (Av=0-10mag), then Teff and Av can be estimated quite
accurately (3-4% and 0.1-0.2mag respectively at G=15), but there is a strong
and ubiquitous degeneracy in these parameters which limits our ability to
estimate either accurately at faint magnitudes. Using the forward model we can
map these degeneracies (in advance), and thus provide a complete probability
distribution over solutions. (Abridged)Comment: MNRAS, in press. This revision corrects a few minor errors and typos.
A better formatted version for A4 paper is available at
http://www.mpia.de/home/calj/ilium.pd
Impact of Galactic polarized emission on B-mode detection at low multipoles
We use a model of polarized Galactic emission developed by the the Planck
collaboration to assess the impact of foregrounds on B-mode detection at low
multipoles. Our main interest is to applications of noisy polarization data and
in particular to assessing the feasibility of B-mode detection by Planck. This
limits the complexity of foreground subtraction techniques that can be applied
to the data. We analyze internal linear combination techniques and show that
the offset caused by the dominant E-mode polarization pattern leads to a
fundamental limit of r approximately 0.1 for the tensor-scalar ratio even in
the absence of instrumental noise. We devise a simple, robust, template fitting
technique using multi-frequency polarization maps. We show that template
fitting using Planck data alone offers a feasible way of recovering primordial
B-modes from dominant foreground contamination, even in the presence of noise
on the data and templates. We implement and test a pixel-based scheme for
computing the likelihood function of cosmological parameters at low multipoles
that incorporates foreground subtraction of noisy data.Comment: 20 pages, 10 figure
CMB map restoration
Estimating the cosmological microwave background is of utmost importance for
cosmology. However, its estimation from full-sky surveys such as WMAP or more
recently Planck is challenging: CMB maps are generally estimated via the
application of some source separation techniques which never prevent the final
map from being contaminated with noise and foreground residuals. These spurious
contaminations whether noise or foreground residuals are well-known to be a
plague for most cosmologically relevant tests or evaluations; this includes CMB
lensing reconstruction or non-Gaussian signatures search. Noise reduction is
generally performed by applying a simple Wiener filter in spherical harmonics;
however this does not account for the non-stationarity of the noise. Foreground
contamination is usually tackled by masking the most intense residuals detected
in the map, which makes CMB evaluation harder to perform. In this paper, we
introduce a novel noise reduction framework coined LIW-Filtering for Linear
Iterative Wavelet Filtering which is able to account for the noise spatial
variability thanks to a wavelet-based modeling while keeping the highly desired
linearity of the Wiener filter. We further show that the same filtering
technique can effectively perform foreground contamination reduction thus
providing a globally cleaner CMB map. Numerical results on simulated but
realistic Planck data are provided
Toward single particle reconstruction without particle picking: Breaking the detection limit
Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray
crystallography and NMR spectroscopy as a high-resolution structural method for
biological macromolecules. In a cryo-EM experiment, the microscope produces
images called micrographs. Projections of the molecule of interest are embedded
in the micrographs at unknown locations, and under unknown viewing directions.
Standard imaging techniques first locate these projections (detection) and then
reconstruct the 3-D structure from them. Unfortunately, high noise levels
hinder detection. When reliable detection is rendered impossible, the standard
techniques fail. This is a problem especially for small molecules, which can be
particularly hard to detect. In this paper, we propose a radically different
approach: we contend that the structure could, in principle, be reconstructed
directly from the micrographs, without intermediate detection. As a result,
even small molecules should be within reach for cryo-EM. To support this claim,
we setup a simplified mathematical model and demonstrate how our
autocorrelation analysis technique allows to go directly from the micrographs
to the sought signals. This involves only one pass over the micrographs, which
is desirable for large experiments. We show numerical results and discuss
challenges that lay ahead to turn this proof-of-concept into a competitive
alternative to state-of-the-art algorithms
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