70,279 research outputs found
Probability density estimation of photometric redshifts based on machine learning
Photometric redshifts (photo-z's) provide an alternative way to estimate the
distances of large samples of galaxies and are therefore crucial to a large
variety of cosmological problems. Among the various methods proposed over the
years, supervised machine learning (ML) methods capable to interpolate the
knowledge gained by means of spectroscopical data have proven to be very
effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric
Redshifts) is a novel method designed to provide a reliable PDF (Probability
density Function) of the error distribution of photometric redshifts predicted
by ML methods. The method is implemented as a modular workflow, whose internal
engine for photo-z estimation makes use of the MLPQNA neural network (Multi
Layer Perceptron with Quasi Newton learning rule), with the possibility to
easily replace the specific machine learning model chosen to predict photo-z's.
After a short description of the software, we present a summary of results on
public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison
with a completely different method based on Spectral Energy Distribution (SED)
template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
784995
Stacking dependence of carrier transport properties in multilayered black phosphorous
We present the effect of different stacking orders on carrier transport
properties of multi-layer black phosphorous. We consider three different
stacking orders AAA, ABA and ACA, with increasing number of layers (from 2 to 6
layers). We employ a hierarchical approach in density functional theory (DFT),
with structural simulations performed with Generalized Gradient Approximation
(GGA) and the bandstructure, carrier effective masses and optical properties
evaluated with the Meta-Generalized Gradient Approximation (MGGA). The carrier
transmission in the various black phosphorous sheets was carried out with the
non-equilibrium Greens function (NEGF) approach. The results show that ACA
stacking has the highest electron and hole transmission probabilities. The
results show tunability for a wide range of band-gap, carrier effective masses
and transmission with a great promise for lattice engineering (stacking order
and layers) in black phosphorous.Comment: 18 Pages , 10 figure
Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies
Despite the high accuracy of photometric redshifts (zphot) derived using
Machine Learning (ML) methods, the quantification of errors through reliable
and accurate Probability Density Functions (PDFs) is still an open problem.
First, because it is difficult to accurately assess the contribution from
different sources of errors, namely internal to the method itself and from the
photometric features defining the available parameter space. Second, because
the problem of defining a robust statistical method, always able to quantify
and qualify the PDF estimation validity, is still an open issue. We present a
comparison among PDFs obtained using three different methods on the same data
set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate
PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution
template fitting method, BPZ. The photometric data were extracted from the KiDS
(Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained
from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical
evaluation of both individual and stacked PDFs was done through quantitative
and qualitative estimators, including a dummy PDF, useful to verify whether
different statistical estimators can correctly assess PDF quality. We conclude
that, in order to quantify the reliability and accuracy of any zphot PDF
method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure
Precision cluster mass determination from weak lensing
Weak gravitational lensing has been used extensively in the past decade to
constrain the masses of galaxy clusters, and is the most promising
observational technique for providing the mass calibration necessary for
precision cosmology with clusters. There are several challenges in estimating
cluster masses, particularly (a) the sensitivity to astrophysical effects and
observational systematics that modify the signal relative to the theoretical
expectations, and (b) biases that can arise due to assumptions in the mass
estimation method, such as the assumed radial profile of the cluster. All of
these challenges are more problematic in the inner regions of the cluster,
suggesting that their influence would ideally be suppressed for the purpose of
mass estimation. However, at any given radius the differential surface density
measured by lensing is sensitive to all mass within that radius, and the
corrupted signal from the inner parts is spread out to all scales. We develop a
new statistic that is ideal for estimation of cluster masses because it
completely eliminates mass contributions below a chosen scale (which we suggest
should be about 20 per cent of the virial radius), and thus reduces sensitivity
to systematic and astrophysical effects. We use simulated and analytical
profiles to quantify systematic biases on the estimated masses for several
standard methods of mass estimation, finding that these can lead to significant
mass biases that range from ten to over fifty per cent. The mass uncertainties
when using our new statistic are reduced by up to a factor of ten relative to
the standard methods, while only moderately increasing the statistical errors.
This new method of mass estimation will enable a higher level of precision in
future science work with weak lensing mass estimates for galaxy clusters.Comment: 27 pages, 7 figures, submitted to MNRAS; v2 has expanded explanation
for clarity, no change in results or conclusion
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