1,208 research outputs found
CMB component separation by parameter estimation
We propose a solution to the CMB component separation problem based on
standard parameter estimation techniques. We assume a parametric spectral model
for each signal component, and fit the corresponding parameters pixel by pixel
in a two-stage process. First we fit for the full parameter set (e.g.,
component amplitudes and spectral indices) in low-resolution and high
signal-to-noise ratio maps using MCMC, obtaining both best-fit values for each
parameter, and the associated uncertainty. The goodness-of-fit is evaluated by
a chi^2 statistic. Then we fix all non-linear parameters at their
low-resolution best-fit values, and solve analytically for high-resolution
component amplitude maps. This likelihood approach has many advantages: The
fitted model may be chosen freely, and the method is therefore completely
general; all assumptions are transparent; no restrictions on spatial variations
of foreground properties are imposed; the results may be rigorously monitored
by goodness-of-fit tests; and, most importantly, we obtain reliable error
estimates on all estimated quantities. We apply the method to simulated Planck
and six-year WMAP data based on realistic models, and show that separation at
the muK level is indeed possible in these cases. We also outline how the
foreground uncertainties may be rigorously propagated through to the CMB power
spectrum and cosmological parameters using a Gibbs sampling technique.Comment: 20 pages, 10 figures, submitted to ApJ. For a high-resolution
version, see http://www.astro.uio.no/~hke/docs/eriksen_et_al_fgfit.p
Spectral Line De-confusion in an Intensity Mapping Survey
Spectral line intensity mapping has been proposed as a promising tool to
efficiently probe the cosmic reionization and the large-scale structure.
Without detecting individual sources, line intensity mapping makes use of all
available photons and measures the integrated light in the source confusion
limit, to efficiently map the three-dimensional matter distribution on large
scales as traced by a given emission line. One particular challenge is the
separation of desired signals from astrophysical continuum foregrounds and line
interlopers. Here we present a technique to extract large-scale structure
information traced by emission lines from different redshifts, embedded in a
three-dimensional intensity mapping data cube. The line redshifts are
distinguished by the anisotropic shape of the power spectra when projected onto
a common coordinate frame. We consider the case where high-redshift [CII] lines
are confused with multiple low-redshift CO rotational lines. We present a
semi-analytic model for [CII] and CO line estimates based on the cosmic
infrared background measurements, and show that with a modest instrumental
noise level and survey geometry, the large-scale [CII] and CO power spectrum
amplitudes can be successfully extracted from a confusion-limited data set,
without external information. We discuss the implications and limits of this
technique for possible line intensity mapping experiments.Comment: 13 pages, 14 figures, accepted by Ap
Halo detection via large-scale Bayesian inference
We present a proof-of-concept of a novel and fully Bayesian methodology
designed to detect halos of different masses in cosmological observations
subject to noise and systematic uncertainties. Our methodology combines the
previously published Bayesian large-scale structure inference algorithm, HADES,
and a Bayesian chain rule (the Blackwell-Rao Estimator), which we use to
connect the inferred density field to the properties of dark matter halos. To
demonstrate the capability of our approach we construct a realistic galaxy mock
catalogue emulating the wide-area 6-degree Field Galaxy Survey, which has a
median redshift of approximately 0.05. Application of HADES to the catalogue
provides us with accurately inferred three-dimensional density fields and
corresponding quantification of uncertainties inherent to any cosmological
observation. We then use a cosmological simulation to relate the amplitude of
the density field to the probability of detecting a halo with mass above a
specified threshold. With this information we can sum over the HADES density
field realisations to construct maps of detection probabilities and demonstrate
the validity of this approach within our mock scenario. We find that the
probability of successful of detection of halos in the mock catalogue increases
as a function of the signal-to-noise of the local galaxy observations. Our
proposed methodology can easily be extended to account for more complex
scientific questions and is a promising novel tool to analyse the cosmic
large-scale structure in observations.Comment: 17 pages, 13 figures. Accepted for publication in MNRAS following
moderate correction
SATMC: Spectral Energy Distribution Analysis Through Markov Chains
We present the general purpose spectral energy distribution (SED) fitting
tool SED Analysis Through Markov Chains (SATMC). Utilizing Monte Carlo Markov
Chain (MCMC) algorithms, SATMC fits an observed SED to SED templates or models
of the user's choice to infer intrinsic parameters, generate confidence levels
and produce the posterior parameter distribution. Here we describe the key
features of SATMC from the underlying MCMC engine to specific features for
handling SED fitting. We detail several test cases of SATMC, comparing results
obtained to traditional least-squares methods, which highlight its accuracy,
robustness and wide range of possible applications. We also present a sample of
submillimetre galaxies that have been fitted using the SED synthesis routine
GRASIL as input. In general, these SMGs are shown to occupy a large volume of
parameter space, particularly in regards to their star formation rates which
range from ~30-3000 M_sun yr^-1 and stellar masses which range from
~10^10-10^12 M_sun. Taking advantage of the Bayesian formalism inherent to
SATMC, we also show how the fitting results may change under different
parametrizations (i.e., different initial mass functions) and through
additional or improved photometry, the latter being crucial to the study of
high-redshift galaxies.Comment: 17 pages, 11 figures, MNRAS accepte
Constraints on the solid dark universe model
If the dark energy is modelled as a relativistic elastic solid then the
standard CDM and CDM models, as well as lattice configurations of
cosmic strings or domain walls, are points in the two-dimensional parameter
space . We present a detailed analysis of the best fitting
cosmological parameters in this model using data from a range of observations.
We find that the is improved by by including the two
parameters and that the CDM model is only the best fit to the
data when a large number of different datasets are included. Using CMB
observations alone we find that and with the addition of
Large-Scale Structure data and . We conclude that the models based on topological defects provide a good
fit to the current data, although CDM cannot be ruled out.Comment: 10 page
Hierarchical Bayesian CMB Component Separation with the No-U-Turn Sampler
Key to any cosmic microwave background (CMB) analysis is the separation of
the CMB from foreground contaminants. In this paper we present a novel
implementation of Bayesian CMB component separation. We sample from the full
posterior distribution using the No-U-Turn Sampler (NUTS), a gradient based
sampling algorithm. Alongside this, we introduce new foreground modelling
approaches. We use the mean-shift algorithm to define regions on the sky,
clustering according to naively estimated foreground spectral parameters. Over
these regions we adopt a complete pooling model, where we assume constant
spectral parameters, and a hierarchical model, where we model individual
spectral parameters as being drawn from underlying hyper-distributions. We
validate the algorithm against simulations of the LiteBIRD and C-BASS
experiments, with an input tensor-to-scalar ratio of .
Considering multipoles , we are able to recover estimates
for . With LiteBIRD only observations, and using the complete pooling model,
we recover . For C-BASS and LiteBIRD observations
we find using the complete pooling model, and
using the hierarchical model. By adopting the
hierarchical model we are able to eliminate biases in our cosmological
parameter estimation, and obtain lower uncertainties due to the smaller
Galactic emission mask that can be adopted for power spectrum estimation.
Measured by the rate of effective sample generation, NUTS offers performance
improvements of over using Metropolis-Hastings to fit the complete
pooling model. The efficiency of NUTS allows us to fit the more sophisticated
hierarchical foreground model, that would likely be intractable with
non-gradient based sampling algorithms.Comment: 19 pages, 9 figure
Halo detection via large-scale Bayesian inference
We present a proof-of-concept of a novel and fully Bayesian methodology designed to detect
haloes of different masses in cosmological observations subject to noise and systematic uncertainties.
Our methodology combines the previously published Bayesian large-scale structure
inference algorithm, HAmiltonian Density Estimation and Sampling algorithm (HADES), and
a Bayesian chain rule (the BlackwellâRao estimator), which we use to connect the inferred
density field to the properties of dark matter haloes. To demonstrate the capability of our
approach, we construct a realistic galaxy mock catalogue emulating the wide-area 6-degree
Field Galaxy Survey, which has a median redshift of approximately 0.05. Application of HADES
to the catalogue provides us with accurately inferred three-dimensional density fields and corresponding
quantification of uncertainties inherent to any cosmological observation. We then
use a cosmological simulation to relate the amplitude of the density field to the probability of
detecting a halo with mass above a specified threshold. With this information, we can sum over
the HADES density field realisations to construct maps of detection probabilities and demonstrate
the validity of this approach within our mock scenario. We find that the probability of
successful detection of haloes in the mock catalogue increases as a function of the signal to
noise of the local galaxy observations. Our proposed methodology can easily be extended to
account for more complex scientific questions and is a promising novel tool to analyse the
cosmic large-scale structure in observations.
Key words: methods: numerical â methods: statistical â galaxies: haloes â galaxies: clusters
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