57 research outputs found
Photometric redshift galaxies as tracers of the filamentary network
Galaxy filaments are the dominant feature in the overall structure of the
cosmic web. The study of the filamentary web is an important aspect in
understanding galaxy evolution and the evolution of matter in the Universe. A
map of the filamentary structure is an adequate probe of the web. We propose
that photometric redshift galaxies are significantly positively associated with
the filamentary structure detected from the spatial distribution of
spectroscopic redshift galaxies. The catalogues of spectroscopic and
photometric galaxies are seen as point-process realisations in a sphere, and
the catalogue of filamentary spines is proposed to be a realisation of a random
set in a sphere. The positive association between these sets was studied using
a bivariate function, which is a summary statistics studying clustering. A
quotient was built to estimate the distance distribution of the filamentary
spine to galaxies in comparison to the distance distribution of the filamentary
spine to random points in dimensional Euclidean space. This measure gives a
physical distance scale to the distances between filamentary spines and the
studied sets of galaxies. The bivariate function shows a statistically
significant clustering effect in between filamentary spines and photometric
redshift galaxies. The quotient confirms the previous result that smaller
distances exist with higher probability between the photometric galaxies and
filaments. The trend of smaller distances between the objects grows stronger at
higher redshift. Additionally, the quotient for photometric galaxies gives
a rough estimate for the filamentary spine width of about ~Mpc. Photometric
redshift galaxies are positively associated with filamentary spines detected
from the spatial distribution of spectroscopic galaxies.Comment: Accepted to Astronomy & Astrophysics. 13 pages and 9 figure
Maximum a posteriori estimation through simulated annealing for binary asteroid orbit determination
This paper considers a new method for the binary asteroid orbit determination
problem. The method is based on the Bayesian approach with a global
optimisation algorithm. The orbital parameters to be determined are modelled
through an a posteriori distribution made of a priori and likelihood terms. The
first term constrains the parameters space and it allows the introduction of
available knowledge about the orbit. The second term is based on given
observations and it allows us to use and compare different observational error
models. Once the a posteriori model is built, the estimator of the orbital
parameters is computed using a global optimisation procedure: the simulated
annealing algorithm. The maximum a posteriori (MAP) techniques are verified
using simulated and real data. The obtained results validate the proposed
method. The new approach guarantees independence of the initial parameters
estimation and theoretical convergence towards the global optimisation
solution. It is particularly useful in these situations, whenever a good
initial orbit estimation is difficult to get, whenever observations are not
well-sampled, and whenever the statistical behaviour of the observational
errors cannot be stated Gaussian like.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical
Societ
Statistically bias-minimized peculiar velocity catalogs from Gibbs point processes and Bayesian inference
Galaxy peculiar velocities are excellent cosmological probes provided that
biases inherent to their measurements are contained before any study. This
paper proposes a new algorithm based on an object point process model whose
probability density is built to statistically reduce the effects of Malmquist
biases and uncertainties due to lognormal errors in radial peculiar velocity
catalogs. More precisely, a simulated annealing algorithm permits maximizing
the probability density describing the point process model. The resulting
configurations are bias-minimized catalogs. Tests are conducted on synthetic
catalogs mimicking the second and third distance modulus catalogs of the
Cosmicflows project from which peculiar velocity catalogs are derived. By
reducing the local peculiar velocity variance in catalogs by an order of
magnitude, the algorithm permits recovering the expected one while preserving
the small-scale velocity correlation. It also permits retrieving the expected
clustering. The algorithm is then applied to the observational catalogs. The
large-scale structure reconstructed with the Wiener-filter technique applied to
the bias-minimized observational catalogs matches with great success the local
cosmic web as depicted by redshift surveys of local galaxies. These new
bias-minimized versions of peculiar velocity catalogs can be used as a starting
point for several studies from possibly estimating the most probable Hubble
constant, H0, value to the production of simulations constrained to reproduce
the local Universe.Comment: Accepted for publication in A&A, 26 pages, 22 figures, 3 table
HUG model: an interaction point process for Bayesian detection of multiple sources in groundwaters from hydrochemical data
This paper presents a new interaction point process that integrates
geological knowledge for the purpose of automatic sources detection of multiple
sources in groundwaters from hydrochemical data. The observations are
considered as spatial data, that is a point cloud in a multi-dimensional space
of hydrogeochemical parameters. The key hypothesis of this approach is to
assume the unknown sources to be the realisation of a point process. The
probability density describing the sources distribution is built in order to
take into account the multi-dimensional character of the data and specific
physical rules. These rules induce a source configuration able to explain the
observations. This distribution is completed with prior knowledge regarding the
model parameters distributions. The composition of the sources is estimated by
the configuration maximising the joint proposed probability density. The method
was first calibrated on synthetic data and then tested on real data from
hydrothermal systems
Gibbsian T-tessellation model for agricultural landscape characterization
A new class of planar tessellations, named T-tessellations, was introduced in
([10]). A model was proposed to be considered as a completely random
T-tessellation model (CRTT) and its Gibbsian variants were discussed. A general
simulation algorithm of Metropolis-Hastings-Green type was derived for model
simulation, involving three local transformations of T-tessellations. The
current paper focuses on statistical inference for Gibbs models of
T-tessellations. Statistical methods originated from point pattern analysis are
implemented on the example of three agricultural landscapes approximated by
T-tessellations. The choice of model statistics is guided by their capacity to
highlight the differences between the landscape patterns. Model parameters are
estimated by Monte Carlo Maximum Likelihood method, yielding a baseline for
landscapes comparison. In the last part of the paper a global envelope test
based on the empty-space function is proposed for assessing the goodness-of-fit
of the model
Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures
International audienceHydrogeochemical data may be seen as a point cloud in a multi-dimensional space. Each dimension of this space represents a hydrogeochemical parameter (i.e. salinity, solute concentration, concentration ratio, isotopic composition...). While the composition of many geological fluids is controlled by mixing between multiple sources, a key question related to hydrogeochemical data set is the detection of the sources. By looking at the hydrogeochemical data as spatial data, this paper presents a new solution to the source detection problem that is based on point processes. Results are shown on simulated and real data from geothermal fluids
Double Negative (CD3+4−8−) TCRαβ Splenic Cells from Young NOD Mice Provide Long-Lasting Protection against Type 1 Diabetes
-reactive T-cells. Herein, we analyzed the function and phenotype of DNCD3 splenic cells in young NOD mice predisposed to several autoimmune disorders among which, the human-like autoimmune diabetes. with a predominant Vβ13 gene usage.T-regulatory cells. DNCD3 splenic cells could be potentially manipulated towards the development of autologous cell therapies in autoimmune diabetes
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