244 research outputs found
Thermal Conduction and Multiphase Gas in Cluster Cores
We examine the role of thermal conduction and magnetic fields in cores of
galaxy clusters through global simulations of the intracluster medium (ICM). In
particular, we study the influence of thermal conduction, both isotropic and
anisotropic, on the condensation of multiphase gas in cluster cores. Previous
hydrodynamic simulations have shown that cold gas condenses out of the hot ICM
in thermal balance only when the ratio of the cooling time () and
the free-fall time () is less than . Since thermal
conduction is significant in the ICM and it suppresses local cooling at small
scales, it is imperative to include thermal conduction in such studies. We find
that anisotropic (along local magnetic field lines) thermal conduction does not
influence the condensation criterion for a general magnetic geometry, even if
thermal conductivity is large. However, with isotropic thermal conduction cold
gas condenses only if conduction is suppressed (by a factor )
with respect to the Spitzer value.Comment: 7 pages, 4 figures; replaced by the MNRAS-accepted versio
Practical Bayesian optimization in the presence of outliers
Inference in the presence of outliers is an important field of research as
outliers are ubiquitous and may arise across a variety of problems and domains.
Bayesian optimization is method that heavily relies on probabilistic inference.
This allows outstanding sample efficiency because the probabilistic machinery
provides a memory of the whole optimization process. However, that virtue
becomes a disadvantage when the memory is populated with outliers, inducing
bias in the estimation. In this paper, we present an empirical evaluation of
Bayesian optimization methods in the presence of outliers. The empirical
evidence shows that Bayesian optimization with robust regression often produces
suboptimal results. We then propose a new algorithm which combines robust
regression (a Gaussian process with Student-t likelihood) with outlier
diagnostics to classify data points as outliers or inliers. By using an
scheduler for the classification of outliers, our method is more efficient and
has better convergence over the standard robust regression. Furthermore, we
show that even in controlled situations with no expected outliers, our method
is able to produce better results.Comment: 10 pages (2 of references), 6 figures, 1 algorith
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