127,903 research outputs found
Simulating rare events using a Weighted Ensemble-based string method
We introduce an extension to the Weighted Ensemble (WE) path sampling method
to restrict sampling to a one dimensional path through a high dimensional phase
space. Our method, which is based on the finite-temperature string method,
permits efficient sampling of both equilibrium and non-equilibrium systems.
Sampling obtained from the WE method guides the adaptive refinement of a
Voronoi tessellation of order parameter space, whose generating points, upon
convergence, coincide with the principle reaction pathway. We demonstrate the
application of this method to several simple, two-dimensional models of driven
Brownian motion and to the conformational change of the nitrogen regulatory
protein C receiver domain using an elastic network model. The simplicity of the
two-dimensional models allows us to directly compare the efficiency of the WE
method to conventional brute force simulations and other path sampling
algorithms, while the example of protein conformational change demonstrates how
the method can be used to efficiently study transitions in the space of many
collective variables
Reducing the number of templates for aligned-spin compact binary coalescence gravitational wave searches using metric-agnostic template nudging
Efficient multi-dimensional template placement is crucial in computationally
intensive matched-filtering searches for Gravitational Waves (GWs). Here, we
implement the Neighboring Cell Algorithm (NCA) to improve the detection volume
of an existing Compact Binary Coalescence (CBC) template bank. This algorithm
has already been successfully applied for a binary millisecond pulsar search in
data from the Fermi satellite. It repositions templates from over-dense regions
to under-dense regions and reduces the number of templates that would have been
required by a stochastic method to achieve the same detection volume. Our
method is readily generalizable to other CBC parameter spaces. Here we apply
this method to the aligned--single-spin neutron-star--black-hole binary
coalescence inspiral-merger-ringdown gravitational wave parameter space. We
show that the template nudging algorithm can attain the equivalent
effectualness of the stochastic method with 12% fewer templates
Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
Calibrating the photometric redshifts of >10^9 galaxies for upcoming weak
lensing cosmology experiments is a major challenge for the astrophysics
community. The path to obtaining the required spectroscopic redshifts for
training and calibration is daunting, given the anticipated depths of the
surveys and the difficulty in obtaining secure redshifts for some faint galaxy
populations. Here we present an analysis of the problem based on the
self-organizing map, a method of mapping the distribution of data in a
high-dimensional space and projecting it onto a lower-dimensional
representation. We apply this method to existing photometric data from the
COSMOS survey selected to approximate the anticipated Euclid weak lensing
sample, enabling us to robustly map the empirical distribution of galaxies in
the multidimensional color space defined by the expected Euclid filters.
Mapping this multicolor distribution lets us determine where - in galaxy color
space - redshifts from current spectroscopic surveys exist and where they are
systematically missing. Crucially, the method lets us determine whether a
spectroscopic training sample is representative of the full photometric space
occupied by the galaxies in a survey. We explore optimal sampling techniques
and estimate the additional spectroscopy needed to map out the color-redshift
relation, finding that sampling the galaxy distribution in color space in a
systematic way can efficiently meet the calibration requirements. While the
analysis presented here focuses on the Euclid survey, similar analysis can be
applied to other surveys facing the same calibration challenge, such as DES,
LSST, and WFIRST.Comment: ApJ accepted, 17 pages, 10 figure
Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for black-box functions
with an environmental variable whose value determines the tradeoff between
evaluation cost and the fidelity of the evaluations. Further, we use a novel
approach to sampling support points, allowing faster construction of the
acquisition function. This allows us to achieve optimization with lower
overheads than previous approaches and is implemented for a more general class
of problem. We show this approach to be effective on synthetic and real world
benchmark problems.Comment: 8 pages, 7 figure
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