20,778 research outputs found
A Multi-Code Analysis Toolkit for Astrophysical Simulation Data
The analysis of complex multiphysics astrophysical simulations presents a
unique and rapidly growing set of challenges: reproducibility, parallelization,
and vast increases in data size and complexity chief among them. In order to
meet these challenges, and in order to open up new avenues for collaboration
between users of multiple simulation platforms, we present yt (available at
http://yt.enzotools.org/), an open source, community-developed astrophysical
analysis and visualization toolkit. Analysis and visualization with yt are
oriented around physically relevant quantities rather than quantities native to
astrophysical simulation codes. While originally designed for handling Enzo's
structure adaptive mesh refinement (AMR) data, yt has been extended to work
with several different simulation methods and simulation codes including Orion,
RAMSES, and FLASH. We report on its methods for reading, handling, and
visualizing data, including projections, multivariate volume rendering,
multi-dimensional histograms, halo finding, light cone generation and
topologically-connected isocontour identification. Furthermore, we discuss the
underlying algorithms yt uses for processing and visualizing data, and its
mechanisms for parallelization of analysis tasks.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical
Journal Supplement Series with revisions from referee. yt can be found at
http://yt.enzotools.org
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
The Theoretical Astrophysical Observatory: Cloud-Based Mock Galaxy Catalogues
We introduce the Theoretical Astrophysical Observatory (TAO), an online
virtual laboratory that houses mock observations of galaxy survey data. Such
mocks have become an integral part of the modern analysis pipeline. However,
building them requires an expert knowledge of galaxy modelling and simulation
techniques, significant investment in software development, and access to high
performance computing. These requirements make it difficult for a small
research team or individual to quickly build a mock catalogue suited to their
needs. To address this TAO offers access to multiple cosmological simulations
and semi-analytic galaxy formation models from an intuitive and clean web
interface. Results can be funnelled through science modules and sent to a
dedicated supercomputer for further processing and manipulation. These modules
include the ability to (1) construct custom observer light-cones from the
simulation data cubes; (2) generate the stellar emission from star formation
histories, apply dust extinction, and compute absolute and/or apparent
magnitudes; and (3) produce mock images of the sky. All of TAO's features can
be accessed without any programming requirements. The modular nature of TAO
opens it up for further expansion in the future.Comment: 17 pages, 11 figures, 2 tables; accepted for publication in ApJS. The
Theoretical Astrophysical Observatory (TAO) is now open to the public at
https://tao.asvo.org.au/. New simulations, models and tools will be added as
they become available. Contact [email protected] if you have data you
would like to make public through TAO. Feedback and suggestions are very
welcom
Recommended from our members
Design, implementation and testing of an integrated branch and bound algorithm for piecewise linear and discrete programming problems within an LP framework
A number of discrete variable representations are well accepted and find regular use within LP systems. These are Binary variables, General Integer variables, Variable Upper Bounds or Semi Continuous variables, Special Ordered Sets of type One and type Two. The FortLP system has been extended to include these representations. A Branch and Bound algorithm is designed in which the choice of sub-problems and branching variables are kept general. This provides considerable scope of experimentation with tree development heuristics and the tree search can then be guided by search parameters specified by user subroutines. The data structures for representing the variables and the definition of the branch and bound tree are described. The results of experimental investigation for a few test problems are reported
The Lov\'asz Hinge: A Novel Convex Surrogate for Submodular Losses
Learning with non-modular losses is an important problem when sets of
predictions are made simultaneously. The main tools for constructing convex
surrogate loss functions for set prediction are margin rescaling and slack
rescaling. In this work, we show that these strategies lead to tight convex
surrogates iff the underlying loss function is increasing in the number of
incorrect predictions. However, gradient or cutting-plane computation for these
functions is NP-hard for non-supermodular loss functions. We propose instead a
novel surrogate loss function for submodular losses, the Lov\'asz hinge, which
leads to O(p log p) complexity with O(p) oracle accesses to the loss function
to compute a gradient or cutting-plane. We prove that the Lov\'asz hinge is
convex and yields an extension. As a result, we have developed the first
tractable convex surrogates in the literature for submodular losses. We
demonstrate the utility of this novel convex surrogate through several set
prediction tasks, including on the PASCAL VOC and Microsoft COCO datasets
Tangos: the agile numerical galaxy organization system
We present Tangos, a Python framework and web interface for database-driven
analysis of numerical structure formation simulations. To understand the role
that such a tool can play, consider constructing a history for the absolute
magnitude of each galaxy within a simulation. The magnitudes must first be
calculated for all halos at all timesteps and then linked using a merger tree;
folding the required information into a final analysis can entail significant
effort. Tangos is a generic solution to this information organization problem,
aiming to free users from the details of data management. At the querying
stage, our example of gathering properties over history is reduced to a few
clicks or a simple, single-line Python command. The framework is highly
extensible; in particular, users are expected to define their own properties
which tangos will write into the database. A variety of parallelization options
are available and the raw simulation data can be read using existing libraries
such as pynbody or yt. Finally, tangos-based databases and analysis pipelines
can easily be shared with collaborators or the broader community to ensure
reproducibility. User documentation is provided separately.Comment: Clarified various points and further improved code performance;
accepted for publication in ApJS. Tutorials (including video) at
http://tiny.cc/tango
Multi-Agent Search for a Moving and Camouflaging Target
In multi-agent search planning for a randomly moving and camouflaging target,
we examine heterogeneous searchers that differ in terms of their endurance
level, travel speed, and detection ability. This leads to a convex
mixed-integer nonlinear program, which we reformulate using three linearization
techniques. We develop preprocessing steps, outer approximations via lazy
constraints, and bundle-based cutting plane methods to address large-scale
instances. Further specializations emerge when the target moves according to a
Markov chain. We carry out an extensive numerical study to show the
computational efficiency of our methods and to derive insights regarding which
approach should be favored for which type of problem instance
Exact solutions to a class of stochastic generalized assignment problems
This paper deals with a stochastic Generalized Assignment Problem with recourse. Only a random subset of the given set of jobs will require to be actually processed. An assignment of each job to an agent is decided a priori, and once the demands are known, reassignments can be performed if there are overloaded agents. We construct a convex approximation of the objective function that is sharp at all feasible solutions. We then present three versions of an exact algorithm to solve this problem, based on branch and bound techniques, optimality cuts, and a special purpose lower bound. numerical results are reported.
Orbitopal Fixing
The topic of this paper are integer programming models in which a subset of
0/1-variables encode a partitioning of a set of objects into disjoint subsets.
Such models can be surprisingly hard to solve by branch-and-cut algorithms if
the order of the subsets of the partition is irrelevant, since this kind of
symmetry unnecessarily blows up the search tree. We present a general tool,
called orbitopal fixing, for enhancing the capabilities of branch-and-cut
algorithms in solving such symmetric integer programming models. We devise a
linear time algorithm that, applied at each node of the search tree, removes
redundant parts of the tree produced by the above mentioned symmetry. The
method relies on certain polyhedra, called orbitopes, which have been
introduced bei Kaibel and Pfetsch (Math. Programm. A, 114 (2008), 1-36). It
does, however, not explicitly add inequalities to the model. Instead, it uses
certain fixing rules for variables. We demonstrate the computational power of
orbitopal fixing at the example of a graph partitioning problem.Comment: 22 pages, revised and extended version of a previous version that has
appeared under the same title in Proc. IPCO 200
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