966 research outputs found
A Virtual Data Grid for LIGO
GriPhyN (Grid Physics Network) is a large US collaboration to
build grid services for large physics experiments, one of which is LIGO, a
gravitational-wave observatory. This paper explains the physics and computing
challenges of LIGO, and the tools that GriPhyN will build to address
them. A key component needed to implement the data pipeline is a virtual
data service; a system to dynamically create data products requested during
the various stages. The data could possibly be already processed in a certain
way, it may be in a file on a storage system, it may be cached, or it may need
to be created through computation. The full elaboration of this system will al-low
complex data pipelines to be set up as virtual data objects, with existing
data being transformed in diverse ways
A First Comparison Between LIGO and Virgo Inspiral Search Pipelines
This article reports on a project that is the first step the LIGO Scientific
Collaboration and the Virgo Collaboration have taken to prepare for the mutual
search for inspiral signals. The project involved comparing the analysis
pipelines of the two collaborations on data sets prepared by both sides,
containing simulated noise and injected events. The ability of the pipelines to
detect the injected events was checked, and a first comparison of how the
parameters of the events were recovered has been completed.Comment: GWDAW-9 proceeding
Data Access for LIGO on the OSG
During 2015 and 2016, the Laser Interferometer Gravitational-Wave Observatory
(LIGO) conducted a three-month observing campaign. These observations delivered
the first direct detection of gravitational waves from binary black hole
mergers. To search for these signals, the LIGO Scientific Collaboration uses
the PyCBC search pipeline. To deliver science results in a timely manner, LIGO
collaborated with the Open Science Grid (OSG) to distribute the required
computation across a series of dedicated, opportunistic, and allocated
resources. To deliver the petabytes necessary for such a large-scale
computation, our team deployed a distributed data access infrastructure based
on the XRootD server suite and the CernVM File System (CVMFS). This data access
strategy grew from simply accessing remote storage to a POSIX-based interface
underpinned by distributed, secure caches across the OSG.Comment: 6 pages, 3 figures, submitted to PEARC1
SciTokens: Capability-Based Secure Access to Remote Scientific Data
The management of security credentials (e.g., passwords, secret keys) for
computational science workflows is a burden for scientists and information
security officers. Problems with credentials (e.g., expiration, privilege
mismatch) cause workflows to fail to fetch needed input data or store valuable
scientific results, distracting scientists from their research by requiring
them to diagnose the problems, re-run their computations, and wait longer for
their results. In this paper, we introduce SciTokens, open source software to
help scientists manage their security credentials more reliably and securely.
We describe the SciTokens system architecture, design, and implementation
addressing use cases from the Laser Interferometer Gravitational-Wave
Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey
Telescope (LSST) projects. We also present our integration with widely-used
software that supports distributed scientific computing, including HTCondor,
CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for
capability-based secure access to remote scientific data. The access tokens
convey the specific authorizations needed by the workflows, rather than
general-purpose authentication impersonation credentials, to address the risks
of scientific workflows running on distributed infrastructure including NSF
resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds
(e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the
interoperability and security of scientific workflows, SciTokens 1) enables use
of distributed computing for scientific domains that require greater data
protection and 2) enables use of more widely distributed computing resources by
reducing the risk of credential abuse on remote systems.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery
We present a novel computational framework that connects Blue Waters, the
NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser
Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science
Grid technology. To enable this computational infrastructure, we configured,
for the first time, a LIGO Data Grid Tier-1 Center that can submit
heterogeneous LIGO workflows using Open Science Grid facilities. In order to
enable a seamless connection between the LIGO Data Grid and Blue Waters via
Open Science Grid, we utilize Shifter to containerize LIGO's workflow software.
This work represents the first time Open Science Grid, Shifter, and Blue Waters
are unified to tackle a scientific problem and, in particular, it is the first
time a framework of this nature is used in the context of large scale
gravitational wave data analysis. This new framework has been used in the last
several weeks of LIGO's second discovery campaign to run the most
computationally demanding gravitational wave search workflows on Blue Waters,
and accelerate discovery in the emergent field of gravitational wave
astrophysics. We discuss the implications of this novel framework for a wider
ecosystem of Higher Performance Computing users.Comment: 10 pages, 10 figures. Accepted as a Full Research Paper to the 13th
IEEE International Conference on eScienc
Best network chirplet-chain: Near-optimal coherent detection of unmodeled gravitation wave chirps with a network of detectors
The searches of impulsive gravitational waves (GW) in the data of the
ground-based interferometers focus essentially on two types of waveforms: short
unmodeled bursts and chirps from inspiralling compact binaries. There is room
for other types of searches based on different models. Our objective is to fill
this gap. More specifically, we are interested in GW chirps with an arbitrary
phase/frequency vs. time evolution. These unmodeled GW chirps may be considered
as the generic signature of orbiting/spinning sources. We expect quasi-periodic
nature of the waveform to be preserved independent of the physics which governs
the source motion. Several methods have been introduced to address the
detection of unmodeled chirps using the data of a single detector. Those
include the best chirplet chain (BCC) algorithm introduced by the authors. In
the next years, several detectors will be in operation. The joint coherent
analysis of GW by multiple detectors can improve the sight horizon, the
estimation of the source location and the wave polarization angles. Here, we
extend the BCC search to the multiple detector case. The method amounts to
searching for salient paths in the combined time-frequency representation of
two synthetic streams. The latter are time-series which combine the data from
each detector linearly in such a way that all the GW signatures received are
added constructively. We give a proof of principle for the full sky blind
search in a simplified situation which shows that the joint estimation of the
source sky location and chirp frequency is possible.Comment: 22 pages, revtex4, 6 figure
Improving the efficiency of the detection of gravitational wave signals from inspiraling compact binaries: Chebyshev interpolation
Inspiraling compact binaries are promising sources of gravitational waves for
ground and space-based laser interferometric detectors. The time-dependent
signature of these sources in the detectors is a well-characterized function of
a relatively small number of parameters; thus, the favored analysis technique
makes use of matched filtering and maximum likelihood methods. Current analysis
methodology samples the matched filter output at parameter values chosen so
that the correlation between successive samples is 97% for which the filtered
output is closely correlated. Here we describe a straightforward and practical
way of using interpolation to take advantage of the correlation between the
matched filter output associated with nearby points in the parameter space to
significantly reduce the number of matched filter evaluations without
sacrificing the efficiency with which real signals are recognized. Because the
computational cost of the analysis is driven almost exclusively by the matched
filter evaluations, this translates directly into an increase in computational
efficiency, which in turn, translates into an increase in the size of the
parameter space that can be analyzed and, thus, the science that can be
accomplished with the data. As a demonstration we compare the present "dense
sampling" analysis methodology with our proposed "interpolation" methodology,
restricted to one dimension of the multi-dimensional analysis problem. We find
that the interpolated search reduces by 25% the number of filter evaluations
required by the dense search with 97% correlation to achieve the same
efficiency of detection for an expected false alarm probability. Generalized to
higher dimensional space of a generic binary including spins suggests an order
of magnitude increase in computational efficiency.Comment: 23 pages, 5 figures, submitted to Phys. Rev.
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