141 research outputs found
A new veto for continuous gravitational wave searches
We present a new veto procedure to distinguish between continuous
gravitational wave (CW) signals and the detector artifacts that can mimic their
behavior. The veto procedure exploits the fact that a long-lasting coherent
disturbance is less likely than a real signal to exhibit a Doppler modulation
of astrophysical origin. Therefore, in the presence of an outlier from a
search, we perform a multi-step search around the frequency of the outlier with
the Doppler modulation turned off (DM-off), and compare these results with the
results from the original (DM-on) search. If the results from the DM-off search
are more significant than those from the DM-on search, the outlier is most
likely due to an artifact rather than a signal. We tune the veto procedure so
that it has a very low false dismissal rate. With this veto, we are able to
identify as coherent disturbances >99.9% of the 6349 candidates from the recent
all-sky low-frequency Einstein@Home search on the data from the Advanced LIGO
O1 observing run [1]. We present the details of each identified disturbance in
the Appendix.Comment: 10 pages, 6 figures, 2 table
An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data
Continuous gravitational waves (CW) are expected from spinning neutron stars
with non-axisymmetric deformations. A network of interferometric detectors
(LIGO, Virgo and GEO600) is looking for these signals. They are predicted to be
very weak and retrievable only by integration over long observation times. One
of the standard methods of CW data analysis is the multi-detector F-statistic.
In a typical search, the F-statistic is computed over a range in frequency,
spin-down and sky position, and the candidates with highest F values are kept
for further analysis. However, this detection statistic is susceptible to a
class of noise artifacts, strong monochromatic lines in a single detector. By
assuming an extended noise model - standard Gaussian noise plus single-detector
lines - we can use a Bayesian odds ratio to derive a generalized detection
statistic, the line veto (LV-) statistic. In the absence of lines, it behaves
similarly to the F-statistic, but it is more robust against line artifacts. In
the past, ad-hoc post-processing vetoes have been implemented in searches to
remove these artifacts. Here we provide a systematic framework to develop and
benchmark this class of vetoes. We present our results from testing this
LV-statistic on simulated data.Comment: 2 pages, 1 figure, to be published in Proceedings of Statistical
Challenges in Modern Astronomy V, Springer 201
Fully coherent follow-up of continuous gravitational-wave candidates: an application to Einstein@Home results
We characterize and present the details of the follow-up method used on the
most significant outliers of the Hough Einstein@Home all-sky search for
continuous gravitational waves arXiv:1207.7176. This follow-up method is based
on the two-stage approach introduced in arXiv:1303.2471, consisting of a
semicoherent refinement followed by a fully coherent zoom. We quantify the
efficiency of the follow-up pipeline using simulated signals in Gaussian noise.
This pipeline does not search beyond first-order frequency spindown, and
therefore we also evaluate its robustness against second-order spindown. We
present the details of the Hough Einstein@Home follow-up (arXiv:1207.7176) on
three hardware-injected signals and on the 8 most significant outliers of
unknown origin.Comment: 8 pages, 3 figures, 3 table
Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates
Broad searches for continuous gravitational wave signals rely on hierarchies
of follow-up stages for candidates above a given significance threshold. An
important step to simplify these follow-ups and reduce the computational cost
is to bundle together in a single follow-up nearby candidates. This step is
called clustering and we investigate carrying it out with a deep learning
network. In our first paper [1], we implemented a deep learning clustering
network capable of correctly identifying clusters due to large signals. In this
paper, a network is implemented that can detect clusters due to much fainter
signals. These two networks are complementary and we show that a cascade of the
two networks achieves an excellent detection efficiency across a wide range of
signal strengths, with a false alarm rate comparable/lower than that of methods
currently in use
Results from the First All-Sky Search for Continuous Gravitational Waves from Small-Ellipticity Sources
We present the results of an all-sky search for continuous gravitational-wave signals with frequencies in the 500-1700 Hz range targeting neutron stars with ellipticity of 10-8. The search is done on LIGO O2 data using the Falcon analysis pipeline. The results presented here double the sensitivity over any other result on the same data [B. P. Abbott (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. D 100, 024004 (2019)PRVDAQ2470-001010.1103/PhysRevD.100.024004, C. Palomba, Phys. Rev. Lett. 123, 171101 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.171101]. The search is capable of detecting low-ellipticity sources up to 170 pc. We establish strict upper limits which hold for worst-case signal parameters. We list outliers uncovered by the search, including several which we cannot associate with any known instrumental cause. © 2020 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Open access publication funded by the Max Planck Society
Sensitivity improvements in the search for periodic gravitational waves using O1 LIGO data
We demonstrate a breakthrough in the capabilities of robust, broad-parameter
space searches for continuous gravitational waves. With a large scale search
for continuous gravitational waves on the O1 LIGO data, we prove that our
Falcon search achieves the sensitivity improvements expected from the use of a
long coherence length, while maintaining the computational expense within
manageable bounds. On this data we set the most constraining upper limits in
the gravitational wave amplitude in the band 100-200 Hz. We provide full
outlier lists and upper limits near 0-spindown band suitable for analysis of
signals with small spindown such as boson condensates around black holes.Comment: Updated paper titl
Adaptive clustering procedure for continuous gravitational wave searches
In hierarchical searches for continuous gravitational waves, clustering of
candidates is an important postprocessing step because it reduces the number of
noise candidates that are followed-up at successive stages [1][7][12]. Previous
clustering procedures bundled together nearby candidates ascribing them to the
same root cause (be it a signal or a disturbance), based on a predefined
cluster volume. In this paper, we present a procedure that adapts the cluster
volume to the data itself and checks for consistency of such volume with what
is expected from a signal. This significantly improves the noise rejection
capabilities at fixed detection threshold, and at fixed computing resources for
the follow-up stages, this results in an overall more sensitive search. This
new procedure was employed in the first Einstein@Home search on data from the
first science run of the advanced LIGO detectors (O1) [11].Comment: 11 pages, 9 figures, 2 tables; v1: initial submission; v2: journal
review, copyedited version; v3: fixed typo in Fig
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