ausblenden:
Schlagwörter:
General Relativity and Quantum Cosmology, gr-qc, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,Computer Science, Learning, cs.LG
Zusammenfassung:
We present a novel Machine Learning (ML) based strategy to search for compact
binary coalescences (CBCs) in data from ground-based gravitational wave (GW)
observatories. This is the first ML-based search that not only recovers all the
binary black hole mergers in the first GW transients calalog (GWTC-1), but also
makes a clean detection of GW151216, which was not significant enough to be
included in the catalogue. Moreover, we achieve this by only adding a new
coincident ranking statistic (MLStat) to a standard analysis that was used for
GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental
transients, which create a loud noise background by triggering numerous false
alarms, is crucial to improving the sensitivity for detecting true events. The
sheer volume of data and and large number of expected detections also prompts
the use of ML techniques. We perform transfer learning to train "InceptionV3",
a pre-trained deep neural network, along with curriculum learning to
distinguish GW signals from noisy events by analysing their continuous wavelet
transform (CWT) maps. MLStat incorporates information from this ML classifier
into the standard coincident search likelihood used by the conventional search.
This leads to at least an order of magnitude improvement in the inverse
false-alarm-rate (IFAR) for the previously "low significance" events GW151012,
GW170729 and GW151216. The confidence in detection of GW151216 is further
strengthened by performing its parameter estimation using SEOBNRv4HM_ROM.
Considering the impressive ability of the statistic to distinguish signals from
glitches, the list of marginal events from MLStat could be quite reliable for
astrophysical population studies and further follow-up. This work demonstrates
the immense potential and readiness of MLStat for finding new sources in
current data and possibility of its adaptation in similar searches.
