Incorporating information from LIGO data quality streams into the PyCBC search for gravitational waves

نویسندگان

چکیده

We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time PyCBC search gravitational waves from compact binary coalescences. use information LIGO data quality streams that monitor status each and its environment to model rate detector. These allow candidates identified during periods malfunctions be more efficiently rejected as noise. This allows machine learning predictions state included part search, increasing total number detectable signals by up 5%. When both classifications manually-generated flags are used LIGO-Virgo's third observing run, is increased 20% compared not using any streams. also show how this flexible enough include large numbers additional arbitrary may able further increase sensitivity search.

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ژورنال

عنوان ژورنال: Physical review

سال: 2022

ISSN: ['0556-2813', '1538-4497', '1089-490X']

DOI: https://doi.org/10.1103/physrevd.106.102006