Research Report
ICRR Inter-University Research Program 2020
Research Subject:(F08) Eccentric Binary Black Hole Waveform Template(F09) Gravitational Wave Trigger Based on Deep Learning
Principal Investigator:(F08) Zhoujian Cao(F09) Fan Zhang

Participating Researchers:
Shichao Wu, He Wang, Xiaolin Liu, Zong-Hong Zhu, Jiang-Yang Zhu, Lijin Shao

Summary of Research Result：(F08) Eccentric Binary Black Hole Waveform Template
In this project, we publish two papers:
1) Measuring the eccentricity of binary black holes in GWTC-1 by using theinspiral-only waveformIn this article, we estimate the eccentricity of 10 binary black holes (BBHs) in GWTC-1 byusing the inspiral-only BBH waveform template EccentricFD. First, we test our methodwith simulated eccentric BBHs. Afterwards we apply the method to real BBH gravitational wave
data. We find that the BBHs in GWTC-1, with the exception of GW151226, GW170608
and GW170729, show very small eccentricity. Their upper limits on eccentricity range
from 0.033–0.084 with 90% credible interval at a reference frequency of 10 Hz. ForGW151226, GW170608 and GW170729, the upper limits are higher than 0.1. The relatively
large eccentricity of GW151226 and GW170729 is probably due to ignoring χeff and the low
signal-to-noise ratio, and GW170608 is worthy of follow-up research. We also point out thelimitations of the inspiral-only non-spinning waveform template in eccentricity measurement.
Measurement of BBH eccentricity helps us to understand its formation mechanism. With anincrease in the number of BBH gravitational-wave events and a more complete eccentric BBHwaveform template, this will become a viable method in the near future.

2) Validating the effective-one-body numerical-relativity waveform models for spinaligned binary black holes along eccentric orbits

Effective-one-body (EOB) numerical-relativity (NR) waveform models for spin-aligned binary
black holes (BBHs), known as the SEOBNR waveform models, are based on the EOB theoreticalframework and NR simulations. SEOBNR models have played an important role in the LIGO
Scientific Collaboration (LSC) gravitational wave (GW) data analysis for both signal search andparameter estimation. SEOBNR models for quasicircular orbits have evolved through version 1 toversion 4 by extending their validity domain and including more NR results. Along anotherdirection, we recently extended the SEOBNRv1 model to the SEOBNRE model which is valid forspin-aligned BBH coalescence along eccentric orbits. In this paper we validate this theoretical
waveform model by comparing them against the NR simulation bank, simulating extreme
spacetimes (SXS) catalog. In total, 278 NR waveforms are investigated which include binaries withlarge eccentricity; large spin and large mass ratio. Our SEOBNRE can model the NR waveforms
quite well. The fitting factor for most of the 278 waveforms is larger than 99%. It indicates that theSEOBNRE model could be used as template waveforms for eccentric spin-aligned BBHcoalescence. Moreover, we investigate the limitation in using circular BBH waveform templates inthe Advanced LIGO and Einstein Telescope era.

(F09) Gravitational Wave Trigger Based on Deep Learning
In this project, we publish one paper:1) Gravitational-wave signal recognition of LIGO data by deep learning

The deep learning method has developed very fast as a tool for data analysis in recent years.
Moreover, as a technique, it is quite promising as a way to analyze gravitational-wave detection
data. Multiple works in the literature have already used deep learning to process simulated
gravitational-wave data. In this paper, we apply deep learning to LIGO data. In order to improve
the weak signal recognition, we design a new structure of the convolutional neural network (CNN).
The key feature of our new CNN structure is the sensing layer. This layer mimics matched filtering
but is different from the usual matched-filtering technique. Usually, the matched-filtering techniqueuses a full template bank to match the data. However, our sensing layer only uses tens ofwaveforms. Our new convolutional neural network admits comparable accuracy and efficiency ofsignal recognition compared to other deep learning works published in the literature. Based on ournew CNN, we can clearly recognize the 11 confirmed gravitational-wave events included in O1and O2. In addition, we find about 2000 gravitational-wave triggers in O1 data.

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