He Wang PRO
Knowledge increases by sharing but not by saving.
- We reproduced a same state-of-the-art result (Green et al. 2008.03312) for GW150914 and applied the formulism to the other GW events.
Normalized flow model and how we pre-processing the data will be explained by pedagogy.(may be omitted) Some on-going works are also mentioned. - I have to explain this in Chinese for clarity...🙏
Statistical inference with normalized flow for BBHs in O1/O2
He Wang
First demonstration of early warning gravitational wave alerts. (2102.04555)
Motivation
- Higher, faster, stronger \(\Rightarrow\) More, Better, Faster
Motivation
Parameter Estimation Bias From Overlapping Binary Black Hole Events In Second Generation Interferometers. (2103.16225)
- Can we find overlapping GW events at the same time?
"If the signals are greater than ~0.1 seconds apart they can be distinguished separately.
Less than this and the second signal will cause significant bias."
\begin{aligned}
10 \mathrm{M}_{\odot} \leq & m_{i} \leq 80 \mathrm{M}_{\odot} \\
100 \mathrm{Mpc} \leq & d_{L} \leq 1000 \mathrm{Mpc}
\end{aligned}
GW150914
Green et al. [2002.07656, 2008.03312] (MAF, CVAE+, Flow-based)
- optimal results for now
Results
- We can reproduce the complete parameter inference result for GW150914.
\begin{aligned}
10 \mathrm{M}_{\odot} \leq & m_{i} \leq 80 \mathrm{M}_{\odot} \\
100 \mathrm{Mpc} \leq & d_{L} \leq 1000 \mathrm{Mpc}
\end{aligned}
Results
GW150914
- But not perfect....
GW151012
GW151226
\begin{aligned}
3 \mathrm{M}_{\odot} \leq & m_{i} \leq 80 \mathrm{M}_{\odot} \\
100 \mathrm{Mpc} \leq & d_{L} \leq 1000 \mathrm{Mpc}
\end{aligned}
\begin{aligned}
5 \mathrm{M}_{\odot} \leq & m_{i} \leq 80 \mathrm{M}_{\odot} \\
100 \mathrm{Mpc} \leq & d_{L} \leq 2000 \mathrm{Mpc}
\end{aligned}
Results
- Even worse on other events in O1
GW170104
Results
GW170823
- Even worse on other events in O1 and O2
Results
- Even worse on other events in O1 and O2
- We need more understanding and exploration on the model.
- Objective 1: A Model trained on a event can estimate the event well. (current)
- Objective 2: A Model trained can estimate all the events well.
Trained from GW151226, testing on GW150914
\text{PSD}_{det}
1024 sec
8 sec
ref_time
GPS time
6 sec
\text{PSD}_{det}
1024 sec
8 sec
ref_time
GPS time
6 sec
\text{PSD}_{det}
1024 sec
8 sec
ref_time
GPS time
6 sec
Training
\text{PSD}_{det}
1024 sec
8 sec
ref_time
GPS time
6 sec
Training
\text{PSD}_{det}
1024 sec
8 sec
ref_time
GPS time
6 sec
Testing
On-going works (1/2)
GW150914, SNR = 36.30
- It the outputs from the model sharing the same behavior like MCMC?
On-going works (1/2)
GW150914, SNR = 36.30
- It the outputs from the model sharing the same behavior like MCMC?
- YES!
On-going works (2/2)
- It the outputs from the model sharing the same behavior like MCMC?
- YES!
- How much lower achievable SNR for the model?
- Comparing with MCMC...
Expected
(The Final Slice)
Statistical inference with normalized flow for BBHs in O1/O2
By He Wang
Statistical inference with normalized flow for BBHs in O1/O2
We reproduced a same state-of-the-art result (Green et al. 2008.03312) for GW150914 and applied the formulism to the other GW events. Normalized flow model and how we pre-rocessing the data will be explained by pedagogy. (omitted) Some on-going works are also mentioned. I have to explain this in Chinese for clarity...🙏
