Naresh Adhikari

42:03

sure

Petra Tang

56:32

in a bounded binary system, does kick velocity still play a role during merging

jkanner@caltech.edu

57:08

yes. the “kick” comes from momentum lost to gravitational waves

Petra Tang

59:51

is the "kick" you meant the same kick during the pre-supernova?

Sylvia Biscoveanu

01:00:35

No, the kick that is imparted by the supernova is the “natal kick”. The final merger remnant also gets a kick from the conversation of angular momentum after the merger

jkanner@caltech.edu

01:01:35

for example: https://arxiv.org/abs/1802.04276

Petra Tang

01:02:06

cool thank you :D

rcayuso

01:07:26

I was wondering if you could expand on how deviations from GR searches are performed in LIGO.

Abhishek MS

01:07:29

why won't there is a head on collapseing,suppose a small BH is sucked to a galactic center..would there be a prturbation or wobling make GWS

Felipe Meza

01:08:08

Thanks for sharing the presentation Prof. Alan…It will be a good idea to have a folder in GitHub with all presentations, lots of details and additional resources sources in there. Thanks!

Sylvia Biscoveanu

01:09:14

@Abhishek Yes, when a small black hole merges with a galactic center, this is called an extreme mass ratio inspiral. These types of systems emit GW’s at much lower frequencies than those probed by LIGO, and will be detectable by LISA, the space-based detector

rcayuso

01:11:36

Could you expand on how LIGO performs deviations from GR searches?

Sylvia Biscoveanu

01:14:35

LIGO performs a number of tests of General relativity including looking for the alternative polarizations that Alan mentioned, calculating the speed of gravitational waves since they should travel at the speed of light if GR is correct, consistency tests looking at the estimated source parameters using different parts of the waveform in isolation like the inspiral and merger/ringdown, and also looking for deviations from the post-newtonian parameters used the describe the inspiral part of the waveform

Plamen Fiziev

01:14:38

And what are present days observational .results, even in low confidence level

Sylvia Biscoveanu

01:15:20

Here is a good reference for the recent tests of GR that LIGO has performed https://arxiv.org/abs/1903.04467

Gregory Harry

01:15:58

Peter Saulson answers this question here

Gregory Harry

01:16:00

https://aapt.scitation.org/doi/10.1119/1.18578

Plamen Fiziev

01:16:38

I know this paper. It seems to me that in it we already see deviation at low confidence level, but there are no such comments at all...

Abhishek MS

01:17:18

stationary on earth but still moving with a enormous around milkyway

Gregory Harry

01:17:28

pdf version: https://pdfs.semanticscholar.org/393a/af6b1ced305ee40d175d5f3c3a2b6020348d.pdf

Raymond Yeung

01:19:37

I have a question: if derivation of waveforms is supposed to be hard (e.g. needs numerical relativity), how could we easily generate them with libraries like SEOBNR, Phenom etc.?

Raymond Yeung

01:20:06

IMRPhenom*

rcayuso

01:20:07

Thank you Sylvia and Gregory

Sylvia Biscoveanu

01:22:42

The libraries like SEOB and Phenom are approximations based on the results of numerical relativity. Even with these approximations, the waveforms are extremely expensive to compute.

Alan Weinstein

01:25:11

Phenom is much less expensive than SEOB. The SEOB waveforms are computed in the time domain by solving coupled differential eons, then you FFT them to get the freq domain waveform. Phenom, and NRSur, are computed quickly in the frequency domain

Santanu Ganguly

01:26:17

Many thanks Prof Weinstein!

Alan Weinstein

01:26:28

The difficulty comes when “walking” through parameter space in an MCMC. There, you need to compute the waveforms maybe a million times. It can take hours or days.

Raymond Yeung

01:26:54

Thank you all for answering. Just curious how long the developers of these libraries spent to develop them…

Alan Weinstein

01:27:17

Many, many person years of effort. Hundreds of papers…

Raymond Yeung

01:29:58

I see. Sometimes it feels complicated to use these products right away, without knowing much about their inner workings… ^^”

Petra Tang

01:42:17

is applying matched filter limited to LIGO events?

Abhishek MS

01:43:41

can we take time corresponds to the peak in SNR as merger time

Alan Weinstein

01:44:16

@Petra Matched filtering was developed in the 40’s for radar and (many, many) other applications for finding weak signals in noisy data. Your brain uses it to pick up “Hey Petra!” in a crowded noisy party (pre-covid).

rcayuso

01:44:37

How does LIGO deal with the "Look elsewhere effect" when considering such a big parameter spce for the search?

Alan Weinstein

01:45:52

@Abhishek our matched filtering algorithm reports the time in terms of the “coalescence time” of the waveform template, which we *conventionally” define as the peak of the waveform template.

Gregory Harry

01:45:53

Sorry, I need to drop off for a bit

Petra Tang

01:46:28

@Alan, thank you, so can we use matched filter for the stochastic GWBG?

Alan Weinstein

01:46:35

@rcayruso Background estimation, as Derek will describe soon

rcayuso

01:47:08

Thank you Alan

Alan Weinstein

01:47:57

@Petra for stochastic, we don’t have a “template”. Instead, we cross-correlate data from a pair of detectors (eg, LIGO Hanford and LIGO Livingston). The data from one detector is the stochastic “template” for the data from the other detector. So yes, it is a form of matched filtering.

Alan Weinstein

01:51:25

@Petra All of the math for our (all sky) stochastic searches are in our many papers on the subject. Eg, our very first stochastic paper from 2003 - https://arxiv.org/pdf/gr-qc/0312088.pdf

Petra Tang

01:52:11

@Alan perfect thank you

Alan Weinstein

01:52:12

All developed by Joe Romano and Bruce Allen, more than 20 years ago

Alan Weinstein

01:56:47

@rcayuso the plots Derek is showing now take into account the “look elsewhere” effect associated with 500,000 matched filter templates (also known as “trials factor”).

Abhishek MS

01:56:48

..thank you:)

Ezequiel

02:03:56

I’ve a question here in regards of GAN networks.

Ezequiel

02:04:15

What is the research done in this regard?

Ezequiel

02:04:22

In detecting GW?

Ezequiel

02:04:50

“Many studies”. I just want to double click here….haha.

Alan Weinstein

02:05:47

@Derek clap clap!!

Abhishek MS

02:05:47

can we use matched filter the strain data from one detector and use templet as strain data from another detector neglecting some known noice

Ezequiel

02:06:15

How may I raise my hand?

jkanner@caltech.edu

02:06:36

in “participant window”, press raise hand

Alan Weinstein

02:06:38

@Abhishek that’s essentially what unmodeled burst searches do.

Ezequiel

02:06:40

(done)

Ezequiel

02:06:57

Thank you @Jonnah.

Alan Weinstein

02:07:01

And stochastic searches

Alan Weinstein

02:08:31

@Ezekiel - MANY groups are working on this - https://phys.org/news/2018-04-machine-gravitational.html

Abhishek MS

02:08:52

if stochastic signal are in high frequency region

Abhishek MS

02:09:18

with less amplitude

Agata Trovato

02:10:28

@Ezekiel This is a recent summary of machine learning applications to GW data analysis: https://arxiv.org/pdf/2005.03745.pdf

Ezequiel

02:10:44

Thank you @Alan! I’m just working into a postgraduate thesis that’s looking to improve on some of those methods. (Is not a PhD. something simpler…)

Ezequiel

02:10:54

So, this link could be of huge help.

Alan Weinstein

02:11:13

@Ezekiel There are many papers like this one - https://inspirehep.net/literature/1792015

Ezequiel

02:11:15

(If you guys knows of any other, that could be really help full) thanks a ton!