tag:blogger.com,1999:blog-8883034.post5426641309080896863..comments2017-06-27T17:14:04.272+02:00Comments on atdotde: Some DIY LIGO data analysisRobert Hellinghttps://plus.google.com/118220336522940810893noreply@blogger.comBlogger6125tag:blogger.com,1999:blog-8883034.post-45909937450383276212017-06-27T17:14:04.272+02:002017-06-27T17:14:04.272+02:00Re windowing: in frequency space this corresponds ...Re windowing: in frequency space this corresponds convolution with the Fourier transform of the window function. What I still don't understand how convolving a function of random phases can yield something phase aligned. Robert Hellinghttps://www.blogger.com/profile/06634377111195468947noreply@blogger.comtag:blogger.com,1999:blog-8883034.post-30428854564242764772017-06-27T16:37:56.900+02:002017-06-27T16:37:56.900+02:00About the article "On the time lags of the LI...About the article "On the time lags of the LIGO signals", I found myself quite in awe after reading how they come to the same conclusions as I did a year ago when I wrote a similar analysis, performing an statistical simulation using R language and finding out signal could be perfectly noise-induced. <br /><br />https://www.linkedin.com/pulse/ligo-gravitational-waves-real-just-noise-juan-ignacio-p%C3%A9rez-sacrist%C3%A1n<br /><br />Best regards!<br />ComputerPhysics Labhttps://www.blogger.com/profile/12781862229716895972noreply@blogger.comtag:blogger.com,1999:blog-8883034.post-15179317140664001072017-06-27T15:59:23.507+02:002017-06-27T15:59:23.507+02:00For the record, the mistake is not windowing the d...For the record, the mistake is not windowing the data before Fourier transforming it: try e.g. a Hann window.Thomas Denthttps://www.blogger.com/profile/16355010444546331416noreply@blogger.comtag:blogger.com,1999:blog-8883034.post-83010164447583876062017-06-15T19:19:10.511+02:002017-06-15T19:19:10.511+02:00"Is it completely raw (except for been down-s..."Is it completely raw (except for been down-sampled to 4096 Hz)?"<br /><br />That's how I understand the LIGO open science web page. I take that data set as is.<br /><br />"You then mention that since the signal is real, Fourier transform has a phase of 0 or pi for a constant phase<br />and you find out that it is pi. I don't know what this tell us. What frequency does the region of constant phase correspond to?"<br /><br />The phase should be random (as it is outside the low frequency region: The dots come at all colors). What is the frequency? You can do the math yourself (and I am too lazy) but given that is the region of low noise I would expect it to be up to a few 100Hz.<br /><br />" Why do you consider h(t) + time-reversed h(t) (containing the signal) noise? <br />Only h(t) containing the signal+LIGO noise -best fit signal template would be noise?<br />But I don't think you are doing that. So I don't understand how an addition of the original signal+ its time-reversal is noise. Also I don't see a cancellation at other frequencies."<br /><br />The signal has orders of magnitude less power than the noise. So not taking out the signal does not really matter.<br /><br />The effect is strongest for the dataset I mentioned.<br /><br />And your time analysis friend is right: Adding to random noise the time reversal should yield noise that is sqrt(2) times stronger (except at omega=0).Robert Hellinghttps://www.blogger.com/profile/06634377111195468947noreply@blogger.comtag:blogger.com,1999:blog-8883034.post-6461691813910510912017-06-15T18:55:05.503+02:002017-06-15T18:55:05.503+02:00I asked someone who has extensively dealt with tim...I asked someone who has extensively dealt with time-series analysis about this and that person said<br /><br />"Time reversing and subtracting a noisy time series should only cancel out 1 sample (for white noise) and increase the variance (by a factor of 2) for the rest. I see from the plot that the noise is canceled over a much longer time (?) before the variance increases. Could be an effect of colored noise thoughâ€¦ I would expect the cancellation to be over the auto-correlation timescale (which is 1 sample for white noise but longer for colored noise + lines). Shantanuhttps://www.blogger.com/profile/16322812456382858228noreply@blogger.comtag:blogger.com,1999:blog-8883034.post-39528952682534453342017-06-15T18:49:41.408+02:002017-06-15T18:49:41.408+02:00There are several things that I have not understoo...There are several things that I have not understood in what you did or why your results are surprising. Following are my questions.<br /><br />o Just to be clear.<br />when you say "raw strain data at 4096 Hz, I presume you mean h(t) timeseries containing GW signal + residual LIGO noise right?<br />Is it completely raw (except for been down-sampled to 4096 Hz)?<br /><br />o You then mention that since the signal is real, Fourier transform has a phase of 0 or pi for a constant phase<br />and you find out that it is pi. I don't know what this tell us. What frequency does the region of constant phase correspond to?<br />(Maybe you can show one plot with frequency in (Hz) on X-axis instead of frequency bin number).<br /><br />o Why do you consider h(t) + time-reversed h(t) (containing the signal) noise? <br />Only h(t) containing the signal+LIGO noise -best fit signal template would be noise?<br />But I don't think you are doing that. So I don't understand how an addition of the original signal+ its time-reversal is noise. Also I don't see a cancellation at other frequencies.<br /><br />oAlso have you repeated the same exercise for Hanford? Do you get the same results?<br />I guess at any rate the best way to resolve this through some toy numerical experiments with injecting a mock signal in mock time series containing white noise (and also doing the same with colored noise)<br />Shantanuhttps://www.blogger.com/profile/16322812456382858228noreply@blogger.com