http://www.ligo.org/news/index.php#gravity-spy LATEST NEWS Gravity Spy, a crowdsourcing tool for finding and analyzing glitches in LIGO data, has been publicly launched today. Glitches, or noise, in the LIGO data are a byproduct of very high sensitivity of LIGO instruments. The presence of these non-gravitational-wave disturbances in the data can obscure or mimic true gravitational-wave signals. The origin of some glitches is well-understood, while others remain a mystery. The rates at which the glitches occur vary depending on what's going on with the detectors and their environments. At their highest rates, glitches happen at 3x/sec. At such rates and with more than 2 dozen types of glitches observed so far, it takes an enourmous amount of data processing to sort out and classify them. To facilitate this process, the Gravity Spy tool is crowdsourcing the glitch identification to citizen scientists. With each new classification, LIGO will move closer and closer to discovering new gravitational-wave signals by identifying possible noise patterns in its data and filtering them out. Read more, and sign up, at the Gravity Spy website. The Gravity Spy tool is a result of collaborative efforts of several LSC groups. The Gravity Spy team consists of LIGO researchers at the Center for Interdisciplinary Exploration and Research in Astronomy (CIERA) at Northwestern University, LIGO researchers at Caltech, machine learning researchers at Northwestern University, crowd-sourced science researchers at Syracuse University, and Zooniverse web developers. - See more at: http://www.ligo.org/news/index.php#gravity-spy ABOUT GRAVITY SPY: Ever had a moment when you thought your computer was smarter than you? Well, now is your chance to teach your computer a thing or two! On September 14th 2015, a century after Einstein predicted the existence of ripples in spacetime known as gravitational waves, the Laser Interferometer Gravitational-wave Observatory (LIGO) made the first direct detection of this elusive phenomenon. This discovery is the first of many that will give us a whole new way to explore our Universe. However, LIGO needs your help! Being the most sensitive and most complicated gravitational experiment ever created, LIGO is susceptible to a great deal of instrumental and environmental sources of noise called glitches. These glitches are difficult to model using computers, can mimic true astrophysical signals, and generally make LIGO less sensitive to gravitational waves. By selecting the right classification for a given glitch, you are helping computers learn to do this classification themselves on much larger datasets, which helps scientists determine and eliminate the sources of noise. Humans still are far better than computers at recognizing subtle differences across images and when an image simply does not fit within a known category. Please help us identify all of the glitch morphologies and open up an even bigger window into the gravitational wave universe! Gravity Spy is funded by the NSF INSPIRE 1547880 grant. This material is based upon work supported by the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Science Foundation. https://www.zooniverse.org/projects/zooniverse/gravity-spy/
A compelling theory of gravity wave would, among other items on the shopping list, be able to explain the magnitude of noise in the power spectrum of the channel, if the receiver / antenna system is at all selective or directional, or of the entire open power bandwidth if it is not. Until this is accomplished, looking for other candidate GW signals buried in the noise is an exercise in futility. I understand, this is how the first GWs were discovered. This does not necessarily mean this methodology is going to continue to be optimal, or at least, I hope that it isn't. Parasitic oscillations in early radio receivers is a good example of how this could go wrong, and instrumentation artifacts in this very young field could easily be mistaken for actual signals. It is to the credit of the team that they did not announce success until they identified the largest amplitude and rich featured signals to analyze until or unless something more remarkable is detected, identified and analyzed. From the descriptions of possible noise sources, this does not resemble a worthwhile use of available computing resources. I hope this program is able to make at least a rudimentary approximation to the GW spectrum power. Once that is accomplished with the system in maximal sensitivity, candidate signals should become easier to identify. Running it as a double blind experiment should not be necessary to continue indefinitely.
