The glitch group has undertaken a systematic search for anomalies in the data
in an almost real-time fashion. The goal of this search is to help improving
the quality of the data by warning the detector experts 'on-site' about special features in the data.
For this reason the groups has organized shifts (twice a week) and reports are
produced at the end of each shift. Furthermore a weekly summary report is
provided in order to list the open problems in each IFO, and discussed during
the weekly Run Cordination meeting.
Scimons can look at the analytical reports and the summary report published for each week and, if they want, keep on investigating on open problems they may find interesting.
After the list of links to reports, a table with the tools used for the
analysis is presented. For each analysis a brief summary should guide the
scimon on what to look for.
For more questions, please e-mail ligo-glitch(at)mit.edu or the analysis contact persons.
| Online Analysis Web-Pages | |||
|---|---|---|---|
| Analysis | Contact Person | Web-Page | |
| Block-Normal | Shantanu (PSU) | Loudest Events: look at event display time series and spectrograms to find features in the transients that can suggest their origin. Also, look for coincident transients in auxiliary and environmental channels. | active link Loudest Events link Hardware Injections link GRB link |
| Block-Normal | Keith T (PSU) | Line Findings: check new lines and their correlation to observed phenomena in the detector | Line Findings' active link |
| BurstMon | Laura (MIT) | BurstMon F.O.Ms: check stability and overall level of HRSS curves for all SineGaussians,look for anomalies in scatter plot of SNR vs frequency of pixels, check level and stability of distribution of pixel rate (at 1.0kHz should be less than 0.6) or pixel fraction (at 1.0kHz should be less than 0.8-0.9) | at LHO: active link at LLO: active link Microseismic and FOMs |
| kleineWelle | Lindy (MIT) | kleineWelle trigger trends: Look for peaks in the trends and possible correlations between channels' behavior. | Triggers link Trends link - old style Trends link - new style for LHO Trends link - new style for LLO |
| kleineWelle | Laura (MIT) | kleineWelle diagnostic plots: many plots showing rates, distributions of relevant parameters and autocorrelation between times for transients in the DARM_ERR channel | active link |
| kleineWelle | Alessandra (Syr) | kleineWelle rates and loudest minutes: Check rate per minute for DARM_ERR and look at a sample of the noisiest 5 minutes for time series and spectrograms in order to find features that could lead to an explanation of their origin. | active link |
| kleineWelle | Erik (MIT) | kleineWelle veto analysis: Comprehensive analysis of ALL channels investigated with the kleineWelle algorithm. Veto analysis done on ALL POSSIBLE combinations of DARM_ERR and ANY other auxiliary/environmental channel. | active link |
| Qmon | Shourov (CalTech) | Q scan spectrograms: produced for selected events, this is another point of view over the transients seen in DARM_ERR and their correlation with other transients in auxiliary and environmental channels. | Triggers link Qscan link |
| WaveBurst | Igor (LLO) | Brief summary | Online analysis link |
| NoiseFloorMon | Soma (UTB) | NoiseFloorMon: This page contains the results of running NoiseFloorMon on the LSC-AS_Q channel on all three LIGO interferometers during the S5 data run. NoiseFloorMon tracks slow non-stationarities present in the noise floor of the data. Noise Floor Mon works in a band limited way, tracking noise floor in four frequency bands viz. 0-20 Hz, 20-100 Hz, 100-200 Hz and 200- Hz. The following plots (click on icon to enlarge) show the percentage of points crossing the 3 \sigma threshold in the output of the Noise Floor Mon in all the frequency band and correlation of NoiseFloorMon output with that of BurstMon pixel fractions for pixels in clusters with TF volume > 2, 4 and 6 and with the 50% hrss (strain/sqrt(Hz)) for Q=9 sine-gaussians at 100, 235 and 555 Hz. The offline analysis will be further enhanced to include correlations with the SenseMon outout. | Online analysis link |