During the S5 run there were a handful of data quality flags set automatically by DMT monitors (written by John Zweizig):
| S5 Flag name (click for details) |
| ASC_Overflow |
| ASI_CORR_OVERFLOW |
| H1_Not_Locked |
| H2_Not_Locked |
| Injection |
| LSC_OVERFLOW |
| PD_Overflow |
| SEVERE_LSC_OVERFLOW |
| Wind_Over_30MPH |
(A complete list of S5 offline DQ flags (defined to date) with documentation links can be found at http://gallatin.physics.lsa.umich.edu/~keithr/S5DQ/flaginfo.html.)
Many of these tasks can be combined together into DMT monitors or cron scripts. Here is one possible grouping:
In addition, there are several S5 flags defined by general "glitchiness" of DARM_ERR, based on qualitative offline assessment:
| Flag name (click for details) |
Method used | Investigator(s) |
| AUTOBURT_GLITCHES | Examining Fourier transforms of KleineWelle trigger rates | Erik Katsavounidis |
| BADRANGE_GLITCHINESS EARTHQUAKE_GLITCHINESS ELEVATED_GLITCHINESS HURRICANE_GLITCHINESS SEVERE_GLITCHINESS SPOB_GLITCHINESS | Examining inspiral range fluctuations | Gaby Gonzalez |
Although it would be difficult to mimic setting these criteria faithfully in an online DMT monitor, it would be desirable to flag periods of glitchiness already recognized in the control via measures of non-stationarity, such as the BurstMon pixel fraction. Hence it probably makes sense to enhance BurstMon to create DQ segments on the fly. The same may be true for the Station monitor.
Similarly, it would make sense to run KleineWelle online (on a relatively small set of channels) and create DQ flags based on high rates of triggers with high significance, especially given how useful KleineWelle triggers have proven in defining many S5 DQ flags described above.
K. Riles - December 7, 2007