3.4 LIGO and Virgo detectors’ noise
4.1.3 Detector characterization and glitch examples
Detector characterizationis the process of looking at the auxiliary channels in conjunction with the GW channel — with the goal of identifying not only the times when the detector is not functioning properly, but
also the causes of the problems so that they might be fixed. Often, the problems cannot be mitigated during the course of a science run, in which case the “bad” data are vetoed. Section 4.2 is devoted to the vetoing procedure. The immediately following subsections illustrate the work done by the Detector Characterization group by presenting the stories of several families of glitches.
4.1.3.1 Grid glitches
Grid glitches (only found in H1) are characterized by a distinctive shape in a plot produced by the Omega analysis (see Section 4.1.2) of the GW channel, as seen in Figure 4.6. The grid structure on the plot is an artifact of the time-frequency tiling of the Omega algorithm’s implementation (see Figure 4.4), but it also a clue as to the possible source of the glitches, as it indicates a stochastic and broad-band noise. Coincident with periods of grid glitches are times of abnormal readings in the quadrant photodiodes in the output mode cleaner (see Figure 3.3 for the locations of these photodiodes, and Figure 4.7 and Figure 4.8 for the Omega analysis on these photodiodes).
In the end, it was found to be an electronics glitch somewhere near the output mode cleaner, and a resoldering of the piezoelectric tower’s power supply eliminated grid glitches in the future.
Figure 4.6: An Omega-gram indicates the time-frequency tiles with excess power in the GW channel; the pattern is characteristic of the grid glitches described in Section 4.1.3.1. Each blue dot is an event found with SNR>5, each green dot is an event found with SNR>10, and each red dot is an event found with SNR>
20. Image courtesy of the Detector Characterization group Wiki page https://wiki.ligo.org/DetChar/H1GridGlitches.
4.1.3.2 Flip glitches
The flip glitch was given its name because of the distinctive shape of the glitch in the GW channel in time- frequency space. For example, see Figure 4.9, which was also created with the Omega algorithm. Although
Figure 4.7: An Omega-gram indicates the time-frequency tiles with excess power in the output mode cleaner’s QPD1 SUM channel, at the same time as the grid glitches seen in Figure 4.6. Each blue dot is an event found with SNR > 5, each green dot is an event found with SNR> 10, and each red dot is an event found with SNR > 20. Image courtesy of the Detector Characterization group Wiki page https://wiki.ligo.org/DetChar/H1GridGlitches.
Figure 4.8: An Omega-gram indicates the time-frequency tiles with excess power in the output mode cleaner’s QPD4 SUM channel, at the same time as the grid glitches seen in Figure 4.6. Each blue dot is an event found with SNR > 5, each green dot is an event found with SNR> 10, and each red dot is an event found with SNR > 20. Image courtesy of the Detector Characterization group Wiki page https://wiki.ligo.org/DetChar/H1GridGlitches.
members of the flip glitch family share the same shape in the GW channel, they do not have a consistent correlation to the same auxiliary channels. Sometimes they are accompanied by a glitch in an auxiliary channel sensor in the output mode cleaner (shown in Figure 3.10), but other times the output mode cleaner auxiliary channels are clean and the auxiliary channels measuring the Michelson or power-recycling cavity
degrees of freedom show excess power. As explained in the following chapter, since we are only allowed to look at auxiliary channels when removing glitchy data from the analysis for fear of vetoing an astrophysical GW burst signal, it is difficult to veto glitches like this where the only identifying features are in the GW channel.
The output mode cleaner caused problems unique to S6. Unlike glitches from environmental or instru- mental sources outside of the OMC, glitches originating in the OMC are not always recorded by multiple auxiliary channels. Since the photodetector used to record GW data are on the same optical table as the OMC subsystem, some glitches in the OMC will only be recorded in the GW channel.
Figure 4.9: This is a Q-scan, also produced by the Omega algorithm. In this plot, the sine-Gaussian decom- position has been whitened and smoothed to emphasize the kinds of glitches seen in LIGO data. Shown here is a Q-scan illustrating a particularly loud example of a flip glitch seen in the GW channel. Figure courtesy of the Detector Characterization group Wiki page https://wiki.ligo.org/DetChar/CurrentGlitchesL1Flip.
4.1.3.3 Upconversion noise
There are many sources of seismic noise, from distant Earthquakes producing noise in the .01 - 1 Hz band to anthropogenic sources producing noise in the 10-30 Hz band. Although the low-frequency cutoff is 40 Hz for LIGO detectors, seismic noise sources still have a considerable effect due to upconversion. The upconversion is thought to be the result of the seismic motion moving electromagnetic components, which causes a Barkhausen effect (discontinuous jumps in flux density of a ferromagnet despite a continuous change of the external magnetic field [87]) in the magnets glued to the mirrors to control their position and angular degrees of freedom [85].
Figure 4.10: The whitened time-domain signal of the flip glitch shown in Figure 4.9. Although not evident at fist glance, a ringdown shape can be seen starting at 0.2 s.
Seismometers are good at measuring the absolute level of seismic activity in their sensitive band. How- ever, upconversion noise is due to spikes above baseline activity; this means that it is difficult to look at their readout and deduce if upconversion noise is a problem at that time. Section 4.2.1.2 describes a method used to veto glitches of this sort.
4.1.3.4 Spike Glitches
Occasionally, there are common and loud glitches for which no explanation can be found. The spike glitch, found only in L1, falls into this category. When the glitch is very loud, as seen in the GW channel, a spike shape is seen in the channel monitoring the sum of the photodiodes in the output mode cleaner (see Figure 4.1 and Figure 4.11). However, this channel is sensitive to GWs, and thus should not be used to identify glitchy times. No other channel or combination of channels could be found to have correlations with spike glitches. During a few particularly bad weeks of data, a veto was created using a matched-filter for this shape in the GW channel, despite this being a potentially dangerous (GW self-veto) procedure (see Section 4.2.1.2).