HIGH-THRESHOLD ANALYSIS: DATA SELECTION
3.4 Event Based Quality cuts
Pre-Pulse cuts (cPstd_v53andcQstd_v53)
If an event has a pre-pulse baseline that is greater than 5σfrom the series mean we get rid of it.
Randoms (∼cRandom_V53)
At the start and end of each run 500 random traces are taken and processed as normal events. There are also a smattering of such random traces taken during every run. These are used in many aspects of the analysis, from studying the noise behavior of our detectors, to constructing templates for the optimal filter algorithm.
Given that they pass through our normal event reconstruction pipeline it is possible that a random event may reconstruct as an actual event or at least something event- like enough to be trouble. We explicitly remove the randoms to avoid this.
Reconstructed Quantity Outlier Rejection Cuts (cGoodKSTest_V53_HT and cGoodQSTest_V53_HT)
Finally, the last time-period cuts are the Kolgorov-Smirnov (KS) and Charge Stabil- ity specific Kolgorov-Smirnov (QS) tests which act as a safety check on the overall results, removing the outlier series. While offline analysis shifts and those responsi- ble for assembling the list of series to process do a good job at marking and filtering out bad series from the analysis, there are still periods of subtle time variation in detector behavior or environmental effects that could affect the result. The standard CDMS approach to protect against this is the use of KS test to look for outlier series in the data by comparing distributions of key quantities. For more on this see [64].
(a) Ionizationχ2[65] (b) Phononχ2[66]
Figure 3.5: Both the ionization (a) and phonon (b) χ2 distributions suffer from flaring at high energy. To preserve these otherwise well-reconstructed events, each distribution is cut using a quadratic rather than a simple rectilinear selection.
Ionizationχ2(cQChiSq_V53)
From section 2.2.7 the OF algorithm reconstructs events by fitting a template to the pulse and extracting an amplitude. Pulses are digitized with 4096 samples. If we were to construct Pearson’s cumulativeχ2test statistic between the fit template and our data, we would expect it to have follow aχ2distribution with 4095 d.o.f.4. As can be seen in figure 3.5a, this is only true for low energy interactions. For high energy events this is not true. As it turns out, our templates systematically have worse fits as energy increases. Our rule of thumb is any event with a Pearson’s cumulativeχ2 test statistic that is greater than 3σabove the mean of the red band in figure 3.5a is removed. To do this the theµ+3σpoint is found for each energy bin, and the resulting points are fit to a quadratic. Every event above this line is removed. It should be noted that this is preformed on side-summed trace fits, so there are two of these goodness-of-fit tests per detector.
Phononχ2(cPChiSq_V53)
Similar to the Ionizationχ2cut, we also preform a goodness-of-fit cut on the phonon reconstruction. This is done in a very similar manner to the Ionizationχ2cut, where for each energy bin the µ+ 3σ point is fit to a quadratic everything above it is removed. This test is preformed on the summed phonon energy, and is only done once per detector. See figure 3.5b
4One degree of freedom is subtracted for the amplitude fit.
(a) (b)
Figure 3.6: The OF glitch cut removes a population of events characterized by very short fall-times. This is done by constructing templates for both a normal event and a so-called glitch event (a) preforming the OF fit and selecting all events that reconstruct as more signal-like than glitch-like (b) [67, 68].
Optima Filter Glitch Cut (cGlitch1_V53)
Occasionally, in the phonon readout channels, there are pulses with very short (∼100 µs) fall-times. These are consistent with TES ETF relaxation time, and it is believed that glitches in the TES biasing voltage cause them. To remove these
“glitch events” we turn to the OF. To remove these events we built a template of them, and then run the reconstruction algorithm using the glitch as the template. By comparing theχ2 goodness-of-fit between the pulse and both templates (signal vs glitch) we can accept events that are more signal-like and reject events that have a better fit to the glitch template. This process can be seen in figure 3.6.
Low Frequency Noise Cut (cLFnoise1_V53)
Very low-frequency noise5, can reconstruct in such a way that it looks like a nuclear- recoil interaction. It can also cause the energy of an actual interaction event to reconstruct incorrectly, if they occur simultaneously. This is dealt with identically to the OF glitch cut. We build a new LF-noise template and see if the OF fit to this noise template is better than the OF fit to the normal phonon template. This is shown in figure 3.7
5Which we believe is primarily due to the e-stem cryocooler.
(a) (b)
Figure 3.7: The LF noise cut is very similar to the glitch cut described in figure 3.6.
An LF template is constructed (a) and events that reconstruct as more LF-noise than true event are rejected (b). [69, 51]
Good Start Time (cGoodPStartTime_v53)
During the OF fit, the start time of a pulse is allowed to float within a window around the trigger time. Ideally an event would be reconstructed near the center of the window. Some low energy pulses will have rise-times that are slow enough that the true start time of the pulse falls outside this window, causing the fit to “rail”.
Similarly, due to the high event rate in some calibration datasets, “cross-detector pileup” events can occur that “rail” at the tail end of the OF search window. This occurs when the secondary trigger is issued just after the OF search window. These are excluded by ensuring that
(EPhonontotal > Emin)∩(−190µs<tOF <tthreshold) (3.1) Where Emin and tthreshold are detector-dependent thresholds. For more on this cut see [70].