Parque Nacional Archipiélago de Cabrera (Islas Baleares).

Raw event data acquired by the DAQ software is first stored on local disks on the DAQ cluster. This is automatically backed up to tape as well as transferred by a dedicated gigabit link to larger storage devices at the surface building. A cluster of computers at the surface building processes a copy of this raw data, with very rough calibration, to provide preliminary reduced datasets that the operators can verify experiment performance with. Separately, the raw data are transferred over the internet to Fermilab for complete data processing on the FermiGrid cluster, to generate reduced datasets that end users can analyze to search for WIMPs. This is described in the next chapter.

Chapter 6

WIMP-search Analysis Pipeline

In this chapter, I review the analysis pipeline for WIMP-search data acquired in CDMS-II germanium detectors between July 2007 and September 2008. These data were taken in four discrete runs of the dilution fridge, numbered 125, 126, 127, and 128. Interruptions between runs were for cryogenic maintenance. Care was taken to account for small differences in run conditions between the four data-taking periods. Data from Runs 123 and 124, the first data with the full complement of 5 towers of ZIP detectors were analyzed and presented in [72] and [132].

I first briefly recap the first- and second-tier data processing mentioned in previous chapters, in the context of the analysis pipeline. Then, I outline the process of blinding the signal region, selecting “good” WIMP-search data and setting reconstruction and physics cuts. I devote a complete section to the implementation of the surface-event rejection cut, in which I played a key role. Finally I review the efficiency and WIMP-search exposure for this analysis. The results of the analysis are the subject of the next chapter. The work of a large number of collaborators made this analysis pipeline possible, including that of former CDMS graduate students and postdocs. As much as possible, I try to directly cite CDMS internal notes and call out names of key contributors to ideas or work that were not my own, so that readers have a paper trail for the evolution of this pipeline.

6.1

Data-Processing Pipeline

After acquisition using the DAQ, the data undergo several stages of data processing on Fermilab’s computing cluster before it is used for analysis [133].

1. First-tier processing: During first-tier processing, raw data, consisting of detector and trigger settings, event traces, veto activity, etc., are converted into reduced quantities (RQs). These have unphysical, uncalibrated units until the second-tier processing is performed. For Runs 125–128, a first-tier processing package in MATLAB calledDarkPipewas retired in favor of a streamlined, C++ package calledBatRoot. BatRoot combined all the old algorithms with new time-domain fitting routines from an alternate pipeline called PipeFitter. The advantage

of the new package was its modularity in adding new experimental algorithms, and its ability to produce RQs in ROOT Ntuple format [134]. The latter was done with the objective of eventually transitioning CDMS data storage format to ntuples, a better standard for handling large datasets than MATLAB’s data format. However, the full transition would have required conversion of all data-processing software to support this format, which could not be achieved in time for the analysis of Runs 125–128.

2. Second-tier processing: During second-tier processing, the charge and phonon RQs undergo calibration resulting in physically meaningful quantities called relational RQs (RRQs). The position dependence in charge is removed at this stage, making it ready for use. The phonon calibration is only preliminary at this stage. Also, the blinding procedure, described in Section 6.2, is performed for WIMP-search data. For Runs 125–128, a MATLAB package called PipeCleaner was used for these calibrations, since the C++ version called BatCalib was not ready for use. SuperCDMS Soudan has already transitioned to an integrated C++ data- reduction pipeline using BatRoot and BatCalib.

3. Lookup table generation: This process is described at length in Chapter 4 and Appendix A. Lookup tables for Runs 125–128 were generated usingCorrTools, a MATLAB package that I wrote.

4. Application of lookup table correction: The phonon energy and timing parameters of events in all datasets underwent phonon-pulse-shape correction using the lookup tables for Runs 125–128. This was done with a CorrTools plugin for PipeCleaner.

5. Assembly of calibrated, corrected dataset for end users: The pipeline, as it stood at the end of Runs 125–128, produced a combination of MATLAB and ROOT datafiles, which were then converted to independent datasets on both platforms to allow users freedom in choosing an analysis platform. Additionally, as cuts were developed, smaller skimmed datasets were produced for both platforms to allow faster loading and shorter operation times. Cuts were produced in MATLAB and ported to ROOT using a standardized boolean format. In the future, there will be a common standard to define cuts on both platforms.

A schematic of this data-processing pipeline is provided in Figure 6.1. Eventually, all of its components will be transitioned to C++ packages that produce ROOT datasets. The standard set of MATLAB tools used for analysis of CDMS datasets, calledCDMS Analysis Package (CAP) [135], has already been modified to directly read ROOT n-tuples for SuperCDMS Soudan.