Data processing framework

The LUX data processing framework (DPF) extracts essential information from the raw data and saves it in output .rq binary files (“RQs” meaning “reduced quantities”). RQs are calculated and stored for each event, and include quantities at the event-level (e.g. number of identified pulses, trigger timestamp), pulse-level (e.g. area, width, type), and channel-level (pulse area fraction in a given PMT). The DPF has a modular structure: data reduction algorithms are implemented as modules, called sequentially by a Python wrapper, and incrementally add condensed information to the output .rq file. In this way, modules can be written in any programming language, and can be easily swapped out for alternate versions. In a given data processing run, the choices of which modules to use (and also what their input parameters should be) are specified in an .xml file.

The DPF will generate an ID unique to that configuration, which is saved in a MySQL database. Figure3.6 shows the organization of the LUX DPF.

Initial modules in the DPF prepare the data for RQ calculation: baselines are sub- tracted; ADC counts are converted to phd/sample, using PMT gains; PODs from different channels that overlap in time are summed into the imaginatively named SUMPODs. A pulse-finding algorithm then scans the SUMPOD data, using boxcar filters of different widths to ensure identification of both large S2s as well as tiny sphes. Pulse-level quanti- ties can now be calculated, including area, width, spike count, etc. A pulse classification algorithm then determines the type of the pulse, which can be “S1” or “S2”, but also “sphe” or “SE”, “other”, or in some algorithm versions, a merger of two pulses. This algorithm is crucial to the identification of so-called golden events, which arise predom- inantly from single-scatter interactions, the signature of a WIMP event. Gammas and neutrons often scatter more than once in the active volume, thus producing multiple S2s. Selecting for golden events, which consist of only one valid S2 following one valid S1, thus immediately rejects a significant number of background events.

Following the identification of S2 and S1 pulses, the position reconstruction module is run. This module is based on the Mercury algorithm developed for the ZEPLIN-III DM detector, another LXe TPC [117]. The LUX technique is described in the recently released Ref. [12]. Essentially, the algorithm makes use of light response functions (LRFs) and the S2 pulse area fractions in each PMT to search for the maximum likelihood (x, y) position of S2 generation. The LRFs are calculated in advance for each PMT by iterative

x (cm) y(cm) x (cm) x (cm) x (cm) 0 5 10 15 20 25 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 a) b) c) d)

Pulse Area Fract

ion 0 0.02 0.04 0.06 0.08 0.10 0.12

Figure 3.7: Illustration of position reconstruction in LUX. Panels a) and b) show the reconstructed positions of events from calibration data, obtained from an iterative fit, colored according to the fraction of S2 pulse area appearing in the PMT indicated with an “X”. Panels c) and d) show the corresponding light response functions used in the reconstruction algorithm. Taken from Ref. [12].

application of the fitting algorithm, using events from calibration data. Figure3.7shows the empirical and parametrized LRFs for two PMTs. Note that the LRFs for the higher radius PMT (panels b) and d)) exhibit radial asymmetry. This is a result of the highly reflective PTFE panels significantly altering the distribution of light. Including this effect in the LRFs represents an improvement of the LUX method over the ZEPLIN-III algorithm.

Figure 3.8a shows the reconstructed positions of uniformly distributed events se- lected from a calibration run. Interestingly, the striped pattern is not an artifact of the reconstruction, but rather a real clustering of electron extraction sites resulting from the positions of the HV grid wires. Figure 3.8b illustrates the origin of this effect: drifting electrons are bunched into narrow bands as they pass the gate grid (with wires running through the page), en route to the liquid-gas boundary, where S2 photon production be- gins. This phenomenon clearly limits the validity of the assumption that an S2 production site matches the (x, y) position of the original energy deposition. A further complication

−25 −20 −15 −10 −5 0 5 10 15 20 25 −25 −20 −15 −10 −5 0 5 10 15 20 25 x (cm) y (cm)

Figure 3.8: At left: reconstructed (x, y) positions of uniformly distributed calibration data. The stripes of high event density are the result of gate grid wires concentrating electron trajectories into tight bands, an effect shown at right. Taken from Ref. [12]. is the geometry of the drift field, which in the first LUX exposure was slightly “fringed.” Consequently, the radius of S2 production was slightly less than that of the scattering event. This effect was modeled and accounted for in the data processing, resulting in a set of corrected coordinates _{{x, y} as well as a set of raw coordinates {x}S2, yS2} (see

Table 3.1). In the second LUX data-taking run, the field geometry was more seriously distorted, requiring more careful treatment (discussed in Chap. 5).

The final major stage of data processing corrects pulse quantities for spatially varying detector efficiencies, known as “flat-fielding” in 2D imaging. In LUX, these are called “area corrections” (since we are correcting pulse areas) as opposed to the aforementioned “position corrections.” For S1 pulses, this correction normalizes the area to what would be observed (on average) if the same number of scintillation photons originated at the

Symbol Unit Description

S1 phd

Total number of detected photons (obtained from summed pulse area) corresponding to prompt scin- tillation VUV light.

S2 phd

Total number of detected photons (obtained from summed pulse area) corresponding to electrolumines- cence VUV light (proportional to charge). Sometimes notated S2raw to distinguish from S2.

S1 phd

S1, corrected for position-dependent detector efficien- cies. Geometrical light collection efficiency dominates the correction factor (see Sec. 3.4.1). Sometimes no- tated S1c or S1corr.

S1_spike phd

S1 (obtained from counting single photon peaks in PMT waveforms), corrected for position-dependent detector efficiencies.

S2 phd

S2, corrected for position-dependent detector efficien- cies. Electron lifetime dominates the correction factor (see Sec. 3.4.1). Sometimes notated S2c or S2corr.

rS2 =

{xS2, yS2, zS2} {cm, cm, µs}

Reconstructed position of interaction, in the uncor- rected S2 coordinate space. Also commonly seen in cylindrical coordinates {rS2, φS2, zS2}. Note that zS2

is the drift time, sometimes notated as tS2 or tdrift.

r =

{x, y, z} {cm, cm, cm}

Reconstructed position of interaction, corrected for electric field effects. Also commonly seen in cylindri- cal coordinates_{{r, φ, z}.}

center of the TPC. S1 photon detection efficiency primarily varies with z position; because of total internal reflection at the liquid-gas interface, most S1 light is predominantly collected in the bottom PMTs. As such, S1 photons emitted near the bottom PMT array are ∼ 50% more likely to be detected than those originating near the liquid surface. S2 pulse areas also vary with z (for fixed ne), since longer drift times incur greater losses

of charge to electronegative impurities. This loss is totally cancelled in the correction, as if the event occurred exactly at the center of the gate grid. Note that some (x, y) dependence exists as well, to some extent for S1 but more significantly for S2 pulse area efficiencies. As indicated in Table 3.1, corrected pulse areas are notated in italics: S1 and S2.

Since electron lifetime (a function of LXe purity) can vary over the course of a long WIMP search exposure, the area corrections must be updated as well. Thus, date- dependent area correction maps are stored in a database which is queried by the DPF in the course of determining IQs (important quantities) required by the various data re- duction modules. The LRFs, for example, can also change over time, as can PMT gains. These and other detector parameters are calculated and monitored using various calibra- tion techniques, allowing for consistent and accurate estimation of key event observables by the DPF.