Data Selection and Reduction
4.3 Data Quality Selection
In order to ensure that MC simulations could be used for the reconstruction of the γ-ray char-acteristics in the data analysis techniques described later (see next section), data of a certain quality must be selected. For this purpose, a set of quality criteria was defined and applied to all observation runs of the PSR B1259−63 dataset. In this section the most important quality requirements are motivated.
4.3.1 Cloud Monitoring
The atmospheric transmission of Cherenkov light is influenced by several factors. Most obvi-ously, no useful air shower images can be obtained if there are clouds within the camera field
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Figure 4.9: Camera acquisition rate (upper points, right axis) and paraxial radiometer temperature (lower points, left axis) for CT4 during two observation runs with different weather conditions. Left:
Hazy sky partially covered with clouds. This run was rejected by the quality selection. Right: Clear sky.
of view. For this purpose, the temperatures measured by the paraxial radiometer were used as an indicator of cloud “activity”. Additionally, if the average radiometer temperature is too high, this indicates a high level of water vapor in the atmosphere. Figure 4.9 shows two examples of the evolution of the radiometer temperature for an observation run with and without clouds.
4.3.2 Air Shower Trigger / Acquisition Rate
One of the most important quantities regarding the quality of the data obtained during a run is the camera and system trigger rate and the acquisition rate of air shower events which are directly measured using the central trigger system. Variations in the absorption of Cherenkov light in the atmosphere result directly in variations of the trigger rate. The dependence of the rate on the pointing of the array can be quantified by simulations of cosmic ray air showers.
The dependence of the system rate on the zenith angleΘ (elevationα = 90◦−Θ) is shown in Fig. 3.13. Furthermore, variations of the camera and system acquisition rates could originate from possible problems during the readout process.
In order to determine variations of the rate during an observation run, a rough zenith angle correction was applied by fitting the temporal evolution of the system acquisition rate by a line, and the fractional RMS deviation from this line ∆Rwas calculated. All runs with a deviation exceeding a certain threshold∆Rrun> ∆Rthwere rejected. Figure 4.9 (left) shows the correlation of the radiometer temperatures and the camera acquisition rate during a run when clouds passed through the field of view of the system.
4.3.3 Camera Calibration – Unusable Pixels
During the calibration procedure of an observation run, problematic pixels were marked as unusable. In case the total number of unusable pixels exceeds 120 (12.5%) for any camera, the run was rejected. Values below this number still lead to a significant loss of image intensity, especially if these pixels are located in the inner part of the camera. Therefore, the resulting systematic effect on the energy reconstruction needs to be taken into account (see Sec. 5.3.4).
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Figure 4.10: Distribution of the centre of gravity of shower images in camera system coordinates. For this rejected run, two “holes” due to bad drawers and one noisy pixel can be seen.
Large clusters of unusable pixels are expected to result in a bias in several reconstruction quantities such as shower direc-tion and energy. This effect is largest in case one or more com-plete 16 pixel drawers are lack-ing intensity information and can be easily recognized in the cen-tre of gravity (see Sec 4.4.1) dis-tribution of all recorded shower images in a run. A flat distri-bution is expected to result from isotropic cosmic ray air showers.
If this distribution showed more than one missing drawer seen as
“holes” within a circle of 2◦ ra-dius around the camera centre (see Fig. 4.10), the run was re-jected.
4.3.4 Pointing Accuracy
Tracking System
The pointing correction methods described in Sec. 4.2.4 rely on the accurate determination of the shaft encoder values. Therefore, these values were continuously monitored by the tracking system. The RMS of the deviation of the actual axis position from it’s nominal position is required to remain below 500. This requirement was well met for all observation runs taken on PSR B1259−63, demonstrating the reliability of the H.E.S.S. tracking system.
Star Position Determination Using PMT Currents
Stars within the field of view of a pixel cause an increase of its anode current. This current is routinely monitored and the data can be used to check the system pointing by determining the position of peaks in the current distribution in each camera and comparing them with nominal star positions for each observation run. Since the field of view rotates during an observation run because of the alt-az mount of the telescopes, the star position can be determined with higher accuracy than the pixel size of 100, but this method is limited by the optical point spread function of the reflector, especially off-axis. Nevertheless, the method was used to check all runs considered for this analysis. Figure 4.11 shows the distribution of PMT currents for the whole PSR B1259−63 dataset and the position of stars within the field of view of a single camera.
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Figure 4.11: Left: RA-Dec sky-map of PMT currents within the field of view of one camera averaged for all runs pointing to the position of PSR B1259−63−0.5◦Dec, overlaid with the nominal positions of stars (white crosses). Within this coordinate system, celestial structures have a fixed position, while the pixel positions rotate around the camera centre during the observations. Right: The same map, but with a limited range of the current scale in order to emphasise low currents so that stars at larger magnitude become visible. The circular structures are due to unusable pixels for which the currents remained at a high level during the observations.