3 The Grazing Incidence Spectrometer (GIS)

The Grazing Incidence Spectrometer (GIS) consists of a grazing incidence spherical grating that disperses the incident light to four detectors placed along the Rowland Circle. It is astigmatic, focusing the image of the slit along the direction of dispersion but not perpendicular to it. Images of the field of view may be obtained using a pinhole slit and rastering using movements of the slit and the scan mirror; normally, the raster covers the region of the Sun moving from south to north and from west to east. A more detailed description of the CDS instrument can be found in Harrison et al. (1995).

The GIS detectors are SPAN (spiral anode) MicroChannel Plate (MCP) detectors, described in detail in Breeveld et al. (1992) and Breeveld (1996). Raw data from the four detectors is collected on a regular basis to check the gain and spectral dispersion. The raw data consists of pairs of voltages produced in the spiral anode in response to each photon which falls on the detector over a period of time. These are plotted against each other to produce a spiral pattern, where the wavelength varies with distance along the spiral and the width of each arm is related to the noise in the electronics. A typical spiral pattern is displayed in Fig. 1. The number of counts at each radial position along the spiral is proportional to the number of photons hitting the front of the detector. The raw data is then used to produce four on-board look up tables (LUTs) which translate each voltage pair into a pixel position along the detectors, dependent on the wavelength. Because the gain in the micro channel plates is sensitive to the intensity, the voltage applied to the detector must be adjusted depending on the slit used and the solar conditions (e.g., active or quiet Sun) and a different LUT must be used in each case. The set of LUTs used in a given observation is identified by its GSET_ID.

  

Figure 1: Typical spiral pattern for the GIS detectors. The x- and y-axis units are Analogue to Digital converter Units (ADU)

There are various effects that must be taken into account before starting any analysis of GIS data, for example electronic dead time, short term gain depression and long term gain depression.

The GIS operates by sending a continuous stream of pixels to the Command and Data-Handling System (CDHS). During normal observations, counts on each pixel position are accumulated for the length of the exposure. An array of 2048 pixels is thus produced for each detector. The various electronic dead-times are such that any events occuring within about 10 $\mu$s will be rejected, and higher rates will render the detectors unusable.

Short term gain depression occurs when count rates are above 40. In this situation, the MCP cannot recharge fast enough to provide full gain for every event, and the data are not usable.

Long term gain depression is an ageing effect, a reduction in detector sensitivity over a long time interval due to continued illumination. The applied high voltage can be increased to compensate for the decrease in the MCP gain with time. However, the gain depression will be greatest at the position of the brightest lines so a wavelength-dependent effect will remain.

Other effects to be taken into account when analysing GIS spectra are fixed patterning and the problem of ghosts.

The profiles of the GIS lines are predominantly instrumental. They are corrupted by the superposition of a spiky effect on the whole spectrum, referred to as fixed patterning. This is caused by the finite number of bits employed in the electronics to obtain the detector positions (pixels). Counts falling near the boundary of a pixel can be shifted to neighbouring pixels, producing narrow spikes and troughs in the profiles of the emission lines. Weaker lines can be swamped by this effect. This process does not alter the total counts recorded in an emission line; it merely displaces some of them.

Ghosts result from the spiral nature of the SPAN detectors (see Fig. 1). In the case of some lines, the photon events will tend to spread across to the neighbouring arms of the spiral. This means that a portion of the counts belonging to a line can be shifted into different parts of the spectrum, giving rise to spurious spectral lines if they fall in a spectral region void of lines, or providing extra intensity to already existing spectral lines. They can also fall outside the spectral range of the detector.

Hereafter we will refer to a spectral line whose intensity is enhanced by the presence of counts coming from a line in a different arm of the spiral as a ghosted line, while the lines whose counts have been partially shifted toward other regions of the spectrum will be referred to as ghosting lines. The displaced counts will be called ghosts.

Each line can generate no more than two ghosts, one blue-shifted and the other red-shifted (see Fig. 1). Attempts are made to reduce the number of ghosts by a judicious choice of look-up tables. For each application of a set of GIS set-up parameters, ghosts will appear in the same positions. Once the applied LUT is known, it is therefore possible to deduce where ghosts will appear, and also which regions of the spectrum are unlikely to be affected by them.