ing AKARI Data
This chapter provides a brief summary of the instrumentation onboard the AKARI satellite, information about the AKARI/IRC observations, the standard pipeline written to process AKARI/IRC data and a thorough description of a new optimised pipeline
2.5. Conclusions on the New Data Pipeline for Processing AKARI Data 67
for processing extragalactic deep fields.
The optimised pipeline replicates and in some cases improves upon the following steps from the standard pipeline: wraparound correction, dark subtraction, normalisation, linearity correction, cosmic ray removal, flat fielding and masking anomalous pixels. The optimised pipeline also performs steps, which the standard pipeline did not do on individual frames: creating a noise image, distortion correction, Earthshine light correction, scattered light correction, astrometry correction and masking.
Six of the above steps make significant improvements on the standard pipeline. The first is the flat fielding step. This removes instrumental artefacts which the standard pipeline was unable to do, e.g. the bean, see Figure 2.13. The second step is masking hot pixels in later Phase 2 data, which may otherwise be detected as point sources in the coadded image. As these hot pixels are not visible in the final frames of a pointing, due to Earthshine light heating the detector to a temperature greater than that of the hot pixels, they are not detected in the standard flat field image. The optimised pipeline uses a bespoke hot pixel mask. Figure 2.16 shows the improvement of using this mask, to a coadded image. AKARI data suffers from image distortion and astrometry offset, which in the past has made cross-matching with ancillary data sets hard to perform. The third significant improvement is the creation and use of a second order polynomial distortion correction for the x and y axes for each of the nine filters. The fourth improvement is correcting the astrometry offset on each individual frame before coadding. AKARI data, in particular later Phase 2 data, is badly effected by Earthshine light, both as a moving artefact on the image and as an increase of average flux of a frame over a pointing, caused by the warming up of the detector. The fifth major improvement of the optimised pipeline is the removal of both types of Earthshine light. Figure 2.22 shows an example of this. The final major improvement, is that individual frames are masked before the coadding stage, as apposed to after coadding. This means that less of the final image is masked, and thus has signal from a greater area. With the improvements described in this chapter, the AKARI frames processed by the optimised pipeline are science ready.
Galaxy Number Counts and
Catalogues from the IRAC Dark Field,
ELAIS North 1 and AKARI Deep
Field South
I had the means of an excellent education placed within my reach; a fondness for some of my studies, and a desire to excel in all
Jane Eyre- Charlotte Brontë
3.1
Introduction
To answer the current question of how galaxies evolve, the statistical properties of galaxy populations are required (Oliver et al., 2000). The most basic of these is galaxy number counts. Galaxy number counts show the number of galaxies per unit area within a specific flux range. Galaxy number counts were originally used as a way of determining the geometry of the Universe. Early discoveries using the results from radio galaxy number counts, showed that the Universe is inconsistent with a steady- state model (Rowan-Robinson, 1967). Subsequently galaxy number counts have been
3.1. Introduction 69
used to study galaxy evolution, star formation history and the epoch of galaxy for- mation (Ellis, 1987). In order to study how different galaxy populations behave at different wavelengths, we need galaxy number counts at many wavelengths (Pearson & Rowan-Robinson, 1996). A population of galaxies which contribute strongly to the mid-infrared number counts, are dusty star forming galaxies (Elbaz et al., 1999). A way to study these galaxies, negating modelling the individual complicated dust fea- tures, is to perform galaxy number counts. Number counts study the population of galaxies as a whole, and can aid in constraining the evolution of dusty star forming galaxies (Murata et al., 2014a).
In this chapter the optimised pipeline is applied to three major legacy survey fields, the IRAC Dark Field, ELAIS North 1 and the AKARI Deep Field South, creating mosaicked images of the fields. Source catalogues and number counts are created us- ing these mosaicked images. The number counts are compared with existing number counts work and galaxy number counts models.
This chapter is organised as follows: Section 3.2 explains how galaxy number counts are calculated, and how reliability and completeness corrections are performed. Sec- tion 3.3 gives a brief summary of the ancillary data in the IRAC Dark Field and data reduction of the AKARI data in this field. Section 3.4 summarises the ancillary data and image reduction of the AKARI ELAIS North 1 extragalactic deep field. The an- cillary data and image reduction of the AKARI Deep Field South are summarised in Section 3.5. The creation of the galaxy catalogues is described in Section 3.6. The number counts work for all three extragalactic fields is presented in Section 3.7. These number counts are compared with non-evolution galaxy number count models in Sec- tion 3.8. Section 3.9 introduces appropriate number count models and some published number counts. These are compared with the number counts of this chapter.