In [Hag01b], Hagfors tackles some general issues that have been neglected in Nielsen’s report [Nie01]. These include the effects of sidelobes, polarisation problems and the effect of snow on the ground. None of these issues seem to be of particular concern for the 2002 experiment.
[HGH03] (based on earlier notes in [Hag01a]) goes into the details of the difference between cross-correlation and filled aperture riometers. Without going into details of the statistical con- siderations used in this description, we will only quote his final result. Hagfors states that “If one inquires as to the amount of integration time one must have to make up this handicap [of the Mills Cross] compared to the filled array, the integration time ratio must be larger by a factor of 100 to 900.”
If we use IRIS (B=250kHz,τ=0.045s) as an example of a filled aperture riometer, this suggests an integration time for ARIES of about 5s to 45s.
5.5
Summary
Table 5.7 summarises the estimated integration times from the previous sections. Note that the three very different approaches to determining reasonable integration times (Grill/Yamagishi, Nielsen, Hagfors) lead to similar results, and later chapters will show that the Mills Cross system can indeed achieve these integration times.
CHAPTER 5. INVESTIGATIONS INTO THE ACHIEVABLE INTEGRATION TIME 92
System specification Nielsen Hagfors Grill
B=600kHz, untapered τ=186s
B=250kHz, cosine-tapered τ=300s
B=250kHz τ=5...45s
Radiation Pattern Simulations:
RIOSIM
Having looked at the basic working principles of antennas and riometers in chapters 2 and 3, this and the following chapter operate on a slightly higher level of abstraction, focusing on radiation patterns and how they can help in the evaluation and deployment of real system designs. As discussed in chapter 2, the receiving properties of each antenna or system of antennas are fully described by its associated radiation pattern. Depending on the point of view, this pattern is also referred to as antenna directivity or sensitivity pattern. It describes how the antenna system in question reacts to an incoming signal from any possible direction. In (imaging) riometry, we want to have a clear peak sensitivity in one direction and as low a sensitivity as possible in all other directions, in other words we want to form pencil-shaped beams with low sidelobes.
Now ideal pencil beams are unfortunately a purely theoretical thing, in fact many of the chapters in this thesis come back to this issue. The aim of this chapter is therefore to simulate the radiation pattern that various configurations of the Mills Cross can be expected to produce. Nielsen did some radiation pattern simulations in [Nie01], and we will refer to this in the appro- priate places. The main purpose is not to imitate work that has already been done, but to put it into a greater, more versatile, context (framework), using the radiation patterns to derive results that can be expected when operating the system as specified, and using these simulated results for validating data received by real systems. The toolbox developed in this chapter will enable us to apply all findings to arbitrary riometers or, in fact, antenna systems.
It is worth mentioning that there are different ways of actually deriving the radiation pattern for (Mills Cross) antennas. While Nielsen’s results are based on theory, it is also possible to
CHAPTER 6. RADIATION PATTERN SIMULATIONS: RIOSIM 94
simulate the Mills Cross, or any other antenna, using finite element method (FEM) software. Initial steps toward this have already been taken by the author in collaboration with G. Dekoulis, and although these are not discussed further in this thesis, FEM-simulated radiation patterns are readily supported by RIOSIM and one example can be found in section 6.3.10 (discussing theRNECPat class). FEM simulations give further insight into the real world behaviour of antennas, as they can take into account real-life effects such as imperfect ground planes that cause the real radiation pattern to deviate from its predicted theoretical shape.
6.1
Design Goals
Having verified the basic fitness of the Mills Cross system for our purposes in chapter 5, the aim of this chapter is to use radiation patterns to simulate the actual results that we can expect from the system. This includes simulations of the received signal during ‘quiet days’1 and simulations concerning the influence of strong celestial radio sources, the latter enabling us to predict, amongst other things, scintillation effects. All the results from this and the following chapter have directly influenced the schedule for the various ARIES on-site experiments, first and foremost the one whose results will be described in chapter 9.
Through the abstraction of radiation patterns, all results that are achieved in this chapter can easily be applied to any riometer system, as long as its radiation pattern is known. Due to a completely object-oriented approach, the core software does not have to be modified in any way to be able to adapt to new radiation patterns. This means that we can, for example, predict scintillation in every existing riometer with the same piece of software.
To summarise, the aims of the toolkit implemented in this chapter are to:
• Integrate different sources of radiation patterns (simulated, calculated, measured) into one program/framework.
• Integrate digitised sky background noise maps.
• Enable creation of theoretical quiet-day curves based on the different available radiation patterns and sky map(s).
1Similar simulations have been done by Huiyu Tao [Tao04] for the IRIS system, and some of the basics of this
chapter are based on Tao’s work. The tools developed in this chapter will, however, be much more flexible, as the following sections will show.
• Predict when certain radio stars will pass through which beam(s) and, based on this,
• predict scintillation effects.
• Enable the development of experiment schedules, taking into account the results of the above simulations.
• Develop all these algorithms in a general way so that they can be applied to other existing riometers.
The remainder of this chapter will describe the RIOSIM framework that was implemented for performing the tasks above. This framework will be used throughout the rest of this thesis. Chapter 7 in particular is dedicated to presenting some major applications.