Please see the below advertisement for a PhD position at the University of Southampton, which is open to any EU student.
The deadline for applications is February 29th. Click for more details.
Monitoring the Ionosphere with LOFAR Chilbolton – Anna Scaife (Southampton), Ian Heywood (Oxford), Bruce Swinyard (RAL)
The variable ionosphere is a calibration issue for both radio astronomy and the RF communications industry, causing both retardation and absorption of radio signals as they pass through the atmosphere. These effects are dependent on the temporal and spatial distribution of total electron content (TEC) in the ionosphere. Their impact varies as a linear function of wavelength, and so signals at low frequencies are most affected. This consideration is consequently an important factor in the calibration of very low frequency radio telescopes such as the LOw Frequency ARray (LOFAR) where such effects are further complicated by the wide reception patterns of the LOFAR antennas, which are substantially larger than the scale of ionospheric fluctuations. A consequence of this fact is that traditional self-calibration techniques for radio astronomy, which rely on reception patterns smaller than or approaching the size of ionospheric fluctuations, are no longer sufficient and a more detailed modeling of the ionosphere is required to completely correct for contaminating effects. Moreover, the recovery of polarization information from astronomical Faraday rotation is dependent on corrections for the absolute ionospheric density along the line of sight, in addition to the relative values required for imaging. Models based on long-term, statistical records can provide useful indications of time-averaged ionospheric conditions, but are generally not suitable for accurate representations of the ionosphere at any in- stant. This is because the short-term variability of the ionosphere regularly causes its morphology to differ from time-averaged conditions. At present, the most numerous and easily accessible ionospheric data come from the international network of ground-based GPS receivers. However, the spatial and temporal sampling of available GPS data is sufficient for neither complete calibration of radio astronomical measurements nor reliable ionospheric modeling. However, linking real-time GPS data to the data reduction of telescopes such as LOFAR, as well as linking ionospheric monitoring using known radio sources to tomographic inversion of the ionosphere from GPS measurements will provide advantages to both disciplines.
The project will make use of the LOFAR Chilbolton station SEPCAM instrument as a riometer for measuring absolute differences in ionospheric fluctuations from the diffuse all-sky radio background, as well as data from the combined International LOFAR Telescope in order to measure small-scale fluctuations through their effect on the astrometry of known radio sources as a function of time. These data will be combined with GPS-based ionospheric modeling tools to look at comparisons between satellite-based and astronomy-based ionospheric measurements. These techniques will be combined into a real time ionospheric correction and prediction network linking the LOFAR Chilbolton data reduction pipeline and the GPS based MIDAS ionospheric inversion tool. This network will have the dual purpose of improving astronomical calibration through linked GPS measurements; and improving ionospheric modeling through the use of astronomical measurements. The student will use the SEPCAM riometry data to tie down the absolute ionospheric levels over LOFAR Chilbolton and their long and short term behavior. These data will be combined with constraints from polarized astronomical sources with known rotation measures to provide a details of the temporal and spatial variation in absolute ionospheric TEC. This absolute measure can then be used as a prior on the local absolute ionospheric TEC, and combined with the relative astrometric disturbance of a grid of known bright radio sources, the student will develop an inversion to be implemented through the MIDAS framework to recover a 4-dimensional ionospheric tomographic mapping of the local ionosphere, which can be compared and combined with GPS based inversions
