Introduction to Difmap - Mike Garrett, JIVE, NL NATO VLBI Summer School 1/16/2019

What problems do we need to solve and why ? We are trying to synthesise a giant, continent sized radio telescope from many small telescopes:

Estimating Telescope Errors – Self-calibration Path length of radiation from the radio source to the telescope is not constant e.g. phase errors are introduced via atmosphere above telescopes. For an array of N telescopes we measure (instantaneously) N(N-1)/2 corrupted interferometer measurements. The “trick”of self-calibration is to understand that the corrupted visibilities arise from telescope based errors – and there are only N of these. Its possible to solve for these N errors by using combinations - (N-1)(N-2)/2 closure phases - of the corrupted “visibilities” AND an assumed model of the source – Hybrid Mapping.

uv-Data “Dirty” Beam “Dirty” Map Deconvolution – CLEAN A VLBI synthesised aperture in NOT filled with data – indeed it is mostly empty! Our aperture is not fully sampled – our beam (PSF or “response”) is IMPERFECT…… uv-Data “Dirty” Beam “Dirty” Map

CLEAN 0 CLEAN30 CLEAN60 CLEAN 2300 The CLEAN algorithm subtracts the dirty beam from the dirty map; building up a list of CLEAN components that are convolved to generate the CLEAN image….. CLEAN 0 CLEAN30 CLEAN60 CLEAN 2300

Difmap Difmap combines together self-calibration and CLEAN using a technique called “difference mapping”. Includes model-fitting facility. Difmap is fast, self-contained and “hands-on” - you can EASILY inspect and edit your data (or self-cal corrections) Difmap is ideal for continuum observations of simple, compact radio sources incl. snapshots Limitations: Basic calibration (e.g. fringe-fitting, amplitude calibration etc) not surported. Wide- field imaging impractical, “finite options” etc.