Transcript Document

Introduction to Difmap
- Mike Garrett, JIVE, NL
NATO VLBI Summer School
7/18/2015
1
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.
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
• 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. Widefield imaging impractical, “finite options” etc.