I'm interested in attempting to use factor on some old data where the observation consists of 6 beams observed simultaneously. These 6 beams are overlapping with the aim to mosaic them together such that I end with a final image of a large patch of sky.
What is the best way to organise the data to do this? Do I process each beam individually and join the images at the end? Or is there a better way?
I've been reading the docs of both prefactor and factor and I was unclear on what the best method would be, or even if it really supports processing different directions - so I just wanted to clarify.
Of course being a multi-beam dataset this also means the frequency coverage is not continuous but rather 4 x 10 SBs of pre-dertmined bands. So the usual size bands but not covering the full frequency range but are instead spread out - will this also cause problems with factor? Pre-factor manages this ok.
I'm interested in attempting to use factor on some old data where the observation consists of 6 beams observed simultaneously. These 6 beams are overlapping with the aim to mosaic them together such that I end with a final image of a large patch of sky.
What is the best way to organise the data to do this? Do I process each beam individually and join the images at the end? Or is there a better way?
I've been reading the docs of both prefactor and factor and I was unclear on what the best method would be, or even if it really supports processing different directions - so I just wanted to clarify.
Of course being a multi-beam dataset this also means the frequency coverage is not continuous but rather 4 x 10 SBs of pre-dertmined bands. So the usual size bands but not covering the full frequency range but are instead spread out - will this also cause problems with factor? Pre-factor manages this ok.