Please read the background information in the slides at
Elahe is investigating potential scheduling algorithms (note: this means, what kinds of algorithms are useful, from an operations research point of view, in developing scheduler ‘controllers’), and as part of that development, she is building prototype cost functions and evaluations of the resulting observations. Currently she is investigating ‘approximate optimal control’ solutions. The scheduling algorithms are focused on optimizing observations within a single night.
One of the areas we would like to investigate is what should the ‘features’ used to weight the desired next field be? (see slide 17 of Elahe’s slides on the confluence page)
For example: would it be useful to combine skybrightness (in each field, in each filter), seeing (in each field, in each filter), and cloud transparency (in each field) a SNR or m5 depth in each field/filter feature?
If different features drive different behavior for the scheduler, they should presumably not be combined. So if we have a cloud mask (0 = no clouds, 1 = clouds) and a separate skybrightness estimate, perhaps that drives the scheduler to behave differently than it would if these values were combined – i.e., with clouds separate the scheduler might be more driven to move to a different field, but with everything combined the scheduler might be more likely to change the filter but stay in one part of the sky.