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Andy asked whether RAPID could be packaged up for users to run from a notebook, with the caveat that it does not necessarily classify events accurately.
Michael worries that traditional spectroscopic follow-up outperforms this.
Stephen believes, if not reliable over first ten days or a light curve, it can not be used in the fast stream.
Meg asked if this was a training problem. Expectation is that ML techniques typically fail when applied to a different dataset.
Michael noted that RAPID had been trained for ZTF, so that was unlikely to be source of inaccuracy.
Meg noted that simulated ZTF data is different to real ZTF data.
Two questions: do you want a ML classifier? Do you want RAPID to be it?
Stelios V, LSST Science Platform
Dave Y asked if Nublado solves issue with sharing notebooks? Nearly impossible to do so in Jupyter Hub, it would seem.
Andy encourages that we make things as familiar as possible to users, meaning maximum harmonisation with US approach – e.g. using their Jupyter Service, TAP, service, etc. Andy is less sure about Firefly, which seems a bit clunky.
Meg asked how this compared to US DAC, given rumour they would move away from DAC.
While there had previously been a pause on development, it looked as if Firefly was still the expected web interface.
Summary, Andy L
Should take a little time to reflect on days discussions, and then consider what next.
Could use Slack to further consider some of the topics, reconvene for another session (e.g. Lasair telco on 24th), or define a smaller group to take forward key issues.
The decision was that key issues would be discussed at the next Lasair telecon, which would have two hours dedicated to it.
However, before this we must attempt to digest and summarise our notes, and produce a tightly focused decision list to be discussed at the telecon
Andy would have the first go at making a wiki page with “decisions required”
All are welcome to continue making points in the Slack discussion
Decisions needed can be divided into:
definite decisions
actions on further experiments/tests
actions on more significant work, e.g. drawing up a Kafka-centred architecture.