Discussion of: Deep learning level-3 electron trigger for CLAS12

Cissie

Paper was clearly written and did a good job describing the work that was done.
Page 4, right column: Stride = (1,2). What is the kernel/filter size?
Fig. 9: They are mainly focused on the left edge where the efficiency is above the 99.5% level (dotted grey line). This is the region of the steepest gradient of purity (i.e. dpurity/dresponse is large)
Fig. 10 (described at bottom of page 6) is for tracks from real data. Wouldn't this include the hardware trigger already?
Fig. 12: The linear extrapolation of the traditional trigger looks like they are being generous. In reality, the traditional trigger probably does much worse are higher beam currents. If a curve was fit to the actual data points shown though, the extrapolation to 90nA would have a negative purity which is unphysical.
Eqn. 4 seems like it should be a ratio rather than a difference. Specifically, wouldn't the data reduction be the ratio of P_AI to P_CLAS12? (The data volume for fixed efficiency would go like 1/P)
Individual process for each GPU was faster than single process using all GPUs. Not clear if single process used feature in Deeplearning4j to distribute or if they used custom code.
Will use in conjunction with existing hardware trigger which already has high efficiency to reduce rate into AI trigger. (Probably the smart way to do it.)