Tamas Vami
Tamas Vami
> Oh interesting, but this is with the default biasing, i.e. 550 Actually I think the defaults were never updated, so it's still 450 even: https://github.com/LDMX-Software/ldmx-sw/blob/b3195994ada82b76b2e540bcca00de8140ed26d5/Biasing/python/ecal.py#L60 ``` sim.biasing_operators = [...
with 450 & 2500 --> 239 / 10 event (took 1039 events to produce 10) with 550 & 5000 --> 251 / 10 event (took 1122 events to produce 10)...
@tomeichlersmith @bryngemark I update the comment below with more data points. Werent we expecting that lowering the factor will reduce the number of these msg?
OK here is some other points (all have the conditions as in https://github.com/LDMX-Software/ldmx-sw/pull/1548 + changing `xsec_bias`: 100: 0/10 (Started 5554 events to produce 10 events) 200: 0/10 (Started 1920 events...
ok I'll open a draft PR and we can just merge it when we are ready
For documentation; here is the link to the SWAN meeting talk regarding this: https://indico.fnal.gov/event/68163/#32-biasing-factor-studies-for
I made the changes as implied by Natalia's comments in https://github.com/LDMX-Software/ldmx-sw/pull/1566 Now tho, if I increase the factor to huge numbers, there are no warnings...
From Natalia > I think our estimates of the appropriate biasing factor are based on tungsten, which dominates the ecal material budget, or on some sort of weighted average of...
Final talk at SWAN: https://indico.fnal.gov/event/69925/#34-ecal-pn-overbiasing
One thing we need to discuss is how to differentiate the overload function from the TH1 case