Before a collision becomes a histogram

A cross section is a number I know how to look at. A log full of calculations that led to it is a rather different proposition. During openlab I learnt to keep the individual event weights in view, because the final sum can hide exactly the thing I’m trying to understand.

What an event carries

PEPPER proposes configurations of outgoing particles and works out how much each contributes to the predicted rate. The weight includes the physics of the hard scattering and factors that account for how the configuration was sampled. Some workflows then unweight those events by accepting heavier events more often, so the accepted events can usually be handled with a common weight. PEPPER paper, event generation framework.

Here’s a tiny positive-weight example. Suppose two candidates have weights 1 and 3, and we use 4 as the acceptance bound. The first gets accepted one quarter of the time and the second three quarters of the time. If both happen to survive, they look alike in the unweighted sample. Their original weights explain why the second was more likely to get there.

Where I put the diagnostic

I recorded numerical diagnostics after each candidate’s weight was calculated and before the accept-or-reject step. That gave me the original weighted calculation to inspect. I could look at the evaluated matrix-element quantity alongside the other ingredients and ask where the sensitivity to rounding entered. A warning from CADNA points to an operation worth investigating. Several such warnings can come from the same candidate. CERN openlab project and talk.

Imagine a distribution split into two bins. A shift from weights 4 and 6 to 3 and 7 leaves the total at 10, but it moves weight from one bin to the other. That is why I want both views of a precision comparison. The overall rate and the shape tell me how a physics prediction moves. The individual weights give me a place to start looking for the reason.