Found another paper that gives further detail; some equations on this page are quoted from here (specifically pages 2-5): https://arxiv.org/pdf/1602.03150v1.pdf
Total integrated charge spectrum is defined by convolution of two components: the pedestal i.e. background and signal :
The mean charge for the SPE contribution can be found using the following equation:
According to the link above, making the assumption that the number of photoelectrons follows a Poissonian distribution, , where,:
where is the estimate for the number of 0PE triggers and is the total number of sample triggers.
NOTE: this analysis only works for low occupancy data
SPE Mean Charge Calculation
Run 12617 (RT124; Detector AARFs, 1 waveform, FWF, RAW 10% occupancy) - specifically focusing on subrun 0, PMTID 0. The spectra are found using SPESpectrumProc.cc
To calculate , found the numerical mean of the entire spectrum
To calculate , rerun processor with time window shifted to get histogram of just pedestal - find numerical mean of this histogram
To calculate :
Normalised the charge spectrum histogram such that the integral = 1. Hence N = 1, and thus and
Took integral of part of histogram below and including Q = 2pC (which is assumed to only include 0PE triggers), to obtain
Repeated this process for other PMTs in this subrun:
Statistical uncertainty on SPE Mean Charge
Statistical uncertainty on will be found by comparing the SPE mean charge from data with MC to see how much the value changes
Plan: make a 'hybrid' charge spectra distribution, with real data noise/pedestal with MC SPE pulses
Repeat above analysis and recalculate SPE mean charge, see how much they differ to obtain UNCERTAINTY
Current status: HIT mixing tool in process of being adapted in order to make this hybrid, when it is ready I will have more to update!