Characterization of Silicon Photomultipliers for use in Scintillating Bubble Chambers (SBCs)

It is believed today that most of the matter in the universe is not composed of Standard Model Particles, but rather of Dark Matter (DM). DM is thought to constitute 85 percent of the matter in the universe. Direct detection of DM is extremely difficult for a myriad of reasons, but bubble chambers have proven to be good experimental probes of the parameter space that DM is suspected to reside in. The two 10Kg Scintillating Bubble Chambers (SBCs) being built at Fermilab and SNOLAB are novel detectors that couple the the background rejection of bubble chambers with the production of scintillation light for each DM candidate particle. The scintillation light in SBCs is used to measure the energy deposited by DM particles. The scintillation light is captured by silicon photomultipliers (SiPMs), which are solid state light detectors capable of single photon detection. This detection is performed by incoming photons being converted into charge at the p-n junctions in the diodes of the SiPMs. The converted photoelectrons are amplified to a detectable current by a cascade effect within the SiPMs, and the proportionality factor of the amplification is ideally linear and is called the gain. Gain is one of the significant sensitivity parameters that characterize a light detector, and in the context of the SBC detectors the calculation of the gain for SiPMs is especially important because it is used to determine energy left by possible DM candidate particles. In the present work, a novel technique for calculating the gain is explored that employs emcee, an open source Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman (2010). The novel fitting procedure is performed with different filters (Butterworth and Fourier) in order to produce a gain calculation from each filter and the results are compared to a standard calculation in order to see which calculation yields the best signal to noise to ratio.