Acoustic analysis methods for particle identification with superheated droplet detectors

Abstract

A superheated droplet detector (SDD) consists of a uniform dispersion of over-expanded, micrometric-sized halocarbon droplets suspended in a hydrogenated gel, each droplet of which functions as a mini-bubble chamber. Energy deposition by irradiation nucleates the phase transition of the superheated droplets, generating millimetric-sized bubbles that are recorded acoustically. A simple pulse shape validation routine was developed in which each pulse is first amplitude demodulated and the decay constant then determined through an exponential fit. Despite this, low amplitude (< 3 mV) events embedded at naked eye in the noise level are not counted for calibration purposes with neutron and alpha sources. The solution found was to filter the data with a low band-pass filter in the region that the bubbles nucleate (typically from 450 to 750 Hz). After this, a peak finding algorithm to count all the events was implemented. The performance demonstrates better than a factor 40 reduction in noise and an extra factor 10 reduction with the filtering application. The lowering of noise and discovery of low signal amplitudes by the acoustic instrumentation and acoustic analysis permits a capability of discriminating nucleation events from acoustic backgrounds and radiation sources and, having a 95% confidence level on identifying and counting events in substantial data sets like in calibrations.