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v3.3.5-pre
To attempt the observation of phenomena expected to happen with an extremely low probability, like the direct interaction of dark matter particles pervading the galactic halo, detectors operating in ultra-low background conditions are mandatory. This imposes operation underground, the use of passive and active shieldings, the control of material radiopurity and the implementation of different types of techniques to discriminate the signal from background events. A good knowledge of the expected backgrounds in an experiment allows to develop precise background models which are necessary to quantify the sensitivity to the investigated physical processes. Monte Carlo simulations, reproducing particle transport and detector response, are valuable tools to understand the origin of backgrounds present in measured data, to quantify the effectiveness of reduction techniques and to compute expected counting rates in detectors.
In this seminar, a brief overview of the main background components and strategies for mitigation and rejection of backgrounds will be firstly made. Then, several computational codes aiding in this effort will be discussed, with particular focus on some background sources like cosmogenic activation of materials or radiogenic neutrons. The relevance of measurements (material radioactivity, ambient fluxes of muons, neutrons, gamma radiation…) following different techniques to feed or validate simulations will be highlighted. Machine-learning techniques are being applied in data analysis to increase efficiency when discriminating backgrounds from potential signals. Some examples of application of the different tools in experiments in the context of dark matter searches will be presented.