Speaker
Description
As LZ prepares to push the limits of known physics and improve our understanding of the nature of dark matter, it is important to ensure that these gains are not mistakenly influenced by human biases towards achieving such results. Such biases often appear in the process of analysis when unconsciously or consciously expecting certain outcomes. Many techniques for avoiding these biases have been employed over the years including blinding and using hidden parameters. LZ will be using a method known as salting, in which fake signal events are injected into our data stream and removed after analysis is complete. In this presentation I will explain the historical motivations for pursuing bias mitigation, the process through which LZ salts its data, and some results after salting LZ’s simulated mock data challenges.
