  Non-adaptive group testing refers to the problem of inferring a sparse set of
defectives from a larger population using the minimum number of simultaneous
pooled tests. Recent positive results for noiseless group testing have
motivated the study of practical noise models, a prominent one being dilution
noise. Under the dilution noise model, items in a test pool have an i.i.d.
probability of being diluted, meaning their contribution to a test does not
take effect. In this setting, we investigate the number of tests required to
achieve vanishing error probability with respect to existing algorithms and
provide an algorithm-independent converse bound. In contrast to other noise
models, we also encounter the interesting phenomenon that dilution noise on the
resulting test outcomes can be offset by choosing a suitable
noise-level-dependent Bernoulli test design, resulting in matching
achievability and converse bounds up to order in the high noise regime.
