  Signal-agnostic data exploration based on machine learning could unveil very
subtle statistical deviations of collider data from the expected Standard Model
of particle physics. The beneficial impact of a large training sample on
machine learning solutions motivates the exploration of increasingly large and
inclusive samples of acquired data with resource efficient computational
methods. In this work we consider the New Physics Learning Machine (NPLM), a
multivariate goodness-of-fit test built on the Neyman-Pearson
maximum-likelihood-ratio construction, and we address the problem of testing
large size samples under computational and storage resource constraints. We
propose to perform parallel NPLM routines over batches of the data, and to
combine them by locally aggregating over the data-to-reference density ratios
learnt by each batch. The resulting data hypothesis defining the
likelihood-ratio test is thus shared over the batches, and complies with the
assumption that the expected rate of new physical processes is time invariant.
We show that this method outperforms the simple sum of the independent tests
run over the batches, and can recover, or even surpass, the sensitivity of the
single test run over the full data. Beside the significant advantage for the
offline application of NPLM to large size samples, the proposed approach offers
new prospects toward the use of NPLM to construct anomaly-aware summary
statistics in quasi-online data streaming scenarios.
