  We present a machine learning approach for model-independent new physics
searches. The corresponding algorithm is powered by recent large-scale
implementations of kernel methods, nonparametric learning algorithms that can
approximate any continuous function given enough data. Based on the original
proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the
compatibility between experimental data and a reference model, by implementing
a hypothesis testing procedure based on the likelihood ratio.
Model-independence is enforced by avoiding any prior assumption about the
presence or shape of new physics components in the measurements. We show that
our approach has dramatic advantages compared to neural network implementations
in terms of training times and computational resources, while maintaining
comparable performances. In particular, we conduct our tests on higher
dimensional datasets, a step forward with respect to previous studies.
