We propose using neural networks to detect data departures from a given
reference model, with no prior bias on the nature of the new physics
responsible for the discrepancy. The virtues of neural networks as unbiased
function approximants make them particularly suited for this task. An algorithm
that implements this idea is constructed, as a straightforward application of
the likelihood-ratio hypothesis test. The algorithm compares observations with
an auxiliary set of reference-distributed events, possibly obtained with a
Monte Carlo event generator. It returns a p-value, which measures the
compatibility of the reference model with the data. It also identifies the most
discrepant phase-space region of the data set, to be selected for further
investigation. The most interesting potential applications are
model-independent new physics searches, although our approach could also be
used to compare the theoretical predictions of different Monte Carlo event
generators, or for data validation algorithms. In this work we study the
performance of our algorithm on a few simple examples. The results confirm the
model-independence of the approach, namely that it displays good sensitivity to
a variety of putative signals. Furthermore, we show that the reach does not
depend much on whether a favorable signal region is selected based on prior
expectations. We identify directions for improvement towards applications to
real experimental data sets.

该论文使用神经网络检测数据与给定参考模型的偏差，并通过极大似然比假设检验算法实现。其主要应用是无需事先假设新物理，实现模型无关的新物理搜索，论文还探讨了进一步优化算法以适用于实验数据的可能性。