  The Astrophysical Multimessenger Observatory Network (AMON) receives
subthreshold data from multiple observatories in order to look for
coincidences. Combining more than two datasets at the same time is challenging
because of the range of possible signals (time windows, energies, number of
events...). However, outlier detection methods can circumvent this issue by
identifying any signal divergent from the background (e.g. scrambled data). We
propose to use these methods to make a model independent combination of the
subthreshold data of neutrino and gamma ray experiments. Using the python
outlier detection (PyOD) package, it allows us to test several methods from a
simple "k-nearest neighbours" algorithm to a more sophisticated Generative
Adversarial Active Learning neural networks which generates data points to
better discriminate inliers from outliers.
