Decades of studies have suggested several criteria to detect Interplanetary coronal mass ejections ( ICME ) in time series from in-situ spacecraft measurements .
Among them the most common are an enhanced and smoothly rotating magnetic field , a low proton temperature and a low plasma beta .
However , these features are not all observed for each ICME due to their strong variability .
Visual detection is time-consuming and biased by the observer interpretation leading to non exhaustive , subjective and thus hardly reproducible catalogs .
Using convolutional neural networks on sliding windows and peak detection , we provide a fast , automatic and multi-scale detection of ICMEs .
The method has been tested on the in-situ data from WIND between 1997 and 2015 and on the 657 ICMEs that were recorded during this period .
The method offers an unambiguous visual proxy of ICMEs that gives an interpretation of the data similar to what an expert observer would give .
We found at a maximum 197 of the 232 ICMEs of the 2010-2015 period ( recall 84 \pm 4.5 \% ) including 90 \% of the ICMEs present in the lists of \citet chinchilla and \citet Chi16 .
The minimal number of False Positives was 25 out of 158 predicted ICMEs ( precision 84 \pm 2.6 \% ) .
Although less accurate , the method also works with one or several missing input parameters .
The method has the advantage of improving its performance by just increasing the amount of input data .
The generality of the method paves the way for automatic detection of many different event signatures in spacecraft in-situ measurements .