  AGILE is a space mission launched in 2007 to study X-ray and gamma-ray
astronomy. The AGILE team developed real-time analysis pipelines to detect
transient phenomena such as Gamma-Ray Bursts (GRBs) and to react to external
science alerts received by other facilities. The AGILE anti-coincidence system
(ACS) comprises five panels (four lateral and one on the top) that surround the
AGILE detectors to reject background charged particles. It can also detect hard
X-ray photons in the energy range 50 - 200 KeV. The acquisition of the ACS data
produces a time series for each panel. These time series can be merged in a
single multivariate time series (MTS). We present in this work a new Deep
Learning model for GRBs detection in the MTSs, generated by the ACS, using an
anomaly detection technique. The model is implemented with a Deep Convolutional
Neural Network autoencoder architecture. We trained the model with an
unsupervised learning algorithm using a dataset of MTSs randomly extracted from
the AGILE ACS data. The reconstruction error of the autoencoder is used as the
anomaly score to classify the MTS. If the anomaly score is higher than a
predefined threshold, the MTS is flagged as a GRB. The trained model is
evaluated using a list of MTSs containing GRBs. The tests confirmed the model's
ability to detect transient events, providing a new promising technique to
identify GRBs in the ACS data that can be implemented in the AGILE real-time
analysis pipeline.
