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AGILE is a space mission launched in 2007 to study X-ray and gamma-rayastronomy. The AGILE team developed real-time analysis pipelines to detecttransient phenomena such as Gamma-Ray Bursts (GRBs) and to react to externalscience alerts received by other facilities. The AGILE anti-coincidence system(ACS) comprises five panels (four lateral and one on the top) that surround theAGILE detectors to reject background charged particles. It can also detect hardX-ray photons in the energy range 50 - 200 KeV. The acquisition of the ACS dataproduces a time series for each panel. These time series can be merged in asingle multivariate time series (MTS). We present in this work a new DeepLearning model for GRBs detection in the MTSs, generated by the ACS, using ananomaly detection technique. The model is implemented with a Deep ConvolutionalNeural Network autoencoder architecture. We trained the model with anunsupervised learning algorithm using a dataset of MTSs randomly extracted fromthe AGILE ACS data. The reconstruction error of the autoencoder is used as theanomaly score to classify the MTS. If the anomaly score is higher than apredefined threshold, the MTS is flagged as a GRB. The trained model isevaluated using a list of MTSs containing GRBs. The tests confirmed the model'sability to detect transient events, providing a new promising technique toidentify GRBs in the ACS data that can be implemented in the AGILE real-timeanalysis pipeline.

Preliminary Results of a Deep Learning Anomaly Detection Method to Identify Gamma-Ray Bursts in the AGILE Anticoincidence System