Despite providing invaluable data in the field of High Energy Physics, the LHC may encounter challenges in finding interesting results through conventional methods utilized in previous run periods. Our proposed approach involves the use of a Joint Variational Autoencoder (JointVAE) model, trained on known physics processes to identify anomalous events that correspond to previously unidentified physics signatures. By doing so, this method does not rely on any specific new physics signatures and can detect anomalous events in an unsupervised manner, complementing the traditional LHC search tactics that rely on model-dependent hypothesis testing. It is presented a study for the implementation feasibility of the JointVAE model, for real-time anomaly detection in general-purpose experiments at LHC CERN. Among the challenges of implementing machine learning models in fast applications, such as the trigger system of the LHC experiments, low latency and reduced resource consumption are crucial. Therefore, the JointVAE model has been studied for its implementation feasibility in Field-Programmable Gate Arrays (FPGAs), utilizing a tool based on High-Level Synthesis named HLS4ML.
