We present results on Deep Learning applied to Amplitude and Partial Wave Analysis (PWA) for spectroscopic analyses. Experiments in spectroscopy often aim to observe strongly-interacting, short-lived particles that decay to multi-particle final states. These particle decays have angular distributions that our deep learning model has been trained to identify. Working with TensorFlow and Keras libraries we have developed several neural network architectures that will be presented. One architecture that will be highlighted is our “Hybrid” Autoencoder (AE) architecture that has the best performance by far as it is able to resolve ambiguities. This AE is an unsupervised regressor that constrains the latent space variables to represent physically relevant quantities such as production amplitudes. As the training needs to be performed in a large amount of simulated data, a novel on-the-fly generation techniques is also used. Results of performed mass-independent and mass-dependent amplitude analyses using this technique will be presented.
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