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A common technique in high energy physics is to characterize the response ofa detector by means of models tunned to data which build parametric maps fromthe physical parameters of the system to the expected signal of the detector.When the underlying model is unknown it is difficult to apply this method, andoften, simplifying assumptions are made introducing modeling errors. In thisarticle, using a waveform toy model we present how deep learning in the form ofconstrained bottleneck autoencoders can be used to learn the underlying unknowndetector response model directly from data. The results show that excellentperformance results can be achieved even when the signals are significantlyaffected by random noise. The trained algorithm can be used simultaneously toperform estimations on the physical parameters of the model, simulate thedetector response with high fidelity and to denoise detector signals.

Data-driven detector signal characterization with constrained bottleneck autoencoders