  Deep neural networks (DNNs) are widely used in pattern-recognition tasks for
which a human comprehensible, quantitative description of the data-generating
process, e.g., in the form of equations, cannot be achieved. While doing so,
DNNs often produce an abstract (entangled and non-interpretable) representation
of the data-generating process. This is one of the reasons why DNNs are not
extensively used in physics-signal processing: physicists generally require
their analyses to yield quantitative information about the studied systems. In
this article we use DNNs to disentangle components of oscillating time series,
and recover meaningful information. We show that, because DNNs can find useful
abstract feature representations, they can be used when prior knowledge about
the signal-generating process exists, but is not complete, as it is
particularly the case in "new-physics" searches. To this aim, we train our DNN
on synthetic oscillating time series to perform two tasks: a regression of the
signal latent parameters and signal denoising by an Autoencoder-like
architecture. We show that the regression and denoising performance is similar
to those of least-square curve fittings (LS-fit) with true latent parameters'
initial guesses, in spite of the DNN needing no initial guesses at all. We then
explore applications in which we believe our architecture could prove useful
for time-series processing in physics, when prior knowledge is incomplete. As
an example, we employ DNNs as a tool to inform LS-fits when initial guesses are
unknown. We show that the regression can be performed on some latent
parameters, while ignoring the existence of others. Because the Autoencoder
needs no prior information about the physical model, the remaining unknown
latent parameters can still be captured, thus making use of partial prior
knowledge, while leaving space for data exploration and discoveries.
