  Time-delay cosmography can be used to infer the Hubble parameter $H_0$ by
measuring the relative time delays between multiple images of
gravitationally-lensed quasars. A few of such systems have already been used to
measure $H_0$: their time delays were determined from the multiple images light
curves obtained by regular, years long, monitoring campaigns. Such campaigns
can hardly be performed by any telescope: many facilities are often
over-subscribed with a large amount of observational requests to fulfill. While
the ideal systems for time-delay measurements are lensed quasars whose images
are well resolved by the instruments, several lensed quasars have a small
angular separation between the multiple images, and would appear as a single,
unresolved, image to a large number of telescopes featuring poor angular
resolutions or located in not privileged geographical locations. Methods
allowing to infer the time delay also from unresolved light curves would boost
the potential of such telescopes and greatly increase the available statistics
for $H_0$ measurements. This work presents a study of unresolved lensed quasar
systems to estimate the time delay using a deep learning-based approach that
exploits the capabilities of one-dimensional convolutional neural networks.
Experiments on state-of-the-art simulations of unresolved light curves show the
potential of the proposed method and pave the way for future applications in
time-delay cosmography.
