Strong gravitational lensing is a powerful tool for probing the nature of
dark matter, as lensing signals are sensitive to the dark matter substructure
within the lensing galaxy. We present a comparative analysis of strong
gravitational lensing signatures generated by dark matter subhalo populations
using two different approaches. The first approach models subhalos using an
empirical model, while the second employs the Galacticus semi-analytic model of
subhalo evolution. To date, only empirical approaches have been practical in
the analysis of lensing systems, as incorporating fully physical models was
computationally infeasible. To circumvent this, we utilize a generative machine
learning algorithm, known as a normalizing flow, to learn and reproduce the
subhalo populations generated by Galacticus. We demonstrate that the
normalizing flow algorithm accurately reproduces the Galacticus subhalo
distribution while significantly reducing computation time compared to direct
simulation. Moreover, we find that subhalo populations from Galacticus produce
comparable results to the empirical model in replicating observed lensing
signals under the fiducial dark matter model. This work highlights the
potential of machine learning techniques in accelerating astrophysical
simulations and improving model comparisons of dark matter properties.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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