In this work, we present EDRIS (French for Distance Estimator for Incomplete
Supernova Surveys), a cosmological inference framework tailored to reconstruct
unbiased cosmological distances from type Ia supernovae light-curve parameters.
This goal is achieved by including data truncation directly in the statistical
model which takes care of the standardization of luminosity distances. It
allows us to build a single-step distance estimate by maximizing the
corresponding likelihood, free from the biases the survey detection limits
would introduce otherwise. Moreover, we expect the current worldwide statistics
to be multiplied by O(10) in the upcoming years. This provides a new challenge
to handle as the cosmological analysis must stay computationally towable. We
show that the optimization methods used in EDRIS allow for a reasonable time
complexity of O($N^2$) resulting in a very fast inference process (O(10s) for
1500 supernovae).

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

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