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SUMMARY:Reconstructing the Universe properties
DTSTART;VALUE=DATE-TIME:20230531T132500Z
DTEND;VALUE=DATE-TIME:20230531T135000Z
DTSTAMP;VALUE=DATE-TIME:20260815T043657Z
UID:indico-contribution-970@cern.ch
DESCRIPTION:Speakers: Alberto VAZQUEZ ()\n"In the absence of a fundamental
  and well defined theory\, several parameterizations of cosmological funct
 ions have been suggested to get insights of the general DE behaviour and h
 ence to look for possible deviations from the cosmological constant.\nEven
  though these parametric forms usually provide a better fit to the data\, 
 they have the limitation of assuming an a priori functional form which may
  lead to some bias or misleading model-dependent results\, regardless of t
 he DE nature. In this talk\, to avoid these possible issues\, non-parametr
 ic and model-independent techniques are presented\, i.e. Gaussian process 
 and Artificial Neural Networks. They allow us to extract information direc
 tly from the data to detect features within cosmological functions\, for i
 nstance a decrease in the dark energy density component at early times and
  a transition to the phantom divide-line in the EoS."\n\nhttps://indico.un
 ina.it/event/70/contributions/970/
LOCATION:Lisbon
URL:https://indico.unina.it/event/70/contributions/970/
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