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SUMMARY:Learning Gravity from Data: Model-Independent Reconstruction of th
 e Cosmic Expansion and Modified Gravity Theories
DTSTART;VALUE=DATE-TIME:20260522T020000Z
DTEND;VALUE=DATE-TIME:20260522T024000Z
DTSTAMP;VALUE=DATE-TIME:20260530T122521Z
UID:indico-contribution-2434@indico.itp.ac.cn
DESCRIPTION:Speakers: Konstantinos Dialektopoulos (University of Malta)\nT
 he standard cosmological model is under increasing observational pressure.
  Persistent tensions in the Hubble constant and the growth of structure su
 ggest either unaccounted systematics or genuine new physics beyond ΛCDM 
 — and distinguishing between these possibilities demands data analysis m
 ethods that do not smuggle in theoretical priors through the back door. In
  this talk\, I present a programme of model-independent reconstruction of 
 late-time cosmology using machine learning to probe both the expansion his
 tory of the Universe and the underlying theory of gravity.\n\nhttps://indi
 co.itp.ac.cn/event/419/contributions/2434/
LOCATION:Small Lecture Hall\, Chongqing University Library (图书馆小
 报告厅) 1F-9
URL:https://indico.itp.ac.cn/event/419/contributions/2434/
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