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SUMMARY:The promise of Simulation-based inference for the dark sector
DTSTART:20260513T090000Z
DTEND:20260513T100000Z
DTSTAMP:20260523T054000Z
UID:indico-event-1649224@indico.cern.ch
CONTACT:EP-seminars.colloquia@cern.ch
DESCRIPTION:Speakers: Roberto Trotta\n\nIn virtue of their size and comple
 xity\, cosmological and astrophysical data are rapidly becoming intractabl
 e by traditional statistical techniques. Unravelling the twin mysteries of
  dark energy and dark matter requires new data analysis methods capable of
  extracting robust and reliable knowledge from upcoming data streams\, inc
 luding the Euclid satellite\, the Nancy Grace Roman space telescope and th
 e Vera Rubin observatory. I will focus on recent advances in simulation-b
 ased inference\, probabilistic diffusion models and self-supervised learni
 ng\, which together are enabling fast\, scalable inference that achieves s
 tate-of-the-art verifiable performance in a variety of settings\, includin
 g supernova type Ia cosmology\, photometric redshift estimation and weak l
 ensing calibration. Methodological issues like domain shift and model miss
 pecification will also be addressed.\nBio: Roberto Trotta is professor of
  theoretical physics at the International School for Advanced Study in Tri
 este\, Italy\, where is the Director of the Interdisciplinary Laboratory\,
  and a visiting professor at Imperial College London\, where he was a prof
 essor of Astrostatistics. His research focuses on cosmology\, machine lear
 ning and data science\, with applications to early universe cosmology\, da
 rk matter direct and indirect detection\, supernova type Ia and large scal
 e structure data. He was awarded the 2018 Chair Lemaître of the Universit
 y of Louvain for his work on astrostatistics and the 2020 Annie Maunder me
 dal of the Royal Astronomical Society for his contributions to public enga
 gement.\n \nCoffee served at 10h30\n\nhttps://indico.cern.ch/event/164922
 4/\n\nZoom: https://cern.zoom.us/j/98545267593?pwd=akZWdmlyK01zbjFQa0x2c2Z
 XWW9ydz09
LOCATION:500/1-001 - Main Auditorium (CERN)
URL:https://indico.cern.ch/event/1649224/
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