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SUMMARY:AI “Shallow&quot\; and “Deep&quot\; Learning of the Dark Unive
 rse - Prof. Ofer Lahav (University College London)
DTSTART:20220303T160000Z
DTEND:20220303T170000Z
UID:TALK168251@talks.cam.ac.uk
CONTACT:Nicolas Laporte
DESCRIPTION:Abstract:  The Cosmological Constant (Lambda) + Cold Dark Matt
 er model has survived many observational tests\, subject to some ‘tensio
 ns’ in the Hubble Constant and the ‘clumpiness' parameter.  When shall
  we stop? Could the impressive data sets be used to address entirely new q
 uestions? \nIn that spirit\, we review recent  results for cosmological pa
 rameters and mass mapping as well as unexpected outcomes from the Dark Ene
 rgy Survey and other experiments. Some of the analyses use AI  “Shallow
 ” and “Deep”  Learning approaches\, with implications for the new su
 rveys (e.g. DESI\, Rubin-LSST and Euclid). We also discuss training of the
  next generation of scientists\, with the example of UCL’s Centre for Do
 ctoral Training in Data Intensive Science. 
LOCATION:Hoyle Lecture Theatre (sign-up needed) + ONLINE - Details will be
  sent by email
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