BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Likelihood Methods in the CRESST-Experiment
DTSTART;VALUE=DATE-TIME:20210901T171500Z
DTEND;VALUE=DATE-TIME:20210901T173000Z
DTSTAMP;VALUE=DATE-TIME:20260710T175155Z
UID:indico-contribution-15870@indico.ific.uv.es
DESCRIPTION:Speakers: Daniel Schmiedmayer (TU-Wien & HEPHY Vienna)\nDespit
 e overwhelming astrophysical evidence for the existence of Dark Matter and
  intense efforts towards its detection\, no clear signal has been found so
  far. Nonetheless\, monumental advancements have been made in the field wh
 ich allowed to put significant constraints on the parameter space for poss
 ible particle candidates. For this reason\, various statistical methods ha
 ve been employed and developed. For many direct dark matter searches the o
 ptimal interval method\, an extension of the maximum gap method developed 
 by Yellin\, has been the golden standard. Recently however\, many experime
 nts have shifted to maximum likelihood based methods for their statistical
  data analysis.\n\nCRESST is a direct Dark Matter search experiment utiliz
 ing scintillating cryogenic bolometers as detectors. This detector princip
 le allows for an extremely low detection threshold as well as particle dis
 crimination. These features allowed CRESST to be one of the leading experi
 ments in low-mass Dark Matter searches for many years.\n\nIn this contribu
 tion an application of the maximum likelihood formalism to the data of CRE
 SST detectors is presented. Recent improvements in detector performance an
 d better understanding of the detector behavior have made the use of a com
 plete and un-binned likelihood approach both possible and beneficial. This
  method enables a better understanding of the properties of individual det
 ectors as well as the use of profile likelihood for limit calculations.\n\
 nhttps://indico.ific.uv.es/event/6178/contributions/15870/
LOCATION:
URL:https://indico.ific.uv.es/event/6178/contributions/15870/
END:VEVENT
END:VCALENDAR