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SUMMARY:Learning the likelihood with tree boosting for extracting EFT para
meters
DTSTART;VALUE=DATE-TIME:20221208T090000Z
DTEND;VALUE=DATE-TIME:20221208T100000Z
DTSTAMP;VALUE=DATE-TIME:20230329T090629Z
UID:indico-event-8711@cern.ch
DESCRIPTION:\n Various extensions of the standard model of particle physic
s (SM) predict anomalous interactions at the weak scale. Effective field t
heory (EFT)\, a generalized extension of the SM\, comprises all the possib
le operators of dimensions greater than four\, satisfying the SM’s symme
tries [1]. The EFT operators modify the production and decay kinematics of
the particles involved in LHC collisions compared with those predicted by
the SM. We have recently developed a tree boosting algorithm for collider
measurements of multiple EFT-operator coefficients [2\, 3]. The design of
the discriminant exploits per-event information from the simulated data s
ets that encodes the predictions for different values of the coefficients.
This “Boosted Information Tree” algorithm provides nearly optimal dis
crimination power order-by-order in the expansion of the EFT-operator coef
ficients and approaches the optimal likelihood ratio test-statistic. In th
is talk\, we will discuss the algorithm and show its application to the Hi
ggsstrahlung process for different types of modeling.\n\nhttps://indico.ti
fr.res.in/indico/conferenceDisplay.py?confId=8711
LOCATION:TIFR\, Mumbai AG-66
URL:https://indico.tifr.res.in/indico/conferenceDisplay.py?confId=8711
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