project

SLDNP

Statistically Learning Dispersed New Physics at the LHC
French National Research Agency (ANR)
  • Funder: French National Research Agency (ANR)Project code: ANR-21-CE31-0023
  • Funder Contribution: 428,001 EUR
Description
"Experiments at the LHC make a tremendous effort to search for new physics in a plethora of final states, exploiting many different analysis techniques. The analysis designs are often motivated by theoretical ideas of how to extend the so-called Standard Model of particle physics. So far, no clear sign of new physics has emerged in any of these searches, and thus limits on the masses of hypothetical new particles of “beyond the Standard Model” (BSM) scenarios are pushed higher and higher. There is a fundamental problem, however, with this channel-by-channel approach: while it is very powerful for discovering or excluding simple, clear-cut signals that show up in...
Description
"Experiments at the LHC make a tremendous effort to search for new physics in a plethora of final states, exploiting many different analysis techniques. The analysis designs are often motivated by theoretical ideas of how to extend the so-called Standard Model of particle physics. So far, no clear sign of new physics has emerged in any of these searches, and thus limits on the masses of hypothetical new particles of “beyond the Standard Model” (BSM) scenarios are pushed higher and higher. There is a fundamental problem, however, with this channel-by-channel approach: while it is very powerful for discovering or excluding simple, clear-cut signals that show up in...
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