Change Details

Repurposing the LauFitter class to optimise ASq rather than Likelihood, ASqMax is found by minimising -ASq w.r.t. m13Sq and m23Sq. This is done from multiple starting points: at cos(helicity) = -1, 0, 1 and m = m0 for each component in the model (resonant and nonresonant). We intend to extend this to time-dependent models later.

As described in T202, currently the maximum value of ASq (used for the accept/reject method of toy generation) for a given DP model has to be determined by trial and error and then set by hand. This revision repurposes the LauFitter class to optimise ASq rather than a likelihood function: ASqMax is found by minimising `-ASq` w.r.t. `m13Sq` and `m23Sq`. This is done from multiple starting points: at cos(helicity) = -1, 0, 1 and m = m0 for each component in the model (resonant and nonresonant). We intend to extend this to time-dependent models later.

RepurposingAs described in T202, currently the maximum value of ASq (used for the accept/reject method of toy generation) for a given DP model has to be determined by trial and error and then set by hand. This revision repurposes the LauFitter class to optimise ASq rather than La likelihood, function: ASqMax is found by minimising `-ASq w` w.r.t. m13Sq and m23Sq. This is done`m13Sq` and `m23Sq`. This is done from multiple starting points: at cos(helicity) = -1, 0, 1 and m = m0 for each component in the model (resonant and nonresonant). We intend to extend this to time-dependent models later.