Change Details

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. This revision also takes the opportunity to perform some modernisation of some of the affected classes.

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 - Gamma0, m0, m0 + Gamma0 for each component in the model (resonant and nonresonant). We intend to extend this to time-dependent models later. This revision also takes the opportunity to perform some modernisation of some of the affected classes.

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 - Gamma0, m0, m0 + Gamma0 for each component in the model (resonant and nonresonant). We intend to extend this to time-dependent models later. This revision also takes the opportunity to perform some modernisation of some of the affected classes.