Fitting and selecting scattering data

TL;DR

This paper addresses the challenge of selecting mutually consistent scattering data from multiple sources, proposing a self-consistent method that maximizes data consensus using flexible models and theoretical constraints.

Contribution

It introduces a novel self-consistent approach for selecting compatible scattering data by maximizing database consensus with minimal theoretical assumptions.

Findings

Effective data selection method demonstrated on $oldsymbol{ ext{πN}}$ and $oldsymbol{ ext{NN}}$ scattering.

Combines long-distance field theory constraints with minimal modeling.

Enables consistent analysis of experimental scattering data.

Abstract

The main purpose of scattering experiments is to unveil the underlying structure of the colliding particles and their interaction. Typically one measures scattering observables (cross sections and polarizations) at discrete angles and energies and mutually consistent data may validate or falsify proposed theories or models. However, the accumulation of data from different laboratories while potentially improves the statistical significance it may sometimes generate mutually inconsistent data as a side-effect. Thus, some decision has to be made on what are the maximal amount of data which are mutually compatible. We show elastic $\pi N $ and $NN$ scattering as prominent examples where this selection is called for. We discuss how it can be done in a self-consistent manner invoking a principle of maximal consensus of the database and with the help of a sufficiently flexible model involving a minimal number of theoretical assumptions. In the NN case this has become possible with a combination of long distance field theoretical constraints at the hadronic level such as pion exchanges and electromagnetic effects and a coarse graining of the unknown interaction over the shortest de Broglie wavelength being probed in the scattering process.