1,017 research outputs found
Model discrimination in pseudoscalar-meson photoproduction
To learn about a physical system of interest, experimental results must be able to discriminate among models. We introduce a geometrical measure to quantify the distance between models for pseudoscalar-meson photoproduction in amplitude space. Experimental observables, with finite precision, map to probability distributions in amplitude space, and the characteristic width scale of such distributions needs to be smaller than the distance between models if the observable data are going to be useful. We therefore also introduce a method for evaluating probability distributions in amplitude space that arise as a result of one or more measurements, and show how one can use this to determine what further measurements are going to be necessary to be able to discriminate among models
Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system
Still under debate is the question of whether machine learning is capable of
going beyond black-box modeling for complex physical systems. We investigate
the generalizing and interpretability properties of learning algorithms. To
this end, we use supervised and unsupervised learning to infer the phase
boundaries of the active Ising model, starting from an ensemble of
configurations of the system. We illustrate that unsupervised learning
techniques are powerful at identifying the phase boundaries in the control
parameter space, even in situations of phase coexistence. It is demonstrated
that supervised learning with neural networks is capable of learning the
characteristics of the phase diagram, such that the knowledge obtained at a
limited set of control variables can be used to determine the phase boundaries
across the phase diagram. In this way, we show that properly designed
supervised learning provides predictive power to regions in the phase diagram
that are not included in the training phase of the algorithm. We stress the
importance of introducing interpretability methods in order to perform a
physically relevant classification of the phases with deep learning
Amplitude analysis and the nature of the Zc(3900)
The microscopic nature of the XYZ states remains an unsettled topic. We show
how a thorough amplitude analysis of the data can help constraining models of
these states. Specifically, we consider the case of the Zc(3900) peak and
discuss possible scenarios of a QCD state, virtual state, or a kinematical
enhancement. We conclude that current data are not precise enough to
distinguish between these hypotheses, however, the method we propose, when
applied to the forthcoming high-statistics measurements should shed light on
the nature of these exotic enhancements.Comment: 14 pages, 10 figures, 3 tables. Version accepted for publication on
Phys.Lett.
On the and Photoproduction Beam Asymmetry at High Energies
We show that, in the Regge limit, beam asymmetries in and
photoproduction are sensitive to hidden strangeness components. Under
reasonable assumptions about the couplings we estimate the contribution of the
Regge pole, which is expected to be the dominant hidden strangeness
contribution. The ratio of the asymmetries in and production is
estimated to be close to unity in the forward region at the photon energy ~GeV, relevant for the upcoming
measurements at Jefferson Lab.Comment: 9 pages, 4 figure
Structure of Pion Photoproduction Amplitudes
We derive and apply the finite energy sum rules to pion photoproduction. We
evaluate the low energy part of the sum rules using several state-of-the-art
models. We show how the differences in the low energy side of the sum rules
might originate from different quantum number assignments of baryon resonances.
We interpret the observed features in the low energy side of the sum rules with
the expectation from Regge theory. Finally, we present a model, in terms of a
Regge-pole expansion, that matches the sum rules and the high-energy
observables.Comment: 19 pages, 15 figures and 4 table
Analyticity constraints for hadron amplitudes : going high to heal low energy issues
Analyticity constitutes a rigid constraint on hadron scattering amplitudes. This property is used to relate models in different energy regimes. Using meson photoproduction as a benchmark, we show how to test contemporary low-energy models directly against high-energy data. This method pinpoints deficiencies of the models and treads a path to further improvement. The implementation of this technique enables one to produce more stable and reliable partial waves for future use in hadron spectroscopy and new physics searches
Finite-Energy Sum Rules in Eta Photoproduction off the Nucleon
The reaction is studied in the high-energy regime
(with photon lab energies GeV) using
information from the resonance region through the use of finite-energy sum
rules (FESR). We illustrate how analyticity allows one to map the t-dependence
of the unknown Regge residue functions. We provide predictions for the energy
dependence of the beam asymmetry at high energies.Comment: Joint Physics Analysis Cente
- …