REVIEW 2 cited by
Feasibility studies for the measurement of time-like proton electromagnetic form factors from $\bar{p}p \rightarrow \mu^+\mu^-$ at $\overline{\textrm{P}}\textrm{ANDA}$ at FAIR
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
This paper reports on Monte Carlo simulation results for future measurements of time-like proton electromagnetic form factors, $|G_{E}|$ and $|G_{M}|$, using the $\bar{p} p \rightarrow \mu^{+} \mu^{-}$ reaction at $\overline{\textrm{P}}\textrm{ANDA}$ (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at $\overline{\textrm{P}}\textrm{ANDA}$, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is $\bar{p} p \rightarrow \pi^{+} \pi^{-}$, due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distributions of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.
Forward citations
Cited by 2 Pith papers
-
Input-to-State Stability Certification via Projection Residuals for Koopman Learning Control of Nonlinear Repetitive Systems
A practical input-to-state stability certificate for Koopman learning control is derived, separating prediction residuals from selected-channel margins and projection residuals.
-
Physiological and Affective Computing through Thermal Imaging: A Survey
Thermal imaging can track physiological and affective states, but most evidence comes from controlled laboratories, and this survey organizes pipelines and open challenges for moving to real-world use.
Discussion (0). Continue with ORCID to comment.