Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow well can we guess theoretical uncertainties?
122
0
0.0
(
0
)
Authors
Andre David PHn Department
Ask ChatGPT about the research
No Arabic abstract
The problem of estimating the effect of missing higher orders in perturbation theory is analyzed with emphasis in the application to Higgs production in gluon-gluon fusion. Well-known mathematical methods for an approximated completion of the perturbative series are applied with the goal to not truncate the series, but complete it in a well-defined way, so as to increase the accuracy - if not the precision - of theoretical predictions. The uncertainty arising from the use of the completion procedure is discussed and a recipe for constructing a corresponding probability distribution function is proposed.
rate research
Read More
Using the perturbative QCD amplitudes for $Bto pipi$ and $Bto Kpi$, we have performed an extensive study of the parameter space where the theoretical predictions for the branching ratios are consistent with recent experimental data. From this allowed range of parameter space, we predict the mixing induced CP asymmetry for $B to pi^+pi^-$ with about 11% uncertainty and the other CP asymmetries for $Bto pipi$, $Kpi$ with 40% ~ 47% uncertainty. These errors are expected to be reduced as we restrict the parameter space by studying other decay modes and by further improvements in the experimental data.
The seesaw mechanism for the small neutrino mass has been a popular paradigm, yet it has been believed that there is no way to test it experimentally. We present a conceivable outcome from future experiments that would convince us of the seesaw mechanism. It would involve a variety of data from LHC, ILC, cosmology, underground, and low-energy flavor violation experiments to establish the case.
The field of extrasolar planets has rapidly expanded to include the detection of planets with masses smaller than that of Uranus. Many of these are expected to have little or no hydrogen and helium gas and we might find Earth analogs among them. In this paper we describe our detailed interior models for a rich variety of such massive terrestrial and ocean planets in the 1-to-10 earth-mass range (super-Earths). The grid presented here allows the characterization of the bulk composition of super-Earths detected in transit and with a measured mass. We show that, on average, planet radius measurements to better than 5%, combined with mass measurements to better than 10% would permit us to distinguish between an icy or rocky composition. This is due to the fact that there is a maximum radius a rocky terrestrial planet may achieve for a given mass. Any value of the radius above this maximum terrestrial radius implies that the planet contains a large (> 10%) amount of water (ocean planet).
In this paper, we consider the problem of learning models with a latent factor structure. The focus is to find what is possible and what is impossible if the usual strong factor condition is not imposed. We study the minimax rate and adaptivity issues in two problems: pure factor models and panel regression with interactive fixed effects. For pure factor models, if the number of factors is known, we develop adaptive estimation and inference procedures that attain the minimax rate. However, when the number of factors is not specified a priori, we show that there is a tradeoff between validity and efficiency: any confidence interval that has uniform validity for arbitrary factor strength has to be conservative; in particular its width is bounded away from zero even when the factors are strong. Conversely, any data-driven confidence interval that does not require as an input the exact number of factors (including weak ones) and has shrinking width under strong factors does not have uniform coverage and the worst-case coverage probability is at most 1/2. For panel regressions with interactive fixed effects, the tradeoff is much better. We find that the minimax rate for learning the regression coefficient does not depend on the factor strength and propose a simple estimator that achieves this rate. However, when weak factors are allowed, uncertainty in the number of factors can cause a great loss of efficiency although the rate is not affected. In most cases, we find that the strong factor condition (and/or exact knowledge of number of factors) improves efficiency, but this condition needs to be imposed by faith and cannot be verified in data for inference purposes.
We consider the production of charmed baryons and mesons in the proton-antiproton binary reactions at the energies of the future $bar{P}$ANDA experiment. To describe these processes in terms of hadronic interaction models, one needs strong couplings of the initial nucleons with the intermediate and final charmed hadrons. Similar couplings enter the models of binary reactions with strange hadrons. For both charmed and strange hadrons we employ the strong couplings and their ratios calculated from QCD light-cone sum rules. In this method finite masses of $c$ and $s$ quarks are taken into account. Employing the Kaidalovs quark-gluon string model with Regge poles and adjusting the normalization of the amplitudes in this model to the calculated strong couplings, we estimate the production cross section of charmed hadrons. For $pbar{p}to Lambda_cbar{Lambda}_c$ it can reach several tens of $nb$ at $p_{lab}= 15 {GeV}$, whereas the cross sections of $Sigma_c$ and $D$ pair production are predicted to be smaller.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
