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Register a new userA Monte Carlo Study of Single Baryon Reconstruction Method
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A Monte Carlo study for single baryon reconstruction method is presented based on two-body baryonic decays of charmonium, $jJ/psi$, $psi(3686)rightarrowXibarXi$ at BESIII experiment. As a result, we find that the detection efficiency for single baryon reconstruction method can be increased by a factor of $sim$4 relative to the traditional full-reconstruction method. It indicates that single baryon reconstruction method could be used in the other two-body baryonic decays of charmonium, such as $J/psi$, $psi(3686)rightarrowXi(1530)barXi(1530)$, $Xi(1530)barXi$, whose expected yields are estimated based on single baryon reconstruction method. The expected uncertainties for the measurements of the angular distribution parameters are also discussed.
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Distances to high mass star forming regions (HMSFRs) in the Milky Way are a crucial constraint on the structure of the Galaxy. Only kinematic distances are available for a majority of the HMSFRs in the Milky Way. Here we compare the kinematic and parallax distances of 75 Galactic HMSFRs to assess the accuracy of kinematic distances. We derive the kinematic distances using three different methods: the traditional method using the Brand & Blitz (1993) rotation curve (Method A), the traditional method using the Reid et al. (2014) rotation curve and updated Solar motion parameters (Method B), and a Monte Carlo technique (Method C). Methods B and C produce kinematic distances closest to the parallax distances, with median differences of 13% (0.43 kpc) and 17% (0.42 kpc), respectively. Except in the vicinity of the tangent point, the kinematic distance uncertainties derived by Method C are smaller than those of Methods A and B. In a large region of the Galaxy, the Method C kinematic distances constrain both the distances and the Galactocentric positions of HMSFRs more accurately than parallax distances. Beyond the tangent point along longitude=30 degrees, for example, the Method C kinematic distance uncertainties reach a minimum of 10% of the parallax distance uncertainty at a distance of 14 kpc. We develop a prescription for deriving and applying the Method C kinematic distances and distance uncertainties. The code to generate the Method C kinematic distances is publicly available and may be utilized through an on-line tool.
Modification of the hard jet substructure in terms of the Soft Drop jet grooming algorithm observables is studied for three different scenarios of jet quenching in a quark-gluon plasma: i) an explicit enhancement of the parton splitting functions, ii) increased soft gluon emissions induced by an in-medium virtuality gain, and iii) energy loss due to a drag force. Despite the fact that first two scenarios both correspond to a radiative energy loss mechanism and lead to similar modifications of parton showers, they are shown to have very different impacts on the momentum balance of hard subjets. Simulations for heavy-ion collisions based on the second scenario are presented and found to be in a good agreement with the experimental data.
We explore connections between two common methods for quantifying the uncertainty in parton distribution functions (PDFs), based on the Hessian error matrix and Monte-Carlo sampling. CT14 parton distributions in the Hessian representation are converted into Monte-Carlo replicas by a numerical method that reproduces important properties of CT14 Hessian PDFs: the asymmetry of CT14 uncertainties and positivity of individual parton distributions. The ensembles of CT14 Monte-Carlo replicas constructed this way at NNLO and NLO are suitable for various collider applications, such as cross section reweighting. Master formulas for computation of asymmetric standard deviations in the Monte-Carlo representation are derived. A correction is proposed to address a bias in asymmetric uncertainties introduced by the Taylor series approximation. A numerical program is made available for conversion of Hessian PDFs into Monte-Carlo replicas according to normal, log-normal, and Watt-Thorne sampling procedures.
We introduce a semistochastic implementation of the power method to compute, for very large matrices, the dominant eigenvalue and expectation values involving the corresponding eigenvector. The method is semistochastic in that the matrix multiplication is partially implemented numerically exactly and partially with respect to expectation values only. Compared to a fully stochastic method, the semistochastic approach significantly reduces the computational time required to obtain the eigenvalue to a specified statistical uncertainty. This is demonstrated by the application of the semistochastic quantum Monte Carlo method to systems with a sign problem: the fermion Hubbard model and the carbon dimer.
We present a lattice Monte Carlo simulation for a multiblock copolymer chain of length N=240 and microarchitecture $(10-10)_{12}$.The simulation was performed using the Monte Carlo method with the Metropolis algorithm. We measured average energy, heat capacity, the mean squared radius of gyration, and the histogram of cluster count distribution. Those quantities were investigated as a function of temperature and incompatibility between segments, quantified by parameter {omega}. We determined the temperature of the coil-globule transition and constructed the phase diagram exhibiting a variety of patchy nanostructures. The presented results yield a qualitative agreement with those of the off-lattice Monte Carlo method reported earlier, with a significant exception for small incompatibilities,{omega}, and low temperatures, where 3-cluster patchy nanostructures are observed in contrast to the 2-cluster structures observed for the off-lattice $(10-10)_{12}$ chain. We attribute this difference to a considerable stiffness of lattice chains in comparison to that of the off-lattice chains.
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