Proton Spin Structure from Simultaneous Monte Carlo Global QCD Analysis

Despite the great effort and achievements made towards understanding proton spin structure in the past few decades, a complete picture is still elusive. Parton distribution functions (PDFs), which in quantum chromodynamics (QCD) encode the momentum and helicity distributions of quarks and gluons inside a proton, provide the means by which to quantify the proton structure information. Being inherently nonperturbative, PDFs have to be extracted from unpolarized and polarized lepton-hadron and hadron-hadron scattering data. In particular, experiments that measure unpolarized and polarized jet observables can provide insight into the momentum and helicity distributions of gluons, which have generally been more difficult to determine reliably than those of quarks.In the past, extraction of the spin-averaged and spin-dependent (or helicity) PDFs has been performed in separate analyses. In this thesis, we perform the first simultaneous extraction of both types of quantities from deep-inelastic scattering (DIS), Drell-Yan and single jet observables, within the Monte Carlo global QCD analysis framework developed by the Jefferson Lab Angular Momentum (JAM) Collaboration. The results from this work indicate that the gluon helicity distributions depend rather strongly on the theory assumptions on which the global analysis is based, which calls for the need of measurements with higher precision. As an application of the new simultaneous JAM analysis, we perform an impact study for future Electron-Ion Collider (EIC) data with parity-conserving and parity-violating polarization asymmetries on quark and gluon helicity distributions in the proton. The extrapolation of structure functions from the current data is studied for the first time in the context of the impact study. Theory assumptions, such as SU(2) and SU(3) flavor symmetries, are also studied to give a more thorough understanding of the impact of EIC pseudodata on proton spin structure

Direction-of-Arrival Estimation Based on Joint Sparsity

We present a DOA estimation algorithm, called Joint-Sparse DOA to address the problem of Direction-of-Arrival (DOA) estimation using sensor arrays. Firstly, DOA estimation is cast as the joint-sparse recovery problem. Then, norm is approximated by an arctan function to represent joint sparsity and DOA estimation can be obtained by minimizing the approximate norm. Finally, the minimization problem is solved by a quasi-Newton method to estimate DOA. Simulation results show that our algorithm has some advantages over most existing methods: it needs a small number of snapshots to estimate DOA, while the number of sources need not be known a priori. Besides, it improves the resolution, and it can also handle the coherent sources well

Curved backgrounds in emergent gravity

Field theories that are generally covariant but nongravitational at tree level typically give rise to an emergent gravitational interaction whose strength depends on a physical regulator. We consider emergent gravity models in which scalar fields assume the role of clock and rulers, addressing the problem of time in quantum gravity. We discuss the possibility of nontrivial dynamics for clock and ruler fields, and describe some of the consequences of those dynamics for the emergent gravitational theory

Numerical study of hydraulic fracturing fracture area changing rules in underground coal mine

To investigate the relationship between the fractured area created by hydraulic fracturing and various fracturing parameters of underground coal mines, we applied fracture and porous media fluid-solid coupling theory to establish a numerical model of hydraulic fracturing. Three-dimensional numerical simulation of hydraulic fracturing of K1 coal seam in a coalmine was performed using the proposed numerical model. We examined the relations between the fractured area and the injection volume, injection rate, and viscosity of the fracturing fluid. The results showed that the fractured area increased with increasing injection rate, however, the extension rate slowed down; the fractured area initially increased then decreased with increasing viscosity; the fractured area increases rapidly with the increase of the water injection volume at the beginning, then begin to slow, eventually approximate linear growth

Experimental study on characteristics of self-excited oscillation pulsed water jet

To promote the application of self-excited oscillation pulsed water jet in various fields, various characteristics of self-excited oscillation pulsed water jet were studied experimentally. A test system of self-excited oscillation pulsed water jet characteristics was designed, and it is composed of pulsed jet producer devices, particle image velocity (PIV) measuring system, pressure pulse test device and the confining pressure cavity suitable for a PIV test. The characteristics of flow field, pressure oscillation, pulsed cavitation and acoustic shock of pulsed jet were researched. The results showed that the axis velocity vector at nozzle outlet changes periodically, and gradually becomes smaller away from the nozzle. The peak pressure of self-excited oscillation pulsed water jet is 2.5 times higher than the common continuous jet. The wave crest and the wave trough of pulsed jet are not completely symmetric. As the pump pressure increases, the length of bubble cloud increases, and it first increases and then decreases with confining pressure increasing. The vibration acceleration of sonic boom increases and then decreases as pump pressure increases, and decreases steadily with confining pressure increasing

A Logic Approach to Granular computing

This article was originally published by the International Journal of Cognitive Informatics and Natural IntelligenceGranular computing is an emerging field of study that attempts to formalize and explore methods and heuristics of human problem solving with multiple levels of granularity and abstraction. A fundamental issue of granular computing is the representation and utilization of granular structures. The main objective of this article is to examine a logic approach to address this issue. Following the classical interpretation of a concept as a pair of intension and extension, we interpret a granule as a pair of a set of objects and a logic formula describing the granule. The building blocks of granular structures are basic granules representing an elementary concept or a piece of knowledge. They are treated as atomic formulas of a logic language. Different types of granular structures can be constructed by using logic connectives. Within this logic framework, we show that rough set analysis (RSA) and formal concept analysis (FCA) can be interpreted uniformly. The two theories use multilevel granular structures but differ in their choices of definable granules and granular structures.NSERC Canada Discovery gran

Accessing gluon polarization with high-PTP_T hadrons in SIDIS

A recent global QCD analysis of jet production and other polarized scattering data has found the presence of negative solutions for the gluon helicity distribution in the proton, $\Delta g$, along with the traditional $\Delta g > 0$ solutions. We consider polarized semi-inclusive deep-inelastic scattering for hadrons produced with large transverse momentum as a means of constraining the dependence of $\Delta g$ on the parton momentum fraction, $x$. Focusing on the double longitudinal spin asymmetry, we identify the kinematics relevant for future experiments at Jefferson Lab and the Electron-Ion Collider which are particularly sensitive to the polarized gluon channel and could discriminate between the different $\Delta g$ behaviors.Comment: 22 pages, 7 figure

In Search of Effective Granulization with DTRS for Ternary Classification

This article was originally published by the International Journal of Cognitive Informatics and Natural IntelligenceDecision-Theoretic Rough Set (DTRS) model provides a three-way decision approach to classification problems, which allows a classifier to make a deferment decision on suspicious examples, rather than being forced to make an immediate determination. The deferred cases must be reexamined by collecting further information. Although the formulation of DTRS is intuitively appealing, a fundamental question that remains is how to determine the class of the deferment examples. In this paper, the authors introduce an adaptive learning method that automatically deals with the deferred examples by searching for effective granulization. A decision tree is constructed for classification. At each level, the authors sequentially choose the attributes that provide the most effective granulization. A subtree is added recursively if the conditional probability lies in between of the two given thresholds. A branch reaches its leaf node when the conditional probability is above or equal to the first threshold, or is below or equal to the second threshold, or the granule meets certain conditions. This learning process is illustrated by an example.NSERC Alexander Graham Bell Canada Graduate Scholarship and NSERC Canada Discovery grant

Sorting photons by radial quantum number

The Laguerre-Gaussian (LG) modes constitute a complete basis set for representing the transverse structure of a {paraxial} photon field in free space. Earlier workers have shown how to construct a device for sorting a photon according to its azimuthal LG mode index, which describes the orbital angular momentum (OAM) carried by the field. In this paper we propose and demonstrate a mode sorter based on the fractional Fourier transform (FRFT) to efficiently decompose the optical field according to its radial profile. We experimentally characterize the performance of our implementation by separating individual radial modes as well as superposition states. The reported scheme can, in principle, achieve unit efficiency and thus can be suitable for applications that involve quantum states of light. This approach can be readily combined with existing OAM mode sorters to provide a complete characterization of the transverse profile of the optical field