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G. Liang

Publications and source records attributed to G. Liang.

5 recordsLinked to original sources
arXiv2025

UK APAP R-matrix electron-impact excitation cross-sections for modelling laboratory and astrophysical plasma

Systematic R-matrix calculations of electron-impact excitation for ions of astrophysical interest have been performed since 2007 for many iso-electronic sequences as part of the UK Atomic Process for Astrophysical Plasma (APAP) network. Rate coefficients for Maxwellian electron distributions have been provided and used extensively in the literature and many databases for astrophysics. Here, we provide averaged collision strengths to be used to model plasma where electrons are non-Maxwellian, which often occur in laboratory and astrophysical plasma. We also provide for many ions new Maxwellian-averaged collision strengths which include important corrections to the published values. The H- and He-like atomic data were recently made available in Mao+(2022). Here, we provide data for ions of the Li-, Be-, B-, C-, N-, O-, Ne-, Na-, and Mg-like sequences.

physics.atom-ph↗
arXiv2020

A deep learning view of the census of galaxy clusters in IllustrisTNG

The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc = 81%, or surface brightness concentrations, giving BAcc = 73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.

astro-ph.CO↗
arXiv2010

A Functional Approach to FBSDEs and Its Application in Optimal Portfolios

In Liang et al (2009), the current authors demonstrated that BSDEs can be reformulated as functional differential equations, and as an application, they solved BSDEs on general filtered probability spaces. In this paper the authors continue the study of functional differential equations and demonstrate how such approach can be used to solve FBSDEs. By this approach the equations can be solved in one direction altogether rather than in a forward and backward way. The solutions of FBSDEs are then employed to construct the weak solutions to a class of BSDE systems (not necessarily scalar) with quadratic growth, by a nonlinear version of Girsanov's transformation. As the solving procedure is constructive, the authors not only obtain the existence and uniqueness theorem, but also really work out the solutions to such class of BSDE systems with quadratic growth. Finally an optimal portfolio problem in incomplete markets is solved based on the functional differential equation approach and the nonlinear Girsanov's transformation.

math.PR↗
arXiv2010

On Girsanov's transform for backward stochastic differential equations

By using a simple observation that the density processes appearing in Ito's martingale representation theorem are invariant under the change of measures, we establish a non-linear version of the Cameron-Martin formula for solutions of a class of systems of quasi-linear parabolic equations with non-linear terms of quadratic growth. We also construct a local stochastic flow and establish a Bismut type formula for such system of quasi-linear PDEs. Gradient estimates are obtained in terms of the probability representation of the solution. Another interesting aspect indicated in the paper is the connection between the non-linear Cameron-Martin formula and a class of forward-backward stochastic differential equations(FBSDEs).

math.PR↗