arXiv subjects

Qihan Zou

Publications and source records attributed to Qihan Zou.

4 recordsLinked to original sources

arXiv2026

A Spatio-Temporal Self-Propagating Log-Gaussian Cox-Hawkes Process for Star Formation Modelling

Stochastic self-propagating star formation models describe galactic structure through local triggering and feedback but are commonly formulated using discrete spatial cells and time steps. We extend the spatio-temporal log-Gaussian Cox-Hawkes framework to obtain the Spatio-Temporal Self-Propagating Log-Gaussian Cox-Hawkes Process, a continuous point process model for the locations and times of star-forming events. The model combines spontaneous formation, correlated environmental effects, outwardly propagating excitation, local inhibition, differential rotation, and saturation. An observation layer transforms the conditional event intensity into idealised maps of instantaneous star-forming arm emissivity and recent young stellar surface brightness. For the selected parameter setting, the displayed realisation exhibits transient flocculent spiral-like patterns without any deterministic spiral geometry being imposed. The Monte Carlo experiment finds similar event production with and without rotation, whereas the stationary-front case produces fewer events. The displayed maps further suggest that differential rotation contributes to the winding and spatial arrangement of activity. The proposed framework provides a basis for future statistical inference from spatially and temporally resolved observations of star formation.

astro-ph.GA↗

arXiv2026

Deep Simulation-Based Inference for Inhomogeneous Bivariate Log-Gaussian Cox Processes

We propose a computationally efficient simulation-based estimation method with a two-step procedure for inhomogeneous bivariate Log-Gaussian Cox Processes. It combines classical Poisson estimation for the first-order parameters with simulation-based inference using neural networks for the latent field parameters. By separating the estimations, it reduces the complexity of high dimensional parameter estimation and the need for the simulation-based method to specify broad parameter ranges in the presence of covariates. In addition, we introduce two dimensional image inputs that enable the model to learn spatial information directly. Simulation results demonstrate that the proposed approach provides accurate estimates of the latent field parameters. We further illustrate the method's practical applicability using the gorilla dataset.

stat.ME↗

arXiv2025

Spatio-Temporal Log-Gaussian Cox-Hawkes Processes with Inhibition and Excitation for Stochastic Star Formation

We establish a connection between the stochastic self-propagating star-formation model and spatio-temporal point processes by showing that the SSPSF update law admits a conditional Poisson representation. Building on this connection, we propose a spatio-temporal log-Gaussian Cox-Hawkes process as a continuous point process model for stochastic star formation. The model represents star-formation events as point patterns driven jointly by deterministic galactic structure, latent spatio-temporal background variation, and dependence on past events. Its key feature is that the deterministic mean field, latent Gaussian random field, and history-dependent interaction field enter through a single log-intensity. This log-scale construction differs from additive Cox-Hawkes formulations and allows the history effect to be signed: past events may either increase or decrease future local intensity while the conditional intensity remains positive. The resulting framework provides an interpretable point-process model for representing latent clustering, self-excitation, local inhibition, and event-driven propagation in stochastic star formation. Beyond linking SSPSF to spatio-temporal point-process theory, it offers a continuous stochastic formulation for analysing the propagation of star formation in galaxies and for interpreting observational surveys of star-forming regions within a unified statistical model.

astro-ph.GA↗

arXiv2025

Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory

Recent advances in quantum computing have significantly accelerated research into quantum-assisted information retrieval and recommender systems, particularly in solving feature selection problems by formulating them as Quadratic Unconstrained Binary Optimization (QUBO) problems executable on quantum hardware. However, while existing work primarily focuses on effectiveness and efficiency, it often overlooks the probabilistic and noisy nature of real-world quantum hardware. In this paper, we propose a solution based on Extreme Value Theory (EVT) to quantitatively assess the usability of quantum solutions. Specifically, given a fixed problem size, the proposed method estimates the number of executions (shots) required on a quantum computer to reliably obtain a high-quality solution, which is comparable to or better than that of classical baselines on conventional computers. Experiments conducted across multiple quantum platforms (including two simulators and two physical quantum processors) demonstrate that our method effectively estimates the number of required runs to obtain satisfactory solutions on two widely used benchmark datasets.

quant-ph↗