arXiv subjects

Yeqi Fang

Publications and source records attributed to Yeqi Fang.

3 recordsLinked to original sources

arXiv2025

Wormhole Solutions and Pre-inflationary Epoch in $F(R, T)$ Gravity with Axion Fields

This study investigates axion-dilaton wormhole solutions within the framework of $F(R,T)$ gravity to resolve the issue of insufficient inflationary e-folds in the no-boundary proposal. By examining both Giddings-Strominger and expanding wormhole solutions in asymptotically flat Euclidean spacetime, we demonstrate that the matter-geometry coupling induces complex dynamical oscillations in the scale factor and the dilaton field. These complex oscillatory modes significantly reduce the Euclidean action compared to standard general relativity, consequently enhancing the nucleation probability. Furthermore, we extend this theoretical setup to a ``wineglass'' half-wormhole model in Euclidean Anti-de Sitter (EAdS) spacetime. Our analysis yields a specific constraint on the coupling parameter. This constraint introduces an unstable maximum in the potential and simultaneously decreases the action. The presence of this unstable maximum fundamentally alters the probability distribution of initial states, rendering the evolution of universes from high-potential regions far more probable. Consequently, this approach significantly increases the likelihood of long-lasting inflation, providing a theoretical pathway to reconcile the no-boundary proposal with astronomical observations requiring sustained cosmic expansion.

gr-qc↗

arXiv2025

Two-stage deep learning framework for the restoration of incomplete-ring PET images

Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector rings for full angular coverage and reliable data collection. However, incomplete-ring PET scanners have emerged due to hardware failures, cost constraints, or specific clinical needs. Standard reconstruction algorithms often suffer from performance degradation with these systems because of reduced data completeness and geometric inconsistencies. We present a two-stage deep-learning framework that, without incorporating any time-of-flight (TOF) information, restores high-quality images from data with about 50% missing coincidences - double the loss levels previously addressed by CNN-based methods. The pipeline operates in two stages: a projection-domain Attention U-Net first predicts the missing sections of the sinogram by leveraging spatial context from neighbouring slices, after which the completed data are reconstructed with OSEM algorithm and passed to a cascaded U-Net & warm-start diffusion model for image refinement. This module starts the reverse diffusion process from the U-Net coarse prediction rather than pure Gaussian noise. Using 613 simulated brain volumes from real scans (196 healthy brain samples, 217 Alzheimer's disease samples, and 200 Mild Cognitive Impairment samples), the result shows that our model successfully preserves most anatomical structures and tracer distribution features with PSNR of 38.18 to 38.59 dB and SSIM of 0.9904 to 0.9925. Our two-stage deep-learning framework effectively restores high-quality PET images from over 50% incomplete-ring data, achieving near-complete anatomical fidelity and robust performance without requiring TOF information.

cs.CV↗

arXiv2024

Identifying Black Holes Through Space Telescopes and Deep Learning

The EHT has captured a series of images of black holes. These images could provide valuable information about the gravitational environment near the event horizon. However, accurate detection and parameter estimation for candidate black holes are necessary. This paper explores the potential for identifying black holes in the ultraviolet band using space telescopes. We establish a data pipeline for generating simulated observations and present an ensemble neural network model for black hole detection and parameter estimation. The model achieves mean average precision [0.5] values of 0.9176 even when reaching the imaging FWHM ($\theta_c$) and maintains the detection ability until $0.54\theta_c$. The parameter estimation is also accurate. These results indicate that our methodology enables super-resolution recognition. Moreover, the model successfully detects the shadow of M87* from background noise and other celestial bodies and estimates its inclination and positional angle. Our work demonstrates the feasibility of detecting black holes in the ultraviolet band and provides a new method for black hole detection and further parameter estimation.

astro-ph.IM↗