arXiv subjects

Chen-Qi Li

Publications and source records attributed to Chen-Qi Li.

4 recordsLinked to original sources

arXiv2026

A Data-Driven Model for $r$-Process Production Patterns

Using the elemental abundances in 68 metal-poor (MP) stars, we present a data-driven model for $r$-process production patterns covering Sr to U. We show that essentially all the $r$-process patterns in those and other test stars can be adequately explained as mixtures of Patterns 1 and 2, which provides theoretical insights into the empirical categories of limited-$r$, $r$-I, and $r$-II stars. We carry out an extensive survey of the yield templates produced by parametric $r$-process calculations. We propose that Pattern 2 may be dominated by a single template and points to regularity in $r$-process production by a subset of neutron star mergers (NSMs), while Pattern 1 is the average superposition of multiple templates and reflects production by other NSMs and perhaps also some magneto-rotational supernovae. We raise possible systematic issues with abundance ratios for elements measured in different ionization states, and highlight the need for examining the Os, Ir, and Pt measurements for HD~122563, which is dominated by Pattern 1 with a prominent Pt peak in our model. If this result is confirmed, the meaning of the limited-$r$ category requires drastic revision. Further measurements of a wider range of $r$-process elements in a larger sample of MP stars are critical to test and improve our model.

astro-ph.SR↗

arXiv2026

Propagating Uncertainties from Nuclear Physics to Gamma-rays in Core Collapse Supernovae

Nuclear yields are powerful probes of supernova explosions, their engines and their progenitors. In addition, as we improve our understanding of these explosions, we can use nuclear yields to probe dense matter and neutrino physics, both of which play a critical role in the central supernova engine. Especially with upcoming gamma-ray detectors that can directly detect radioactive isotopes out to increasing distances from gamma-rays emitted during their decay, nuclear yields have the potential to provide some of the most direct probes of supernova engines and stellar burning. To utilize these probes, we must understand and limit the uncertainties in their production. Uncertainties in the nuclear physics can be minimized by combining both laboratory experiments and nuclear theory. Similarly, astrophysical uncertainties caused by simplified explosion trajectories can be minimized by higher-fidelity stellar-evolution and supernova-engine models. This paper reviews the physics and astrophysics uncertainties in modeling nucleosynthetic yields, identifying the key areas of study needed to maximize the potential of supernova yields as probes of astrophysical transients and dense-matter physics.

astro-ph.HE↗

arXiv2023

Solving Einstein equations using deep learning

Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric systems or ideal matter distribution. However, accurate solutions are crucial, particularly in systems with strong gravitational field such as black holes or neutron stars. In this work, we use neural networks and auto differentiation to solve the Einstein field equations numerically inspired by the idea of physics-informed neural networks (PINNs). By utilizing these techniques, we successfully obtain the Schwarzschild metric and the charged Schwarzschild metric given the energy-momentum tensor of matter. This innovative method could open up a different way for solving space-time coupled Einstein field equations and become an integral part of numerical relativity.

gr-qc↗

arXiv2022

Deep learning on nuclear mass and $α$ decay half-lives

Ab-initio calculations of nuclear masses, the binding energy and the $α$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and neutron number($\mathrm{Z}$) grow. We take advantage of the powerful non-linear transformation and feature representation ability of deep neural network(DNN) to predict the nuclear masses and $α$ decay half-lives. For nuclear binding energy prediction problem we achieve standard deviation $σ=0.263$ MeV on 10-fold cross validation on 2149 nuclei. Word-vectors which are high dimensional representation of nuclei from the hidden layers of mass-regression DNN help us to calculate $α$ decay half-lives. For this task, we get $σ=0.797$ on 100 times 10-fold cross validation on 350 nuclei on $log_{10}T_{1/2}$ and $σ=0.731 $ on 486 nuclei. We also find physical a priori such as shell structure, magic numbers and augmented inputs inspired by Finite Range Droplet Model are important for this small data regression task.

nucl-th↗