Ruixuan Tang | Searcharxiv

arXiv2026

Forward Modeling of the $\delta$ Sct Star V1790 Ori: $\Delta \nu$, $\Omega$, Resolution and Non-adiabatic Effects

We investigate the role of large separation, rotational correction order, structural resolution, and non-adiabatic effects in modelling the rotating $\delta$ Scuti star V1790 Ori. From TESS data, we extract 69 frequencies and determine $\Delta\nu \simeq 82$ $\mu$Hz. Rotating MESA models are computed at low and high resolution; their pulsation frequencies are calculated with GYRE (adiabatic/non-adiabatic, first-order rotation) and FILOU (adiabatic, second-order rotation). Using $\Delta\nu$ as a structural constraint is necessary to reduce model degeneracy. For the selected minimum-misfit reference model, considering only the 40 modes with consistent $(n,\ell,m)$ labels, the RMS$_{40}$ theoretical frequency differences are 0.442 $\mu$Hz (resolution), 0.062 $\mu$Hz (non-adiabatic), and 2.962 $\mu$Hz (GYRE vs FILOU); including all 48 frequencies gives RMS$_{48}$ values of 1.033, 2.326, and 3.931 $\mu$Hz. Relative to observations, higher resolution reduces residuals from 4.457 to 4.387 $\mu$Hz (RMS$_{40}$) and from 4.715 to 4.682 $\mu$Hz (RMS$_{48}$); non-adiabatic effects change them marginally to 4.381 and 4.673 $\mu$Hz. FILOU gives the largest residuals: 5.331 $\mu$Hz (RMS$_{40}$) and 5.270 $\mu$Hz (RMS$_{48}$). Second-order rotation produces the largest frequency shifts, but improving agreement with observations requires denser grids and self-consistent FILOU optimisation. The 260.672 $\mu$Hz peak -- previously identified as the fundamental radial mode -- shows uncertain identification. The results should be interpreted as diagnostics of modelling systematics and mode-identification robustness.

astro-ph.SR↗

arXiv2022

Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification

Some recent works observed the instability of post-hoc explanations when input side perturbations are applied to the model. This raises the interest and concern in the stability of post-hoc explanations. However, the remaining question is: is the instability caused by the neural network model or the post-hoc explanation method? This work explores the potential source that leads to unstable post-hoc explanations. To separate the influence from the model, we propose a simple output probability perturbation method. Compared to prior input side perturbation methods, the output probability perturbation method can circumvent the neural model's potential effect on the explanations and allow the analysis on the explanation method. We evaluate the proposed method with three widely-used post-hoc explanation methods (LIME (Ribeiro et al., 2016), Kernel Shapley (Lundberg and Lee, 2017a), and Sample Shapley (Strumbelj and Kononenko, 2010)). The results demonstrate that the post-hoc methods are stable, barely producing discrepant explanations under output probability perturbations. The observation suggests that neural network models may be the primary source of fragile explanations.

cs.CL↗