SearcharxivSearch

arXiv subjects

Xiaoya Sun

Publications and source records attributed to Xiaoya Sun.

2 recordsLinked to original sources

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

Local False Discovery Rate Estimation with Competition-Based Procedures for Variable Selection

Multiple hypothesis testing has been widely applied to problems dealing with high-dimensional data, e.g., selecting significant variables and controlling the selection error rate. The most prevailing measure of error rate used in the multiple hypothesis testing is the false discovery rate (FDR). In recent years, local false discovery rate (fdr) has drawn much attention, due to its advantage of accessing the confidence of individual hypothesis. However, most methods estimate fdr through p-values or statistics with known null distributions, which are sometimes not available or reliable. Adopting the innovative methodology of competition-based procedures, e.g., knockoff filter, this paper proposes a new approach, named TDfdr, to local false discovery rate estimation, which is free of the p-values or known null distributions. Simulation results demonstrate that TDfdr can accurately estimate the fdr with two competition-based procedures. In real data analysis, the power of TDfdr on variable selection is verified on two biological datasets.

stat.ME