# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 2 Related Works
### Traditional Stellar Age Determination Methods  
5 Spectroscopic Method  
Spectroscopic methods excel in determining the ages of the main-sequence turnoff and subgiant stars, owing to the broad and non-intersecting isochrone intervals characteristic of these stars. However, for low-mass cooling main-sequence stars or giants, the accuracy of this method is limited due to the narrow and indistinct isochrone intervals. [PERSON] et al. [17] employed spectral data to determine the ages of thousands of F/G-type stars, but the method's efficacy is limited for stars with indistinct spectral features. [PERSON] et al. [18] combined the ubvy\(\beta\) photometric method [19] with precise trigonometric parallax data to determine the ages of 16,682 F and G-type stars, although this approach demands high-quality observational data. [PERSON] et al. [20] and [PERSON] et al. [21] conducted age determinations by comparing the positions of stars on the Hertzsprung-Russell diagram with theoretical predictions of stellar evolutionary models, but the dependency and uncertainty of these models impact the accuracy of the results. [PERSON] et al. [22] utilized LSP3 [23] and asteroseismic features based on Kepler photometry [24] to construct stellar evolutionary models, determining the ages of 150 main-sequence turnoff stars. Although effective in parameter extraction, this method's reliance on complex data limits its widespread application. 6 Gyrochronology Method  
The gyronchronology approach infers stellar ages by analyzing the rotational periods and chromatic characteristics of stars, being particularly applicable to solar-type and young stars whose rotation periods and colors can be measured with high precision. [PERSON] et al. [25] and [PERSON] et al. [26] have employed this methodology for the determination of stellar ages. However, the utility of this method is somewhat constrained due to its reliance on the rotational periods and stellar chromatic attributes, limiting its general applicability. 7 Asteroseismology Method  
The asteroseismology method infers global stellar attributes, including mass and age, based on frequencies generated by stellar oscillations [27]. However, it is primarily applicable to stars with spectral types exhibiting solar-like oscillations [28]. [PERSON] and [PERSON] [29] identified two key asteroseismic parameters: \(\Delta\
u\) (large frequency separation) and \(\
u_{\rm max}\) (frequency of maximum oscillation power), which were used to deduce the mass and radius from oscillation data. [PERSON] et al. [30], [PERSON] et al. [31], and [PERSON] et al. [32] report that asteroseismology-derived stellar ages have an error margin of about 30%. [PERSON] et al. [33] utilized these two asteroseismic parameters and stellar temperature to determine the radius and mass of approximately 7300 stars, determining their ages based on masses and metallicity. [PERSON] and [PERSON] [34] used two asteroseismic parameters, \(\Delta\
u\) and \(\
u_{\rm cross}\), to determine the age of the star HD 49385, with an uncertainty of about 5.18%, but its applicability to a broader range of star types is limited. [PERSON] et al. [35] implemented a novel Bayesian scheme capable of averaging asteroseismic parameters. By applying this scheme to stellar evolution models, they derived the fundamental properties of 33 Kepler planetary candidate stars, with a median statistical uncertainty of 14%, and it can be as good as 5% for red giants. [PERSON] and [PERSON] [36] proposed a Bayesian Estimation of STellar Parameters (BESTP), which is a tool that utilizes Bayesian statistics and the nested sampling Monte Carlo algorithm to search for the stellar models that best match a particular set of classical and asteroseismic constraints from observations. For HD 222076, the uncertainties of estimated masses, radii, and ages are reduced by 2%, 0.5%, and 4.7%. However, Bayesian methods rely heavily on prior knowledge and model selection. If these priors are not accurate or the model does not fit the data perfectly, then the results may be biased.