Abstract
Abstract
Filaments are cool and dense plasmas suspended in the hot corona of the Sun and other stars. Accurately estimating their masses is of great significance for understanding subsequent eruptions and induced space weather effects, but it remains hindered by their intrinsic geometric uncertainties, particularly in spatially unresolved stellar observations. To test and calibrate the methods for estimating the masses of stellar filaments, we conduct a statistical Sun-as-a-star analysis of solar filaments, utilizing full-disk H
α
spectroscopic observations from the Chinese H
α
Solar Explorer (CHASE). A total of 1346 filaments, covering a period from 2024 January to 2025 October, are identified via a machine learning segmentation model. We construct their virtual Sun-as-a-star spectra by spatially integrating the filament regions and then obtain their optical parameters by cloud-model fitting. Upon correcting projection effects, we establish a representative three-dimensional morphological scaling of length, apparent width, and line-of-sight depth (
L
:
W
app
:
D
LOS
≈ 4.5:1:1.7), with a median filament depth of about 8000 km. Interestingly, the Sun-as-a-star estimated mass shows high consistency with the resolved intrinsic mass across the full sample, with a log-space regression slope of 1.07. As the first large-sample Sun-as-a-star study of solar filaments, our results provide empirical constraints on filament geometries and masses, offering a critical reference for estimating stellar filament masses based on H
α
spectroscopy.