Abstract
Consistent and accurate estimation of stellar parameters is of great importance for information retrieval in astrophysical research. The parameters
span a wide range from effective temperature to rotational velocity. We propose to estimate the stellar parameters directly from spectral signals coming from the HARPS-N spectrograph pipeline before any spectrum-processing steps are applied to extract the 1D spectrum. We use residual networks
and an attention-based model to estimate the stellar parameters. The models estimate both mean and uncertainty of the stellar parameters through
the parameters of a Gaussian distribution. The estimated distributions create a basis to generate data-driven Gaussian confidence intervals for the
estimated stellar parameters. We show that residual networks and attention-based models can estimate the stellar parameters with high accuracy for
low Signal-to-noise ratio (SNR) compared to previous methods. With an observation of the Sun from the HARPS-N spectrograph, we show that the models can estimate stellar parameters from real observational data.
span a wide range from effective temperature to rotational velocity. We propose to estimate the stellar parameters directly from spectral signals coming from the HARPS-N spectrograph pipeline before any spectrum-processing steps are applied to extract the 1D spectrum. We use residual networks
and an attention-based model to estimate the stellar parameters. The models estimate both mean and uncertainty of the stellar parameters through
the parameters of a Gaussian distribution. The estimated distributions create a basis to generate data-driven Gaussian confidence intervals for the
estimated stellar parameters. We show that residual networks and attention-based models can estimate the stellar parameters with high accuracy for
low Signal-to-noise ratio (SNR) compared to previous methods. With an observation of the Sun from the HARPS-N spectrograph, we show that the models can estimate stellar parameters from real observational data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Northern Lights Deep Learning Workshop 2021 |
| Number of pages | 7 |
| Publication date | 2021 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | Northern Lights Deep Learning Workshop 2021 - Virtual event, Tromsø , Norway Duration: 18 Jan 2021 → 20 Jan 2021 http://www.nldl.org |
Workshop
| Workshop | Northern Lights Deep Learning Workshop 2021 |
|---|---|
| Location | Virtual event |
| Country | Norway |
| City | Tromsø |
| Period | 18/01/2021 → 20/01/2021 |
| Internet address |