TL;DR
This paper develops and applies a new method called APOLLO to detect solar-like oscillations in pre-main sequence stars using Kepler K2 data, successfully identifying candidate stars and demonstrating robustness even with short observational time series.
Contribution
The paper introduces APOLLO, a novel software tool for detecting low signal-to-noise solar-like oscillations in pre-main sequence stars, calibrated with main sequence data and applied to Kepler K2 observations.
Findings
Identified EPIC 205375290 as a candidate pre-main sequence star with solar-like oscillations.
Demonstrated APOLLO's robustness on short-duration TESS data.
Calibrated the method using known Kepler main sequence oscillators.
Abstract
In recent years, our understanding of solar-like oscillations from main sequence to red giant stars has improved dramatically thanks to pristine data collected from space telescopes. One of the remaining open questions focuses around the observational identification of solar-like oscillations in pre-main sequence stars. We aim to develop an improved method to search for solar-like oscillations in pre-main sequence stars and apply it to data collected by the Kepler K2 mission. Our software APOLLO includes a novel way to detect low signal-to-noise ratio solar like oscillations in the presence of a high background level. By calibrating our method using known solar-like oscillators from the main Kepler mission, we apply it to T Tauri stars observed by Kepler K2 and identify several candidate pre-main sequence solar-like oscillators. We find that our method is robust even when applied to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
