Automated asteroseismic peak detections
Andr\'es Garc\'ia Saravia Ortiz de Montellano, Saskia Hekker and, Nathalie Theme{\ss}l

TL;DR
This paper introduces an automated peak detection algorithm for solar-like oscillations in stellar data, enabling efficient and reliable analysis of large datasets from space observatories like Kepler.
Contribution
The paper presents a novel automated peak detection method that accurately identifies and characterizes solar-like oscillations without human intervention, improving efficiency over traditional visual inspection.
Findings
Reliable detection and characterization of solar-like oscillations.
Provides metrics for false positive and false negative rates.
Enables automated analysis of thousands of stars.
Abstract
Space observatories such as have provided data that can potentially revolutionise our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible in a power density spectrum. Identification of oscillation modes is usually done by visual inspection which is time-consuming and has a degree of subjectivity. Here, we present a peak detection algorithm specially suited for the detection of solar-like oscillations. It…
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