Minimal Data Fidelity for Detection of Stellar Features or Companions

TL;DR

This study uses a multifractal framework to determine the minimum data quality needed to reliably detect stellar features and exoplanets amidst noise, informing observational strategies.

Contribution

It introduces a novel multifractal analysis method to establish detection thresholds for stellar features and planets in noisy spectroscopic data.

Findings

Detects 1% stellar spots and faculae at S/N of 35 and 60 with resolution above 20,000

Detects 10 m/s exoplanets at S/N of 600

Provides lower limits on resolution and S/N for robust feature detection

Abstract

Technological advances in instrumentation have led to an exponential increase in exoplanet detection and scrutiny of stellar features such as spots and faculae. While the spots and faculae enable us to understand the stellar dynamics, exoplanets provide us with a glimpse into stellar evolution. While the ubiquity of noise (e.g., telluric, instrumental, or photonic) is unavoidable, combining this with increased spectrographic resolution compounds technological challenges. To account for these noise sources and resolution issues, we use a temporal multifractal framework to study data from the SOAP 2.0 tool, which simulates a stellar spectrum in the presence of a spot, a facula or a planet. Given these controlled simulations, we vary the resolution as well as the signal-to-noise (S/N) ratio to obtain a lower limit on the resolution and S/N required to robustly detect features. We show that a spot and a facula with a 1% coverage of the stellar disk can be robustly detected for a S/N (per pixel) of 35 and 60 respectively, for any spectral resolution above 20,000, while a planet with a radial velocity (RV) of 10 m/s can be detected for a S/N (per pixel) of 600. Rather than viewing noise as an impediment, our approach uses noise as a source of information.