Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 12 tok/s Pro
GPT-5 High 10 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 139 tok/s Pro
GPT OSS 120B 433 tok/s Pro
Claude Sonnet 4 31 tok/s Pro
2000 character limit reached

X-Shooting ULLYSES: Massive stars at low metallicity. IV. Spectral analysis methods and exemplary results for O stars (2407.03137v2)

Published 3 Jul 2024 in astro-ph.SR, astro-ph.GA, and astro-ph.IM

Abstract: CONTEXT: The spectral analysis of hot, massive stars is a fundamental astrophysical method to obtain their intrinsic properties and their feedback. Quantitative spectroscopy for hot, massive stars requires detailed numerical modeling of the atmosphere and an iterative treatment to obtain the best solution within a given framework. AIMS: We present an overview of different techniques for the quantitative spectroscopy of hot stars employed within the X-Shooting ULLYSES collaboration, from grid-based approaches to tailored fits. By performing a blind test, we gain an overview about the similarities and differences of the resulting parameters. Our study aims to provide an overview of the parameter spread caused by different approaches. METHODS: For three different stars from the sample (SMC O5 star AzV 377, LMC O7 star Sk -69 50, and LMC O9 star Sk -66 171), we employ different atmosphere codes (CMFGEN, Fastwind, PoWR) and strategies to determine their best-fitting model. For our analyses, UV and optical spectra are used to derive the properties with some methods relying purely on optical data for comparison. To determine the overall spectral energy distribution, we further employ additional photometry from the literature. RESULTS: Effective temperatures for each of three sample stars agree within 3 kK while the differences in log g can be up to 0.2 dex. Luminosity differences of up to 0.1 dex result from different reddening assumptions, which seem to be larger for the methods employing a genetic algorithm. All sample stars are nitrogen-enriched. CONCLUSIONS: We find a reasonable agreement between the different methods. Tailored fitting tends to be able to minimize discrepancies obtained with more course or automatized treatments. UV spectral data is essential for the determination of realistic wind parameters. For one target (Sk -69 50), we find clear indications of an evolved status.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 2 posts and received 0 likes.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube