Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 99 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 40 tok/s
GPT-5 High 38 tok/s Pro
GPT-4o 101 tok/s
GPT OSS 120B 470 tok/s Pro
Kimi K2 161 tok/s Pro
2000 character limit reached

Genesis-Metallicity: Universal Non-Parametric Gas-Phase Metallicity Estimation (2409.07455v2)

Published 11 Sep 2024 in astro-ph.GA

Abstract: We introduce genesis-metallicity, a gas-phase metallicity measurement python software employing the direct and strong-line methods depending on the available oxygen lines. The non-parametric strong-line estimator is calibrated based on a kernel density estimate in the 4-dimensional space of O2 = [O II]$\lambda\lambda 3727,29$/H$\beta$; O3 = [O III]$\lambda 5007$/H$\beta$; H$\beta$ equivalent width EW(H$\beta$); and gas-phase metallicity $12 + \log$(O/H). We use a calibration sample of 1510 galaxies at $0 < z < 10$ with direct-method metallicity measurements, compiled from the JWST/NIRSpec and ground-based observations. In particular, we report 122 new NIRSpec direct-method metallicity measurements at $z > 1$. We show that the O2, O3, and EW(H$\beta$) measurements are sufficient for a gas-phase metallicity estimate that is more accurate than 0.09 dex. Our calibration is universal, meaning that its accuracy does not depend on the target redshift. Furthermore, the direct-method module employs a non-parametric ${\rm T}{\rm e}$(O II) electron temperature estimator based on a kernel density estimate in the 5-dimensional space of O2, O3, EW(H$\beta$), ${\rm T}{\rm e}$(O III), and ${\rm T}{\rm e}$(O II). This ${\rm T}{\rm e}$(O II) estimator is calibrated based on 1004 spectra with detections of both [O III]$\lambda 4363$ and [O II]$\lambda\lambda 7320,30$, notably reporting 20 new NIRSpec detections of the [O II]$\lambda\lambda 7320,30$ doublet. We make genesis-metallicity and its calibration data publicly available and commit to keeping both up-to-date in light of the incoming data.

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

Collections

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

Summary

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

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

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