A Comprehensive Reanalysis of K2-18 b's JWST NIRISS+NIRSpec Transmission Spectrum

Authors: Stephen P. Schmidt, Ryan J. MacDonald, Shang-Min Tsai, Michael Radica, Le-Chris Wang, Eva-Maria Ahrer, Taylor J. Bell, Chloe Fisher, Daniel P. Thorngren, Nicholas Wogan, Erin M. May, Piero Ferrari, Katherine A. Bennett, Zafar Rustamkulov, Mercedes López-Morales, David K. Sing

arXiv: 2501.18477v1 - DOI (astro-ph.EP)
42 pages, 20 figures. Submitted to AAS Journals

Abstract: Sub-Neptunes are the most common type of planet in our galaxy. Interior structure models suggest that the coldest sub-Neptunes could host liquid water oceans underneath their hydrogen envelopes - sometimes called 'hycean' planets. JWST transmission spectra of the $\sim$ 250 K sub-Neptune K2-18 b were recently used to report detections of CH$_4$ and CO$_2$, alongside weaker evidence of (CH$_3$)$_2$S (dimethyl sulfide, or DMS). Atmospheric CO$_2$ was interpreted as evidence for a liquid water ocean, while DMS was highlighted as a potential biomarker. However, these notable claims were derived using a single data reduction and retrieval modeling framework, which did not allow for standard robustness tests. Here we present a comprehensive reanalysis of K2-18 b's JWST NIRISS SOSS and NIRSpec G395H transmission spectra, including the first analysis of the second-order NIRISS SOSS data. We incorporate multiple well-tested data reduction pipelines and retrieval codes, spanning 60 different data treatments and over 250 atmospheric retrievals. We confirm the detection of CH$_4$ ($\approx$ 4$\sigma$), with a volume mixing ratio of log CH$_4$ = $-1.15^{+0.40}_{-0.52}$, but we find no statistically significant or reliable evidence for CO$_2$ or DMS. Finally, we quantify the observed atmospheric composition using photochemical-climate and interior models, demonstrating that our revised composition of K2-18 b can be explained by an oxygen-poor mini-Neptune without requiring a liquid water surface or life.

Submitted to arXiv on 30 Jan. 2025

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.