CORINOS IV: Quantifying Baseline-Fitting Uncertainties in SO$_2$ Ice Measurements with JWST/MIRI

TL;DR

This work tackles the long-standing question of SO$_2$ ice as a sulfur reservoir by systematically quantifying how continuum baseline placement biases affect SO$_2$ abundance in JWST/MIRI spectra of four Class 0 protostars. It introduces a local, Monte Carlo–style baseline fitting approach in the 6.8–8.5 μm window and uses Leiden Ice Database laboratory spectra with Omnifit to decompose overlapping features, including CH$_4$ and various organics. The analysis yields robust CH$_4$ detections across all targets and places plausible bounds on SO$_2$ ice abundances: 0.3–1.2% for pure SO$_2$, 0.02–0.18% for CH$_3$OH:SO$_2$, and 0.2–0.9% for H$_2$O:SO$_2$, with 0.2–0.9% deemed the most realistic. SO$_2$ detections are claimed for Ser-emb 7, L483, and IRAS 15398-3359, while B335 remains too noisy for a definitive detection, underscoring how baseline treatment and data quality shape solid-state sulfur inferences. The results inform the sulfur budget in protostellar environments and demonstrate a robust methodology for extracting weak ice features from crowded mid-IR spectra, contributing to the broader missing sulfur problem in star and planet formation contexts.

Abstract

Sulfur dioxide (SO$_2$) ice has been tentatively detected in protostellar envelopes, but its reliability as a solid-state sulfur reservoir remains unclear. We present new measurements of SO$_2$ ice from 6.8-8.5 $μ$m toward four Class 0 protostars observed with JWST's Mid-Infrared (MIRI) Medium Resolution Spectrometer, as part of the COMs ORigin Investigated by the Next-generation Observatory in Space (CORINOS) program. The sample spans a luminosity range from 1 $L_\odot$ (B335, IRAS 15398-3359) to 10 $L_\odot$ (L483, Ser-emb~7). To assess continuum placement uncertainty in absorption spectra, we apply randomized polynomial fits over the restricted region. We fit laboratory spectra from the Leiden Ice Database for Astrochemistry (LIDA) using the open-source Python library Omnifit. We detect the 7.7 $μ$m CH$_4$ band in all sources and find its column density robust to baseline choice, providing a reference for evaluating the weaker SO$_2$ feature on its blue shoulder and quantifying baseline-related uncertainty. Three SO$_2$ laboratory ices were tested: pure SO$_2$ ice yields 0.3-1.2% of volatile sulfur may be locked in SO$_2$ ice (lower and upper limits); CH$_3$OH:SO$_2$ ice gives 0.02-0.18%, but with lower quality fitting. The best-fitting H$_2$O:SO$_2$ ice yields 0.2-0.9%, which we consider the most realistic. These ranges define plausible bounds on SO$_2$ ice abundances in our sample. We find evidence for SO$_2$ in Ser-emb 7, L483, and IRAS 15398-3359, but emphasize the noisy spectrum of B335 prevents a definitive detection. Comparing SO$_2$ ice abundances across the different environments, we assess how conditions influence role of SO$_2$ as a potential sulfur reservoir and implications for the longstanding ``missing sulfur'' problem.

Figures (4)

• Figure 1: The flux density for all four sources in the CORINOS program. The grey highlighted region between 6.8-8.5 µm shows the blended COM region that this paper will focus. These are all 1D spectra extracted with a four-beam aperture with scaling for three of the sources (IRAS 15398-3359, Ser-emb 7, and L483). The sharp peaks in the spectra are from gas-phase species. Note the faintest source, B335, has no subband to subband scaling, and the spectrum is shown as extracted from individual MRS subbands.
• Figure 2: Example of baseline fits for each of the four CORINOS sources. The top plot with the vertical grey bars show the areas of minimal local ice absorption. This choice assumes that large absorption features (e.g., due to water or silicates) smoothly vary over our wavelength range that is fit by our polynomial. For each protostar one example set of guiding points is shown as blue markers, with the corresponding polynomal fit as a purple line. The resulting local optical depth spectra is shown below each panel. This process is repeated fifty times to generate different baseline subtracted optical depth spectra.
• Figure 3: The colored line in each panel is the "best-fit" total Omnifit result out of the 50 baseline iterations using Reduced $\chi^2$ for the pure SO$_2$ ice. The grey lines represent all other total fit lines. On the left panel, we show IRAS 15398-3359 as an example of a source with little variation in the fitting routine compared to B335 in the right side panel, whose fitting varies substantially with baseline choice.
• Figure 4: Example of fitting a spline to the local optical depth spectrum of IRAS 15398-3359, with the residuals used to derive the per-point noise level $\sigma_i$ for Omnifit. Right: The same procedure is applied to B335, which yields a noticeably higher noise level.