# A JWST/MIRI View of the Interstellar Medium in M83. I. Resolved Molecular Hydrogen Properties, Star Formation, and Feedback

Logan Jones, Svea Hernandez, Linda Smith, Aditya Togi, Tanio Diaz-Santos, Alessandra Aloisi, William Blair, Alec Hirschauer, Leslie Hunt, Bethan James, et al.

► To cite this version:

Logan Jones, Svea Hernandez, Linda Smith, Aditya Togi, Tanio Diaz-Santos, et al. A JWST/MIRI View of the Interstellar Medium in M83. I. Resolved Molecular Hydrogen Properties, Star Formation, and Feedback. The Astrophysical Journal, 2025, 987 (2), pp.142. 10.3847/1538-4357/adce71. hal-05360290

HAL Id: hal-05360290

https://hal.science/hal-05360290v1

Submitted on 13 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
OPEN ACCESS

<figure><img src="image_2.png" /></figure>

CrossMark

# A JWST/MIRI View of the Interstellar Medium in M83. I. Resolved Molecular Hydrogen Properties, Star Formation, and Feedback

Logan H. Jones<fnref n="1" />, Svea Hernandez<fnref n="2" />, Linda J. Smith<fnref n="1" />, Aditya Togi<fnref n="3" />, Tanio Diaz-Santos<fnref n="4" />,<fnref n="5" />, Alessandra Aloisi<fnref n="1" />, William Blair<fnref n="6" />, Alec S. Hirschauer<fnref n="1" />, Leslie K. Hunt<fnref n="7" />, Bethan L. James<fnref n="2" />, Nimisha Kumari<fnref n="2" />, Vianney Lebouteiller<fnref n="8" />, Matilde Mingozzi<fnref n="1" />, and Lise Ramambason<fnref n="9" />

Received 2024 October 13; revised 2025 March 21; accepted 2025 April 16; published 2025 July 3

## Abstract

We present a spatially resolved (~3 pc pixel⁻<fnref n="1" />) analysis of the distribution, kinematics, and excitation of warm H₂ gas in the nuclear starburst region of M83. Our JWST/MIRI integral field unit spectroscopy reveals a clumpy reservoir of warm H₂ (>200 K) with a mass of ~2.3 × 10⁵ M☉ in the area covered by all four Medium Resolution Spectrometer (MRS) channels. We additionally use the [Ne II] 12.8 µm and [Ne III] 15.5 µm lines as tracers of the star formation rate (SFR), ionizing radiation hardness, and kinematics of the ionized interstellar medium (ISM), finding tantalizing connections to the H₂ properties and stellar populations (e.g., broad and blueshifted [Ne II] emission coincident with a region with >5 Myr old stellar populations, low SFR, and low H₂ surface density.) Finally, qualitative comparisons to the trove of public, high-spatial-resolution multiwavelength data available on M83 shows that our MRS spectroscopy potentially traces all stages of the process of creating massive star clusters, from the embedded protocluster phase through the dispersion of ISM from stellar feedback.

*Unified Astronomy Thesaurus concepts: Galaxy evolution (594); Galaxy chemical evolution (580); Molecular gas (1073); Infrared spectroscopy (2285)*

## 1. Introduction

Since ongoing or imminent star formation (SF) primarily takes place in molecular clouds, scaling relations have been in use for decades (e.g., R. C. J. Kennicutt 1998; F. Bigiel et al. 2008; M. Béthermin et al. 2023) to tie the star formation rate (SFR) to the mass of cold H₂ gas in galaxies on both integrated and resolved scales. Once massive stars form and evolve, that same H₂ gas is heated through a combination of far-ultraviolet (FUV) pumping and shocks from winds or supernovae, leading to strong emission from transitions in the mid-infrared (MIR; e.g., P. N. Appleton et al. 2006; F. R. Donnan et al. 2023a). In other words, precise measurements of the mass and excitation state of H₂ in galaxies—especially at small scales relevant to cloud evolution and SF—are directly linked to both past and future generations of galaxy growth. However, because H₂ molecules lack a permanent dipole moment, H₂ radiates weakly at the ≲100 K temperatures associated with the star-forming ISM.

Central bursts of SF in massive galaxies are excellent laboratories for understanding the interwoven connections between H₂ gas, SF, and feedback episodes and their impact on the evolution of galaxies. The Infrared Space Observatory and Spitzer Space Telescope eras of space-based infrared

astronomy revealed a rich suite of spectroscopic features to diagnose the physical state of the interstellar medium (ISM) in SF galaxies—for example, emission from polycyclic aromatic hydrocarbon (PAH) grains (A. Maragkoudakis et al. 2018), or line emission from shocked and/or photoionized atomic and molecular gas (H. Inami et al. 2013). With an order of magnitude improvement in sensitivity and in spatial and spectral resolution, the advent of the Medium Resolution Spectrometer (MRS) on JWST/MIRI has further broadened the diagnostic space for understanding SF and feedback at previously unattainable scales (J. Young et al. 2023; A. Bik et al. 2024; D. Rigopoulou et al. 2024).

As the nearest face-on spiral galaxy with a nuclear starburst (D = 4.6 Mpc, A. Saha et al. 2006), M83 has been studied extensively across the electromagnetic spectrum, including the X-ray (e.g., Q. Hunt et al. 2021; Q. D. Wang et al. 2021), UV (e.g., D. A. Thilker et al. 2005; B. L. James et al. 2014; S. Hernandez et al. 2021), optical and near-IR (e.g., D. Mast et al. 2006; J. H. Knapen et al. 2010; P. F. Winkler et al. 2023), and millimeter to radio (e.g., T. D. Russell et al. 2020; J. Koda et al. 2023) regimes. Additionally, the nucleus and part of the disk of M83 was recently observed with wide- and medium-band filters from JWST/NIRCam and JWST/MIRI as part of the FEAST program (GO 1783, PI: A. Adamo; A. Adamo et al. 2025, in preparation), which provide unprecedented views of the bulk stellar populations, sites of embedded SF, and diffuse emission from PAHs. Finally, M83 contains numerous young massive clusters (YMCs) with archival FUV spectroscopy from the Hubble Space Telescope (HST) Cosmic

<figure><img src="image_3.png" /></figure>

Original content from this work may be used under the terms of the [Creative Commons Attribution 4.0 licence](https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

<footnote n="1">¹Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA; lojones@stsci.edu</footnote>

<footnote n="2">²AURA for ESA, Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA</footnote>

<footnote n="3">³Department of Physics, Texas State University, 601 University Drive, San Marcos, TX 78666, USA</footnote>

<footnote n="4">⁴Institute of Astrophysics, Foundation for Research and Technology-Hellas (FORTH), Heraklion, 70013, Greece</footnote>

<footnote n="5">⁵School of Sciences, European University Cyprus, Diogenes Street, Engomi, 1516 Nicosia, Cyprus</footnote>

<footnote n="6">⁶The William H. Miller III Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA</footnote>

<footnote n="7">⁷INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, I-50125 Firenze, Italy</footnote>

<footnote n="8">⁸AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, F-91191 Gif-sur-Yvette, France</footnote>

<footnote n="9">⁹Institut fur Theoretische Astrophysik, Zentrum für Astronomie, Universität Heidelberg, Albert-Ueberle-Str. 2, D-69120 Heidelberg, Germany</footnote>
Original resolution, 5.56 µm

<figure><img src="image_4.png" /></figure>

Smoothed resolution, 5.56 µm

<figure><img src="image_5.png" /></figure>

Original resolution, 20.68 µm

<figure><img src="image_6.png" /></figure>

**Figure 1.** An illustration of the Gaussian convolution process we use to smooth our IFU observations of M83's nuclear region to a common spatial resolution. Left: a continuum-only wavelength slice at 5.56 µm at its native resolution (point-spread function (PSF) FWHM of 0.29), with the four regions labeled. Center: the same Channel 1 wavelength slice, now convolved with a Gaussian to a PSF FWHM of 0.788. Right: the reddest wavelength slice in our truncated IFU cube (20.68 µm) at its native resolution (PSF FWHM of 0.788). The rightmost image is displayed in log scale to better show faint structures. The red circles in the lower-left corner of each panel show the size of the PSF FWHM at the given native/smoothed resolution.

Origins Spectrograph (COS; pIDs 11579, 14681, and 15193). The COS spectra provide direct constraints on the intensity and hardness of the ambient ionizing radiation field and of the amount of mechanical feedback from stellar winds (via, e.g., C IV $\lambda\lambda$1549, 1551), each of which acts to regulate the excitation of nearby molecular clouds.

In S. Hernandez et al. (2023, hereafter H23), we reported top-level results from JWST-GO program 02219 (PI: S. Hernandez), in which we investigated the spatially integrated spectral properties of our four MRS pointings covering the central ~200 × 200 pc² of M83’s nuclear region, including the optical nucleus. These spectra provided insights into the ISM conditions of M83’s center at a scale of ~100 pc, as well as how they vary from region to region. In this work, we return to the same data set to inspect the properties of the warm H₂ gas at the level of individual spaxels (0.13 or ~3 pc). The high spatial resolution and sensitivity of these data allows us to search for quantitative and qualitative changes in the gas properties of the molecular and ionized gas with respect to the location of the optical nucleus and (un)obscured YMCs.

We structure this paper as follows. In Section 2, we describe the observations and data reduction steps. In Section 3, we outline the spectral-fitting tools and procedures we use to fit our integral field unit (IFU) data on a spaxel-by-spaxel basis, followed by a summary in Section 4 of our methods for determining H₂ excitation, SFR, and other physical properties. We put our findings for the warm H₂ content into a broader multiwavelength context in Section 5, including a qualitative discussion of the different stages of stellar feedback and ISM clearing that M83 displays in its nuclear region.

## 2. Observations

We use the MIRI/MRS observations from program GO 02219 (PI: S. Hernandez), which were obtained between 2022 July 5–9. The raw MIRI/MRS files were retrieved from the Mikulski Archive for Space Telescopes; all JWST data used in this paper can be found doi:10.17909/a61h-f081. Additional details on the observation strategy are given in H23, though we summarize the most important points below.

The primary MRS observations consist of four pointings in a 2 × 2 mosaic, with a four point dither pattern applied to each pointing to achieve optimal sampling throughout the MRS field of view (FOV) and to mitigate detector artifacts. This results in contiguous spatial coverage across the mosaic in MRS Channels 3 and 4 but not Channels 1 and 2. The specific pointings (labeled as Regions 1, 2, 3, and 4 in the left panel of Figure 1 and in select figures thereafter) were strategically chosen to provide full spatial coverage of the YMCs in all four MRS channels, with the FOV per pointing increasing from 3.2 × 3.7 in Channel 1 (4.9–7.65 µm) to 6.6 × 7.7 in Channel 4 (17.7–27.9 µm). The observations were obtained over three visits, with one visit each dedicated to observing with the SHORT (A), MEDIUM (B), and LONG (C) gratings. The total integration time per grating was 9056 s, split evenly across the four pointings.

Due to the extended nature of M83’s nuclear region, we required a dedicated sky observation to accurately measure and correct for the thermal background. We thus also obtained a background pointing with MRS along a sight line well off the disk of M83, with integration times equal to those of the primary pointings. All MRS observations were reduced with the standard JWST pipeline (version 1.10.2), with the spatial pixels gridded to a common scale of 0.13 pixel⁻¹ and the output type set to *multi* in the Spec3 pipeline to automate the spectral stitching between bands/channels. We also applied residual fringe corrections available in the JWST pipeline to both the 3D and 1D observations, though as we discuss below, significant fringing is present at the longest wavelengths even with these corrections.

In the course of reducing and analyzing these data, we also noted significant discontinuities in the continua of some individual spaxel spectra. These discontinuities are most prominent in the Channel 4 data and occur approximately at the wavelengths where the spectral coverage begins or ends for the 4A, 4B, and 4C bands, which we attribute to issues with flux calibration across bands within the MRS pipeline. These artificial breaks in the continuum flux are often severe enough to affect the quality of our fits to the warm dust continuum at $\lambda_0 \gtrsim 20$ µm (see Section 3), which in turn complicated our
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Example best fits (top) and residuals (bottom) from CAFE for two spaxel spectra in our MRS cube, which illustrate the diversity of dust conditions across our FOV. Model PAH features and continuum emission from cool and/or warm dust (~60 and 200 K) are plotted as marked in the legend. The dashed gray curve shows the attenuation fraction as a function of wavelength.</figcaption></figure>

emission-line extraction and fitting. We opted to truncate the
wavelength axis of our combined data cube at 20.68 µm
(the blue limit of band 4B) to mitigate the impact of the
discontinuities on our full spectral fitting. We note that smaller
discontinuities also appear in the overlap regions between
Channels 2 and 3 for some spaxels, though the magnitude and
direction of the jump was quite variable if present at all. Since
these artifacts are generally comparable in strength to the
residual fringes at ~11.5 µm, we did not find it useful to
attempt correcting these breaks.

For this work, we also restrict ourselves only to the parts of
our IFU data cube covered by MRS Channel 1 (the smallest
FOV of the four channels) to ensure contiguous spectral
coverage down to ~5 µm. Finally, we spatially smoothed our
data cube to a common resolution of 0.788 FWHM (~18 pc at
D = 4.6 Mpc), which is the PSF FWHM of MRS at our long-
wavelength cutoff of 20.68 µm (see Equation (1) of D. R. Law
et al. 2023). For each wavelength slice in the IFU cube, we
determine the quadrature difference between the PSF FWHM
at 20.68 µm and the nominal PSF FWHM at that wavelength
(again according to Equation (1) of D. R. Law et al. 2023), and
then convolve the slice with a 2D Gaussian with the
corresponding width, $\sigma = \sqrt{\sigma_{20.68}^2 - \sigma_{\lambda}^2}$. We show an example
of this process in Figure 1.

**3. Methods**

3.1. Polycyclic Aromatic Hydrocarbons, Dust Continuum, and Attenuation

In recent years, numerous spectral-energy-distribution-
fitting and spectral-fitting tools have been developed (or
radically upgraded from the pre-JWST era) to model PAH
emission, reprocessed dust emission, and other features
unique to the NIR and MIR regimes. These include
PAHFIT (J. D. T. Smith et al. 2007); the multidust-
component models of F. R. Donnan et al. (2023b, 2024);
and the Continuum and Feature Extraction (CAFE) software

(J. A. Marshall et al. 2007; T. Diaz-Santos et al. 2025).<fnref n="10" />
We opt to use CAFE to model PAH features, dust continuum
emission, and extinction in our MRS spectra. We began by
assuming a systemic redshift of z = 0.001733 (M. J. Meyer
et al. 2004) to shift our MRS cube into the approximate rest
frame for fitting with CAFE. We used a modified version of the
MIRI-optimized starburst parameter file in CAFE, which
models the MIR continuum as the sum of reprocessed dust
emission and “starburst” continuum components (see
Figure 2). The stellar/starburst model consists of a mixture
of 2 and 10 Myr old single stellar population (SSP) templates
from Starburst99 (C. Leitherer et al. 1999). However, we note
that only ~half of the spaxels (mostly concentrated near the
optical nucleus and generally dust-free areas in Regions 3 and
4) ultimately include a nonzero stellar component in their best-
fit CAFE model, with the 10 Myr component dominant.

Our primary dust model includes emission and absorption
from cool (40–100 K) and warm (100–400 K) dust and
assumes a screen geometry. However, we found that the two-
component dust model produced physically implausible fits
(i.e., optical depths $\gtrsim$ 10 at 14 µm) for some highly obscured
areas in Region 2 whose continuum spectra were dominated by
emission from cool dust. For these ~70 spaxels, we instead
fitted the spectra with a single dust component with
temperatures between 40–100 K. Finally, we assume a
Galactic interstellar radiation field as the illuminating source
for all model dust components.

Individual PAH features (drawn from a feature library
included in CAFE) were fitted with Drude profiles. The rest-
frame central wavelengths and profile widths for each feature
were held fixed for computational efficiency, although we
allowed for a velocity shift of up to ±100 km s⁻¹ in each
spaxel, which is applied to all PAH features uniformly. We did
not fit emission lines simultaneously with PAHs and MIR
continua also for computational efficiency, as this would have

<footnote n="10">¹⁰ https://github.com/GOALS-survey/CAFE</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure 3.** Spatial distribution of best-fit values of $\tau_{\text{warm}}$ from CAFE, which indicates the amount of attenuation applied to PAHs and emission lines in the CAFE framework. The plus symbol, open circle, and open diamond respectively denote the position of the optical nucleus, an unidentified H$_2$ point source, and a partly embedded YMC, and the locations of the single-pixel spectra and CAFE fits shown in Figure 2 are marked with black squares.</figcaption></figure>

nearly tripled the number of fittable model parameters for a single spaxel, and instead perform our own line fitting after subtracting the best-fit dust + PAH model as described in the next section. Finally, we assumed the V. Ossenkopf et al. (1992) extinction curve, which includes strong silicate absorption features at 9.7 and 18 µm.

We show representative spaxel spectra and CAFE fits in Figure 2, which illustrate the diversity of dust properties in our data. Both spectra have a signal-to-noise ratio (S/N) > 100 at 10 µm, somewhat higher than the median S/N (~78) across the Channel 1 FOV at 10 µm. The left panel shows a spectrum toward the mildly obscured optical nucleus ((α, δ) = (13:17:0.887, -29:51:55.921) as measured from our MRS data), while the right panel shows a spectrum toward a UV-bright star cluster with partially cleared foreground ISM. CAFE produces acceptable model fits across the wavelength range we consider in this work (with residuals of ±5% in line-free regions), with the exception of the highly structured PAH complexes at 6–8 and 11–13 µm.

Our use of fixed Drude profile centroids and widths is likely responsible for the poor fit in these intervals—see, e.g., the 12.7 µm feature, for which the actual peak wavelength is redder (by ~0.08 µm) than that assumed in CAFE’s feature library. The fitting algorithm also sometimes fails to identify weak features in the broad wings of more dominant PAH features (e.g., the small feature at 11.05 µm adjacent to the prominent PAH peak at 11.2 µm). These difficulties with PAH feature fits should nonetheless have at most a small effect on the inferred dust continuum properties, including attenuation of emission lines. We note that the fractional attenuation (dotted gray curve) experienced by PAH features and emission lines can be significant, even in the MIR. For the nuclear spaxel spectrum, for example, roughly 10% of the light at 15 µm is attenuated; this increases to 30% at 10 µm, driven by the deep silicate absorption feature at 9.7 µm.

In Figure 3, we show the spatial distribution of the optical depth due to warm dust at 14 µm, $\tau_{\text{warm}}$, as inferred by CAFE across the FOV of our MRS cube. Although the median optical depth across our FOV is fairly low ($\tau_{\text{warm}} \sim 0.64$), we note

<table><caption>**Table 1**
List of H$_2$ Rotational Transitions</caption><thead><tr><th>Transition<br>(ν = 0)</th><th>Name</th><th>λ<sub>0</sub><br>(μm)</th><th>E<sub>ν</sub>/k<br>(K)</th></tr></thead><tbody><tr><td>J = 9 → 7</td><td>S(7)</td><td>5.5115</td><td>7197</td></tr><tr><td>J = 8 → 6</td><td>S(6)</td><td>6.1088</td><td>4830</td></tr><tr><td>J = 7 → 5</td><td>S(5)</td><td>6.9095</td><td>4586</td></tr><tr><td>J = 6 → 4</td><td>S(4)</td><td>8.0250</td><td>3475</td></tr><tr><td>J = 5 → 3</td><td>S(3)</td><td>9.6649</td><td>2504</td></tr><tr><td>J = 4 → 2</td><td>S(2)</td><td>12.2786</td><td>1682</td></tr><tr><td>J = 3 → 1</td><td>S(1)</td><td>17.0348</td><td>1015</td></tr></tbody></table>

regions where the dust is optically thick ($\tau_{\text{warm}} \gtrsim 1$), particularly in Regions 1 and 2. Because the amount of attenuation experienced by PAHs and emission lines in CAFE is tied to the optical depth of warm dust, this means that all line fluxes in these regions will be boosted by up to ~40%.

## 3.2. Emission-line Maps

After obtaining reliable PAH + continuum models and attenuation estimates across the FOV of our combined cube using the fitting procedure outlined above, we turn to the fitting of emission lines. We perform a local continuum fitting and subtraction in the vicinity of the lines analyzed in this work. We then use *specutils* to fit a single Gaussian to each of the H$_2$ transitions listed in Table 1. We also perform single and double-Gaussian fits to the [Ne II] 12.81 µm and [Ne III] 15.56 µm lines (see Section 4.3, which we use as an SFR diagnostic and approximate tracer of the hardness of the ambient UV field). All fits were allowed to vary independently (i.e., the kinematics of the H$_2$ lines are not tied). We integrate over the best-fit Gaussian profile to obtain total line or line component fluxes in units of erg s$^{-1}$ cm$^{-2}$.

We note that a small but significant number of spaxels (particularly those near the edge of the Channel 1 FOV) have no data in certain narrow wavelength intervals, most of which lie below 8 µm; we attribute this to a combination of the dither pattern and the spectral stitching routines in the MRS pipeline, which result in edge spaxels having sporadic wavelength coverage when combining our data into a single cube. Unfortunately, these missing wavelength intervals frequently overlap with the expected positions of the S(5)–S(8) transitions of H$_2$, which limits the number of transitions that go into our H$_2$ excitation modeling near the edge of the Channel 1 FOV.

We estimate the significance of a line detection by estimating the noise in a nearby, line-free region of the continuum-subtracted spectra, in a manner similar to that used in B. Rodríguez Del Pino et al. (2023). Specifically, we measure the rms flux density in 0.03 µm wide windows in the pseudocontinuum around each line, then multiply this value by $2 \times$ the FWHM of the line as an estimate of the amount of “signal” that would be present in the absence of any line emission. Unless otherwise specified, we adopt a threshold of S/N > 3 for all lines and line components, in line with other emission-line studies (e.g., M. Mingozzi et al. 2022).

Finally, we correct all line fluxes and uncertainty estimates using the wavelength-dependent attenuation fraction inferred by CAFE for each spaxel, shown as a dotted gray line in Figure 2. In other words, at the rest-frame wavelength of each line, we extract the value A of the gray curve (V. Ossenkopf et al. 1992) and multiply the observed flux and flux uncertainty
<figure><img src="image_9.png" /><figcaption>**Figure 4.** Maps of attenuation-corrected line flux (left), velocity offsets from systemic (center), and line FWHM (right) for odd-J pure rotational transitions of H₂ in our MRS cube. All lines are required to have S/N > 3 to be included in the maps and our H₂ excitation fitting. Cyan circles (0.6 diameter) mark the position of UV-bright star clusters from J. Harris et al. (2001). The plus symbol, circle, and downward triangle respectively denote the position of the optical nucleus, an unidentified H₂ point source (see Section 5.3), and an unidentified point source showing [Ne v] 14.32 µm and [Ne VI] 7.65 µm emission (see Section 5.5).</figcaption></figure>

by (1/A) to recover the intrinsic, de-reddened line flux without artificially inflating its S/N.

## 4. Analysis

We show maps of the attenuation-corrected flux, gas velocity, and line FWHM for the S(1), S(3), S(5), and S(7)

rotational H₂ lines in Figure 4. Velocity maps are shown with respect to the systemic redshift, $z = 0.001733$, corresponding to a recession velocity of 519 km s⁻¹ (M. J. Meyer et al. 2004). Line FWHMs have been corrected for instrumental broadening, using Equation (1) of O. C. Jones et al. (2023b) to determine the spectral resolution at the wavelength of each
line except for S(1). Their Equation (1) underestimates the observed resolving power of MRS at ~15–17 µm (see their Figure 5 and the JWST user documentation),<fnref n="11" /> leading us to adopt $R = 3300$ for the S(1) transition and other lines in this wavelength interval.

The flux and kinematic maps of these transitions show generally similar structures, with prominent H₂ line emission from the optical nucleus (cross symbol in Figure 4 and thereafter), highly extincted knots in Region 2, and other clumps centered around a string of UV-bright YMCs in Region 4. However, we note that higher-excitation lines are stronger (relative to S(1)) in the optical nucleus and weak or undetected in much of Regions 3 and 4. The lines are generally resolved (corrected FWHM ~30 km s⁻¹) across much of our FOV, with some filamentary or clumpy structures reaching up to FWHM ~140 km s⁻¹. We note that the portions of Region 3 which are undetected in higher-excitation lines are coincident with one such patch of highly dispersed gas, as observed in the lower-excitation lines. We also identify a kinematically distinct point source in Region 2, the position of which is marked with a circle in the first and final rows of Figure 4. We further discuss the possible nature of this object in Section 5.3, though we note that it is only detected robustly in the flux and kinematics of S(1) and weakly in S(2). No such structure is seen at this position in [Ne II], [Ne III], or other H₂ lines.

### 4.1. H₂ Excitation Modeling

To obtain an estimate of the warm H₂ mass per spaxel, we use the H₂ excitation model of A. Togi & J. D. T. Smith (2016, hereafter TS16). In brief, the model assumes that the rotational temperatures of H₂ gas follow a power-law distribution $dN = mT^{-n}dT$ for column density $N$, temperature $T$, and a constant $m$. Given the analytical expressions in TS16 for $m$ and other relevant quantities (e.g., column density of H₂ molecules in the upper energy level $jN_j$), the model parameters that must be constrained by the data are the slope $n$ and the upper and lower bounds of the temperature distribution $T_u$ and $T_l$. TS16 find that the model fit quality and inferred H₂ mass become insensitive to the choice of $T_u$ at temperatures above 2000 K and thus assume a fixed $T_u = 2000$ K, which we also adopt in this work. $n$ and $T_l$ can then be constrained directly from the data by minimizing the difference between the observed values of $N_j$ (which are tied to the line fluxes) and the predicted values of $N_j$ for some $n$ and $T_l$. Both the observed and predicted columns are normalized to the S(1) line for the fitting procedure.

For this work we created a Python adaptation of the TS16 model, which we release for community use.<fnref n="12" /> Our Python version yields best-fit values of $n$, $T_l$, and H₂ masses $M(T > T_l)$ that are identical to the original implementation in IDL to within <1%, which we confirm through tests using the TS16 sample. Parameter uncertainties on $n$ and $T_l$ are slightly higher in our Python implementation, which we attribute to the choice of minimization algorithm (the Levenberg-Marquardt algorithm in IDL versus the Trust Region Reflective algorithm in `scipy's curve_fit` function).

<figure><img src="image_10.png" /><figcaption>**Figure 5.** Spatial distribution of best-fit power-law slopes (top) and lower cutoff temperature $T_{l,fit}$ (bottom) for the continuous-temperature power-law model of TS16. Blue circles (0''/6 diameter) mark the position of UV-bright star clusters from J. Harris et al. (2001). The plus symbol, diamond, and black circle respectively denote the position of the optical nucleus, a partially embedded YMC, and an unidentified H₂ point source described in Section 4. Purple–pink contours trace $\Sigma_{SFR}$ using the M.-Y. Zhuang et al. (2019) diagnostic at intervals of [2, 5, 15, 30, 45, 55] $M_{\odot}~yr^{-1}~kpc^{-2}$ (see Section 4.3.)</figcaption></figure>

We show the map of best-fit slopes $n$ in Figure 5. For the remainder of our resolved H₂ analysis, we consider only those pixels with four or more H₂ line detections and with relative error $\delta n/n < 25\%$ to mitigate the impact of poor power-law fits on our total H₂ mass estimates. This mostly affects pixels along the FOV edges and the corners of Region 3, which are generally very faint or undetected in H₂ transitions beyond S(3), as seen in Figure 4. The median slope and $1\sigma$ range across our FOV, $n = 5.75 \pm 0.64$, reaches the upper end of the $3.79 < n < 6.39$ range reported in TS16 for their sample of SINGS galaxies and is somewhat steeper than (but consistent with) their sample mean of $n = 4.84 \pm 0.61$. This indicates, on average, relatively little H₂ in highly excited states across our four regions in M83, though with significant variation. We note quite shallow slopes (i.e., relatively high H₂ excitation) are clustered near the optical nucleus and the clumps or strings of UV-bright star clusters in Regions 2 and 4, while the steepest slopes are concentrated in Region 3. We also find that the best-fit values of $T_l$ across our FOV vary from ~130–343

<footnote n="11">¹¹ https://jwst-docs.stsci.edu/jwst-mid-infrared-instrument/miri-observing-modes/miri-medium-resolution-spectroscopy</footnote>

<footnote n="12">¹² https://h2powerlaw.readthedocs.io/en/latest/</footnote>
K (median of 247 K), with the lowest values of $T_l$ coincident with CO-bright knots in Region 2 (see the discussion in Section 5) and the highest found near the optical nucleus.

## 4.2. Warm H₂ Mass Estimates

With the power-law H₂ temperature distribution thus constrained, we can then use Equations (9) and (14) in TS16 to derive total H₂ column densities and masses per spaxel. However, while the slope of the temperature distribution in each spaxel is reasonably well constrained by the data, there remain several justifiable choices for the lower temperature bound $T_l'$ for the column integral. Since the bulk of the cold, imminently star-forming H₂ cannot be probed through rotational emission, it is natural to wonder if extrapolation of the power-law model to lower temperatures will produce physically sensible results. A proper choice for $T_l'$ is particularly critical for our data given the steep slopes we measure in some areas, as extrapolating to lower and lower values of $T_l'$ may cause the total inferred H₂ mass to balloon beyond what is inferred through CO and other tracers.

Using the best-fit power-law slopes per spaxel, we therefore construct three different maps of the H₂ mass distribution, each with a different definition of $T_l'$.

1. $T_l'$ is set equal to the best-fit $T_l$ for each spaxel. This approach provides an estimate of the warm H₂ that is the most directly constrained by the data.

2. $T_l'$ is fixed at 200 K across the FOV, which imposes a modest extrapolation of the temperature distribution for most spaxels but provides a more uniform estimate of the warm H₂ than (1).

3. $T_l'$ is fixed at 80 K across the FOV, which is a typical temperature below which the H₂ reservoir can change in mass without measurable changes to the rotational line fluxes in the Spitzer Infrared Spectrograph (IRS) spectroscopy analyzed in TS16.

Unless otherwise specified, we propagate only the uncertainty on *n* (and not $T_l$) to the gas mass estimates. Adopting the first two definitions of $T_l'$, the warm H₂ mass across our FOV ranges between (1) $1.50_{-0.09}^{+0.09} \times 10^5 M_\odot$ for a variable $T_l'$ and (2) $2.26_{-0.19}^{+0.17} \times 10^5 M_\odot$ for $T_l' = 200$ K. Adopting the third definition with $T_l' = 80$ K, which taps into the cooler gas, yields (3) $7.93_{-2.39}^{+3.52} \times 10^6 M_\odot$. In Figure 6, we show the mass per 0.13 spaxel inferred using definitions (2) and (3)—in other words, maps of the warm and cool + warm H₂ gas over our FOV.

We note that performing the same analysis described above on the observed line (with no attenuation corrections) can yield significantly different best-fit H₂ models and gas masses. In particular, the wavelength-dependent extinction has the greatest impact on the S(1) and S(3) lines, which are coincident with silicate absorption features at ~9.7 and 18 µm. Because we use the S(1) flux as the normalizing factor in the TS16 framework, not correcting for the relatively greater attenuation at 17 µm creates the appearance of relatively stronger higher-J lines, which then yields steeper power-law slopes, warmer best-fit $T_l$, and lower $M_{H2}(T > T_l)$, by a factor of $\ge 2$ overall. The silicate absorption feature at 9.7 µm can also significantly depress the observed flux of the S(3) line; we omitted this line entirely for three of our four regions in our top-level analysis presented in S. Hernandez et al. (2023), which did not incorporate attenuation corrections. This exercise illustrates

<figure><img src="image_11.png" /><figcaption>**Figure 6.** Mass per 0.13 spaxel of H₂ as estimated from the power-law model of TS16, integrated to a common $T_l' = 200$ K (top) and 80 K (bottom). Cyan circles (0.6 diameter) mark the position of UV-bright star clusters from J. Harris et al. (2001). Other symbols are as defined in Figure 5.</figcaption></figure>

that attenuation corrections to emission lines remain crucial even in the MIR.

We further note that this work is one of the first uses of the TS16 model on the $\lesssim 5$ pc scale, compared to the ~kiloparsec-scale regions probed by Spitzer/IRS in the sample analyzed in TS16. Even in the era of JWST, other recent works that follow TS16 to model H₂ masses and excitation from MRS spectroscopy only probe down to scales ~100 pc (e.g., T. S. Y. Lai et al. 2022; L. Armus et al. 2023; L. Zhang & L. C. Ho 2023; J. H. Costa-Souza et al. 2024). To assess the importance of physical scale on the derived mass estimates, we applied CAFE and our custom line-fitting procedure to the integrated-light spectrum (created through the coaddition of every spaxel in our common FOV, which approximates the total spectrum of the central $200 \times 200$ pc² of M83).

The observed and attenuation-corrected fluxes of the S(1)–S(7) transitions in the integrated spectrum are consistent within 5%–10% of the sum of the corresponding line fluxes from the spaxel-by-spaxel fits, with the greatest discrepancies occurring for the de-attenuated S(1) and S(3) fluxes. We attribute this to the modest attenuation ($\tau_{\text{warm}} = 0.49$) inferred by CAFE in our integrated spectrum, which is slightly lower than the median
<table><caption>**Table 2**
Model H₂ Masses for M83's Nuclear Region</caption><thead><tr><th>Analysis</th><th>T<sub>l</sub>'<br>(K)</th><th>M<sub>H2</sub>(T > T<sub>l</sub}')<br>(M<sub>☉</sub>)</th><th>F<sub>CO</sub><br>(%)</th></tr></thead><tbody><tr><td rowspan="4">Resolved</td><td>Model T<sub>l</sub></td><td>1.50<sup>+0.09</sup><sub>-0.09</sub> × 10<sup>5</sup></td><td>0.89<sup>+0.03</sup><sub>-0.07</sub></td></tr><tr><td>200</td><td>2.26<sup>+0.17</sup><sub>-0.19</sub> × 10<sup>5</sup></td><td>1.32<sup>+0.10</sup><sub>-0.11</sub></td></tr><tr><td>80</td><td>7.93<sup>+3.52</sup><sub>-2.59</sub> × 10<sup>6</sup></td><td>46.2<sup>+20.5</sup><sub>-15.1</sub></td></tr><tr><td>50</td><td>8.17<sup>+8.67</sup><sub>-4.04</sub> × 10<sup>7</sup></td><td>476<sup>+506</sup><sub>-235</sub></td></tr><tr><td rowspan="4">Integrated</td><td>Model T<sub>l</sub></td><td>1.54<sup>+0.06</sup><sub>-0.07</sub> × 10<sup>5</sup></td><td>0.90<sup>+0.04</sup><sub>-0.04</sub></td></tr><tr><td>200</td><td>1.84<sup>+0.09</sup><sub>-0.09</sub> × 10<sup>5</sup></td><td>1.07<sup>+0.05</sup><sub>-0.05</sub></td></tr><tr><td>80</td><td>4.47<sup>+0.98</sup><sub>-0.84</sub> × 10<sup>6</sup></td><td>26.1<sup>+5.72</sup><sub>-4.91</sub></td></tr><tr><td>50</td><td>3.05<sup>+1.14</sup><sub>-0.86</sub> × 10<sup>7</sup></td><td>178<sup>+66.7</sup><sub>-49.9</sub></td></tr></tbody></table>

**Note.** H₂ masses and uncertainties above a given temperature for both our resolved and integrated-light analyses. Column 4 (F<sub>CO</sub>) is the percentage of the CO-derived total molecular gas mass each measurement accounts for; we calculate M<sub>H2</sub>(CO) = 1.715 × 10<sup>7</sup> M<sub>☉</sub> assuming a Galactic α<sub>CO</sub> = 3.2 M<sub>☉</sub> (K km s<sup>-1</sup> pc<sup>2</sup>)<sup>-1</sup> and the CO(1–0) data of A. Hirota et al. (2018).

value of τ<sub>warm</sub> = 0.60 for our resolved analysis as shown in Figure 3.

Additionally, while the best-fit value of T<sub>l</sub> (215.2 ± 26.2 K) is roughly consistent with the median T<sub>l</sub> value from the spaxel-level analysis, the power-law slope n = 5.10 ± 0.26 is slightly lower (i.e., shallower) than the median slope in our resolved analysis, though still consistent within the uncertainties. Again adopting each of our three definitions of T<sub>l</sub>', the H₂ masses we infer from the integrated spectrum are (1) 1.54<sup>+0.06</sup><sub>-0.07</sub> × 10<sup>5</sup> M<sub>☉</sub> for T<sub>l</sub>' = 215.2 K; (2) 1.84<sup>+0.09</sup><sub>-0.09</sub> × 10<sup>5</sup> M<sub>☉</sub> for T<sub>l</sub>' = 200 K; and (3) 4.47<sup>+0.98</sup><sub>-0.84</sub> × 10<sup>6</sup> M<sub>☉</sub> for T<sub>l</sub>' = 80 K.

When adopting Definition (1), the warm H₂ mass estimates in our integrated and resolve-then-sum analyses are in excellent agreement with one another, as expected. However, in the temperature regimes considered under Definitions (2) and (3), the H₂ masses estimated from our integrated spectrum are at best only marginally consistent within the uncertainties with those from our resolved analysis. The systematically lower masses in the integrated case can be attributed to the slight differences in typical power-law slope, which can greatly affect the inferred amount of cool/cold gas as we extend to lower and lower values of T<sub>l</sub>'. Together with the different degrees of attenuation inferred for integrated and spaxel-level data, this hints at a scenario where the inferred H₂ mass and excitation depends on the physical scale probed, at least within the framework of the TS16 model. We summarize the various estimates of M<sub>H2</sub>(T > T<sub>l</sub}') and their uncertainties in Table 2.

For completeness, we note the existence of a Spitzer/IRS spectrum that partially overlaps with our FOV (covering some or all of our Regions 1 and 2) from which we could, in principle, extract a loose constraint on the flux of the S(0) line at 28 µm. This low-excitation line would constrain the low-temperature end of the excitation model, which in turn somewhat bypasses the need for multiple temperature cutoffs. However, we did not think this approach to be proper since the IRS aperture covers a somewhat different portion of M83's inner disk. The fact that we see <10 pc variations in the S(1)–S(7) line luminosities and H₂ excitation conditions also provides a post hoc argument against incorporating the Spitzer spectrum, for which the spectral aperture is nearly as large as the FOV for MRS Channel 1.

### 4.3. Star Formation Rates

As described in Section 3.2, we performed single and double-Gaussian fits to the [Ne II] 12.81 µm and [Ne III] 15.56 µm lines. To determine whether to use the single or two-component fits for a given line in each spectrum, we compare the reduced χ² of the two fits in a statistical F-test, following M. S. Westmoquette et al. (2007). In essence, we adopt the two-component fit if the reduced χ² is lowered by a factor of at least 3.289 compared to the one-component fit, corresponding to a significance level α = 0.10; otherwise, we default to the one-component fit. Only after application of the F-test do we then apply our detection threshold of S/N > 3, since our definition of noise depends on the fitted FWHM of the line or line component.

The bright [Ne II] line is better modeled with a two-component fit across nearly all of our FOV (see Figure 7), indicating a ubiquitous ISM component that is kinematically distinct from gas near H II regions. The secondary component contributes ~23% of the total [Ne II] line flux on average in our FOV. The need for a second component in [Ne III] is less uniformly evident, with our F-test favoring a single-Gaussian fit across large swathes of all four regions. However, we note that for [Ne III] spectra which do not “pass” the F-test, the FWHM of the single-Gaussian fits are slightly but systematically larger (by 20–60 km s<sup>-1</sup>) than the FWHM of the primary component in the corresponding double-Gaussian fit. This suggests that there may be a broad component that is strong enough to affect the wings of the [Ne III] line profile, but is unable to be detected by the F-test at a reasonable statistical significance. We show the results of both our single-Gaussian and double-Gaussian fitting of [Ne III] in the Appendix (Figure A1).

We additionally show the flux ratio of the primary/narrow components of [Ne III] to [Ne II] in the upper panel of Figure 8. We find that the neon emission is predominantly from the singly ionized state across our FOV with the exception of two compact regions in Region 1, where [Ne III] and [Ne II] are of roughly equal strength. One of these areas corresponds to the location of the optical nucleus, while the other has an unclear origin. Though shocks may be partly responsible for the elevated [Ne III] abundance, the nearest cataloged supernova remnant (SNR; P. F. Winkler et al. 2023, marked with gray circles in Figure 8) is 1''4 (33 pc projected) away.

Finally, we use our F-test and S/N-filtered line luminosity maps to derive SFRs per spaxel using the relation presented in M.-Y. Zhuang et al. (2019). Their relation is similar to that given in L. C. Ho & E. Keto (2007) but with an explicit dependence on metallicity, which affects the neon abundance and the shape of the incident ionizing spectrum. M.-Y. Zhuang et al. (2019) also provide prescriptions for determining the fraction of neon in the first and second ionization states, f<sub>+</sub> and f<sub>+2</sub>, directly from the [Ne III]/[Ne II] line ratio. We use the extinction-corrected line luminosities and an assumed gas-phase metallicity of Z = 2 × Z<sub>☉</sub> (S. Hernandez et al. 2021) to derive SFRs using the M.-Y. Zhuang et al. (2019) relation. Converting the spaxel size (0''13) to kiloparsecs at the distance of M83 (D = 4.6 Mpc A. Saha et al. 2006), we create a map of the SFR surface density Σ<sub>SFR</sub> in units of M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup>, shown in the lower panel of Figure 8. The SFR densities in Region 2 are fairly high, reaching Σ<sub>SFR</sub> ~ 75 M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup> near a grouping of very young (see Section 5.4) UV-bright star clusters, and rapidly decrease as the eye moves clockwise
<figure><img src="image_12.png" /><figcaption>**Figure 7.** Flux, velocity shift, and FWHM of the [Ne II] 12.8 µm line, with the narrow/primary component in the top row and the broader/secondary component in the bottom. Cyan circles again mark the position of UV-bright star clusters, and other symbols are as defined in Figure 5.</figcaption></figure>

through Regions 4, 3, and 1, with SFR densities as low as ~1 $M_{\odot}$ yr$^{-1}$ kpc$^{-2}$.

We note the M.-Y. Zhuang et al. (2019) prescriptions were calibrated using Starburst99 SSP models (C. Leitherer et al. 1999) only up to ages of 2.5 Myr, while the stellar populations in our field span ages up to ≳10 Myr. The star cluster ages (≤3 Myr) we estimate from archival HST/COS spectroscopy in Regions 2 and 4 (see Section 5.4) are roughly consistent with this calibration. Slightly older stellar populations are mainly seen in Region 3 (e.g., 8.6 Myr in the photometric analysis from J. H. Knapen et al. 2010). This area is where we see the faintest [Ne II] and [Ne III] emission, consistent with a low instantaneous SFR despite being outside the age calibration of the M.-Y. Zhuang et al. (2019) relations.

# 5. Discussion

## 5.1. Molecular Gas Contents of M83's Nuclear Region

Historically, attempts to quantify the amount and distribution of molecular gas in M83 (A. Hirota et al. 2018; J. Koda et al. 2023) and SF galaxies more generally (A. D. Bolatto et al. 2013; R. Pavesi et al. 2018; A. K. Leroy et al. 2021) have relied on observations of CO (and other, less abundant tracers —e.g., OH; D. Xu et al. 2016) which, unlike $H_2$, have a nonzero dipole moment and hence are strong rotational emitters. CO-based molecular gas measurements usually require an assumed value for the factor $\alpha_{CO}$ to convert between the intensity of CO emission and $H_2$ mass surface density, although the value of $\alpha_{CO}$ can vary significantly with time, environment, and metallicity, even within individual

galaxies (K. M. Sandstrom et al. 2013; C.-Y. Hu et al. 2022; I.-D. Chiang et al. 2024; A. M. Lee et al. 2024).

Our use of the TS16 model, in principle, allows us to extract estimates of the total molecular gas mass across our FOV in addition to only the warm ($T > 200$ K) $H_2$. TS16 distinguishes between the lower temperature bound for the power-law model $T_l$ and the sensitivity temperature $T_s$, or the temperature below which an increase in the $H_2$ mass results in no measurable change to the line fluxes or excitation, which they determine following Y. Avni (1976) by evaluating their model over a grid of $n$ and $T_l$ values and examining the change in $\chi^2$ relative to its minimum value, $\Delta\chi^2$. For the Spitzer/IRS spectra they considered in that work, only a handful of galaxies (primarily LINER and Seyfert hosts) showed evidence for a well-defined minimum in $\Delta\chi^2$, such that extrapolating the power-law model for those galaxies to temperatures less than ~120 K results in a poorer fit. They interpret this as evidence for a warm gas excess in these sources, such that the majority of $H_2$ is directly detected via the MIR rotational transitions. Most galaxies in their sample have no well-defined minimum in $\Delta\chi^2$ space, meaning that any $T_l < T_s$ remains consistent with the observed line ratios; their sample average is $T_s = 81$ K.

For completeness, we performed the same $\Delta\chi^2$ evaluation exercise as in TS16 for select spaxel-level spectra and for the FOV-integrated spectrum to obtain estimates of the sensitivity temperature for our observations. We find in all cases that there is a well-defined minimum in $\Delta\chi^2$, such that extrapolating below the best-fit lower temperature inferred by the model ($T_l \sim 220-270$ K) degrades the fit. However, we stress that unlike in TS16, our data and analysis do not include the low-excitation S(0) line. Without this line to constrain the colder
<figure><img src="image_13.png" /><figcaption>**Figure 8.** Top: line flux ratio of [Ne III]/[Ne II], considering only the narrow component of [Ne II] and [Ne III] (or the single-Gaussian fit, if a two-component fit was not needed for some spectra). Bottom: SFR surface density across our MRS cube as computed using the diagnostic of M.-Y. Zhuang et al. (2019). Cyan circles mark the position of UV-bright star clusters from J. Harris et al. (2001), and gray circles mark the locations of SNRs from P. F. Winkler et al. (2023). White contours in the lower panel show curves of constant velocity $v \in [-90, -40, 0, 40, 90]$ km s⁻¹ for the secondary component of [Ne II] (with dotted being the most negative and thick solid being the most positive).</figcaption></figure>

H₂, the power-law temperature model has little incentive to “prefer” low temperatures at a fixed slope $n$. We therefore cannot directly constrain a sensitivity temperature comparable to that in TS16 for the observations presented here. However, as discussed in Section 4.2, we nonetheless estimated the cool + warm H₂ mass down to 80 K, $7.93_{-2.59}^{+3.52} \times 10^6 M_\odot$, to serve as a lower limit to the total amount of molecular gas in M83’s nuclear region.

TS16 also discuss the utility of a calibration temperature, which we label here as $T_{cal}$, defined such that at $T_I = T_{cal}$ the H₂ mass inferred by their model equals the cold/total molecular gas mass derived from CO emission, $M_{H2}(T > T_{cal}) = M_{H2(CO)}$. In that work they assume a Galactic $\alpha_{CO} = 3.2 M_\odot$ (K km s⁻¹ pc²)⁻¹, which we also adopted in H23 and the present work. TS16 report an average $T_{cal} = 49 \pm 9$ K for their Spitzer/IRS sample, such that H₂ warmer than ~50 K can account for the entirety of the molecular gas mass inferred from CO in their galaxies.

We performed a first-pass analysis of this type in H23, in which we applied the TS16 model to the integrated spectra of our four regions. In that work, we measured a warm H₂ mass of $8.76 \pm 0.33 \times 10^4 M_\odot$ summed across the four regional spectra, tracing H₂ from 2000 K down to 252–301 K. We also measured $M_{H2}(T > 50 \text{ K}) = 6.79 \pm 0.54 \times 10^7 M_\odot$, which we took as an estimate of the total H₂ mass in our field.

Recalling the three definitions of $T_I'$ we outlined in Section 4.2, the warm H₂ mass estimate from H23 is most consistent with Definition (1), which uses the best-fit value of $T_I$ as the lower bound on the mass integral. Using that limit, in the present work we measured a warm H₂ mass of $1.50_{-0.09}^{+0.09} \times 10^5 M_\odot$ summed across our FOV. Although the nominal warm gas mass we measure in this work is higher by a factor of ~2, we note that H23 did not apply attenuation corrections to their line fluxes. Since the H₂ mass estimate is proportional to the S(1) line flux in the TS16 framework, the difference between the two values is almost entirely accounted for by our use of attenuation corrections in the present work.

If we instead extrapolate the best-fit TS16 models for each pixel to $T_{cal} = 50$ K, we obtain $M_{H2}(T > 50 \text{ K}) = 8.17_{-4.04}^{+8.67} \times 10^7 M_\odot$. Although the nominal “total” mass is again slightly higher than that reported in H23, it remains consistent within the (considerable) uncertainties, especially once attenuation corrections are taken into account. The bulk of the uncertainty on our new estimate of the total molecular gas mass is attributable to uncertainties on the per-pixel power-law slopes.

## 5.2. Potential CO-dark Molecular Gas

The observational dependence on CO introduces the problem of “CO-dark” molecular gas (e.g., M. G. Wolfire et al. 2010), or reservoirs of H₂ that are not traced by spatial CO emission. As FUV photons and cosmic rays (e.g., T. G. Bisbas et al. 2017) permeate into a molecular cloud, the CO molecules dissociate and leave behind an envelope of ionized and neutral carbon in addition to the colder, denser cloud core. H₂, meanwhile, can self-shield from photodissociation (N. Y. Gnedin & B. T. Draine 2014) by FUV photons, which leaves a potentially significant reservoir of gas that is coinhabited (partially or fully) by H₂, C, and $C^+$ but not CO (S. C. O. Glover & P. C. Clark 2012; S. C. Madden et al. 2020; C.-Y. Hu et al. 2021).

In H23, we used the TS16 framework to estimate the H₂ gas mass $M_{H2}(T > 50 \text{ K})$ in the integrated spectra of our four regions, which we took as a measure of the total molecular gas mass. Using the CO(1–0) observations of A. Hirota et al. (2018) and a Galactic CO-to-H₂ conversion factor ($\alpha_{CO} = 3.2 M_\odot$ (K km s⁻¹ pc²)⁻¹), we compared this “total” molecular gas mass per region to that inferred from CO using traditional methods and found $M_{H2}(T > 50 \text{ K}) > M_{H2(CO)}$ in all four pointings. We estimated that, depending on which region/environment we consider, between ~27% and 92% of the molecular gas mass is not accounted for through CO(1–0) emission—in other words, CO-dark gas mass fractions upwards of ~30%, even in the supersolar metallicity core of M83.

While a direct comparison of pixel-level gas masses between our MRS observations and CO data from the Atacama Large Millimeter/submillimeter Array (ALMA) is beyond the scope of this work, the resolved power-law models presented above allow us to more accurately examine the CO-dark gas contents of M83’s nuclear region when considering the
entirety of our FOV. As summarized in Table 2, at temperatures >80 K, the TS16 model can only recover ~46% of the CO-based molecular gas mass in our field. However, given the moderately steep power-law slopes we measure in some areas (Figure 5), extrapolating to lower temperatures will rapidly inflate the predicted molecular gas mass. At $T'_l = 50$ K, the total $M_{H2}$ mass is a factor of ~4.7 higher than that inferred from CO observations, implying that ~79% of the molecular gas content of M83's nuclear region is not traced by CO.

On the other hand, if we assume that the CO-based gas mass estimates are correct, we may tweak the chosen value of $T'_l$ to find an appropriate $T_{cal}$ for our observations, such that $M_{H2}(T > T_{cal}) = M_{H2}(CO)$. We iterated over values of $T'_l$ to find a range of temperatures that satisfy $M_{H2}(T > T'_l) = M_{H2}(CO)$, again only considering the sum of our FOV. For our resolved analysis, a range of temperatures $T_{cal}(60–70 K)$ yield an $H_2$ mass estimate that is consistent with $M_{H2}(CO) = 1.715 \times 10^7 M_\odot$ within the uncertainties. For the integrated-light spectrum, we find that $T_{cal} = 57$ K provides the best match to $M_{H2}(CO)$. These are generally consistent with the range of $T_{cal}$ values given in Table 4 of TS16 for their sample of SINGS galaxies.

While temperatures ≳ 50 K are significantly higher than the ~10–30 K range typically assumed for the dense, imminently star-forming parts of molecular clouds, the prediction of significant CO-dark gas reservoirs even in high-metallicity galaxies has been borne out repeatedly in simulations. For example, in the simulations by C.-Y. Hu et al. (2021), their models with time-dependent $H_2$ chemistry suggest a shallow radial abundance profile for the $H_2$ gas such that a significant fraction of the $H_2$ mass is found in low-density regions, far from the CO-bearing core. When showing the density distribution in their simulations weighted by CO mass, C.-Y. Hu et al. (2021) find that the bulk of the CO is found at densities comparable to or greater than the densities at which C is readily converted to CO. When weighting by $H_2$ mass, however, the majority of $H_2$ is found at densities significantly lower than the H/H$_2$ conversion density. This suggests that a significant fraction of the $H_2$ mass is found not in the CO-bearing cloud cores, but in the atomic-dominated regime. While this effect is most extreme at low metallicities (and hence intrinsically low CO abundances), even at $Z = 1–3 \times Z_\odot$, between 20% and 35% of the $H_2$ mass can be found in gas with abundances $log_{10}(2x_{H_2}) < -0.5$ in the C.-Y. Hu et al. (2021) simulations. When translated to a mass fraction of CO-dark gas at (super-)solar metallicities, they report that even high-sensitivity CO(1–0) observations (down to column density limits of ~6 × 10$^{14}$ cm$^{-2}$), ~55%–60% (~58%–70%) of the $H_2$ gas mass is CO dark in their time-dependent (steady-state) models.

Even in the context of C.-Y. Hu et al. (2021) and other theoretical works which predict the existence of CO-dark gas in galaxies (M. G. Wolfire et al. 2010; R. J. Smith et al. 2014), CO-dark mass fractions of ~80% in the $\gtrsim 2 \times Z_\odot$ environments of M83’s nucleus are surprising and reinforce the need for small-scale spectroscopic observations of the ISM in starbursting galaxies at all metallicities. It is worth noting that our estimate of $M_{H2}(CO)$ in our FOV already has a CO-to-$H_2$ conversion factor baked in, which (in principle) should account for the fact that CO does not perfectly trace the low-density $H_2$. The fact that our $H_2$ mass measurements at cool temperatures $\gtrsim 70$ K (let alone the colder, denser ISM)

are comparable to $M_{H2}(CO)$ suggests that a higher-than-Galactic value of $\alpha_{CO}$ may be necessary even in solar or supersolar environments. A possible exception to this is the nucleus proper, which has the highest best-fit values of $T'_l (>300$ K) in the TS16 framework (Figure 5). This suggests that the bulk of the $H_2$ in the central $r \sim 15$ pc of the galaxy is in a warm excess phase and is detected directly from rotational emission.

The notion that high values of $\alpha_{CO}$ are needed for the nuclear region of M83 runs contrary to several literature results which suggest galaxy centers, starbursting galaxies, and galaxies with high metallicity are expected to have $\alpha_{CO}$ factors lower than the Galactic value (e.g., $\alpha_{CO} \lesssim 1.0 M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$, versus Galactic values $\alpha_{CO} \approx 3–4 M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$); see A. D. Bolatto et al. (2013) and references therein. For example, recent work by A. M. Lee et al. (2024) shows that, within M83’s disk, $\alpha_{CO}$ decreases steadily with radius from ~5–7 $M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$) at $R = 6$ kpc to ~2 $M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$) at $R = 2$ kpc. ALMA observations of barred galaxy centers at ~100 pc scales (Y.-H. Teng et al. 2022, 2023) find generally low values of $\alpha_{CO}$ ($\lesssim 0.5 M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$) that decrease with galactocentric radius, driven primarily by a correlation with the optical depth of CO emission.

In the context of these results, a low assumed value of $\alpha_{CO}$ may be more appropriate for M83’s nuclear region than the value we adopted in this work ($3.2 M_\odot$ (K km s$^{-1}$ pc$^2$)$^{-1}$). However, a low $\alpha_{CO}$ would result in even lower estimates of $M_{H2}(CO)$ in our field, exacerbating the discrepancy with the $H_2$ masses from the TS16 model which include the coldest ($\lesssim 60$ K) gas. Comprehensive modeling of the $H_2$ rotational lines, CO lines, dust emission, and other putative tracers of CO-dark gas (e.g., the $^3P_1 \rightarrow ^3P_0$ of C I; P. P. Papadopoulos et al. 2004, 2018; T. G. Bisbas et al. 2017; L. Ramambason et al. 2024) will help to better understand the relationship between diffuse $H_2$ and traditional molecular gas tracers in heavily star-forming environments.

### 5.3. A Massive, Obscured Point Source

We now briefly discuss a point source which we identify through the flux and kinematic maps of the S(1) line shown in the top row of Figure 4. Specifically, we identify a compact ($\lesssim 5$ pc) source at $(\alpha, \delta) = (13:37:00.555, -29:51:53.900)$, which (1) is slightly redshifted (by ~10 km s$^{-1}$) relative to its surroundings and (2) is fairly broad and spectrally resolved, with an FWHM of >150 km s$^{-1}$ at its core. Additionally, this kinematic structure appears at high significance only in the S(1) line, with neither Ne lines nor other $H_2$ rotational lines indicating the presence of a point source at the same position.

We also note a moderately reddened (possibly partially embedded) star cluster at $(\alpha, \delta) = (13:37:00.5205, -29:51:53.702)$, as measured from JWST/NIRCam imaging from the FEAST program (see the next section). We show multiwavelength zoom-in views of this area in Figure 9, including an FWHM map of the $H_2$ S(1) line; CO(2–1) emission (ALMA pID 2013.1.01161.S, PI: K. Sakamoto), and F187N and F335M imaging from JWST-FEAST (A. Adamo et al. 2025, in preparation). The F187N image in Figure 9(c) clearly shows that the partially embedded cluster has blown away most of its natal material. The ionized shell is a quite striking feature that is “pinched” in the northwest and southeast direction, presumably due to higher density material, and more freely expanding to the northeast. The “pinch” point
<figure><img src="image_14.png" /><figcaption>**Figure 9.** Zoom-in view of Region 2, highlighting the multiband properties of an unknown, obscured point source. From top to bottom, we show (a) an H₂ S(1) FWHM map; (b) single-channel CO(2–1) emission at –20 km s⁻¹; (c) Paα imaging from JWST-FEAST; and (d) F335M emission from JWST-FEAST. Brown–yellow contours in (a) show the H₂ S(1) flux (with gold marking the highest fluxes), while the green contours in (c) and (d) mark regions with CO(2–1) intensity > 150 mJy beam⁻¹. The obscured point source and a nearby reddened star cluster are respectively marked with yellow and pink dotted circles.</figcaption></figure>

to the southwest corresponds to the edge of a CO cloud (Figure 9(b)) and the location of the H₂ point source, which lies 0.43 or 9.6 pc away.

Recent studies of the spiral galaxy NGC 1365 from the PHANGS collaboration (B. C. Whitmore et al. 2023) showed that YMCs with ages ~1.3 ± 0.7 Myr are fully obscured in the optical and can remain completely or partly obscured for up to ~3.7 ± 1.1 Myr. This is consistent with clearing timescales of 3–5 Myr based on HST data only, as determined for M83 (K. Hollyhead et al. 2015; S. Deshmukh et al. 2024) and other galaxies (e.g., M. Messa et al. 2021; T. McQuaid et al. 2024). The stellar population modeling we discuss in Section 5.4

suggests very young ages (2 ± 1 Myr) even for the UV-brightest and largely unobscured clusters in our Region 2. The young stellar ages in this area, along with the spatial coincidence of the H₂ source with CO-emitting gas, leads us to speculate that the kinematically distinct H₂ point source may be a forming or fully embedded star cluster.

### 5.4. Gaseous Confirmation of a Stellar Age Gradient?

Nearby starbursts, such as the nuclear region in M83, make optimal laboratories for unraveling the mechanisms operating in extreme environments and across different phases of SF as their proximity affords exquisite spatial resolution. This is clearly highlighted in the true-color HST optical image displayed in Figure 10(e). This figure shows the various complexities in this starburst system, with some regions almost completely obscured toward the north (in Region 2), partially cleared ISM channels in the west–southwest area (Region 4), and fully cleared regions toward the southeast (Region 3).

The JWST and HST color image in panel (a) of Figure 10 shows the spatial distribution of the stellar populations, specifically contrasting the old stars (500–1000 Myr; shown in yellow) with young stars (<25 Myr; shown in blue). The younger, bluer population in Figure 10(a) depict a clear “active ringlet,” as labeled by J. Harris et al. (2001), who find an apparent age gradient through a photometric study investigating the properties of the massive clusters in this circumnuclear region. Using HST broadband and narrowband filters, J. Harris et al. (2001) report that stellar clusters with ages < 5 Myr are preferentially located along the northwestern area (Region 2 in this work). The presence of this age gradient along the starburst’s arc, running from northern to southeastern regions, has since then been studied and debated (R. J. Díaz et al. 2006; J. H. Knapen et al. 2010; A. Wofford et al. 2011).

To further investigate the possible age gradient, we have analyzed archival HST/COS observations targeting YMCs in Regions 2 and 4. In the final two panels of Figure 10, we show with white dotted circles the locations of the four COS pointings, which encompass multiple star clusters in a single 2.5 aperture. We characterize the dominant stellar population in each COS pointing using the SESAMME software (L. H. Jones et al. 2023a). Being a Python-based full spectrum fitting code, SESAMME uses Markov Chain Monte Carlo methods to obtain estimates of the cluster age, metallicity, $E(B-V)$ value, and when the distance to the cluster is known, the user can also constrain the cluster mass. A caveat to the analysis of the COS observations with this software is that SESAMME assumes individual stellar populations for each input spectrum. In the context of our test, this means that our analysis then assumes a single dominant stellar population when inferring the stellar properties of the FUV integrated-light spectra. We find that the most massive stellar population encompassed in our COS pointings resides in Region 2, with a mass estimate of 1.13(±0.02) × 10⁶ M☉ and an age of 2 ± 1 Myr. For the remaining targets in Region 4 we infer an age of 3 ± 1 Myr for the two easternmost clusters, and 2 ± 1 Myr for the pointing west of Region 4. Generally, this agrees with the originally proposed trend in the active ringlet, where some of the youngest stellar clusters are found in Region 2. We note that the science programs collecting the HST/COS archival data (HST program IDs: 11579, 14681, and 15193) analyzed here primarily targeted the brightest FUV objects in the nuclear region of M83. Not surprisingly, we estimate very young ages for these clusters.
<figure><img src="image_15.png" /><figcaption>**Figure 10.** Multiwavelength view of the center of M83 using images from HST/WFC3, JWST/NIRCam, JWST/MIRIM, and ALMA. The brightness of some images have been rescaled to better show small-scale structures. Solid squares denote the Channel 1 FOV of our four MRS pointings, and the dotted square approximates the total Channel 4 FOV. White dotted and cyan circles are as described in previous figures. The plus symbol, diamond, and variably colored circle respectively denote the position of the optical nucleus, a partially embedded YMC, and an unidentified H₂ point source described in Section 5.3. (a) Old (yellow) and young (blue) stellar populations as traced by NIRCam F300W (JWST-FEAST) and WFC3 F336W (GO 14059, PI: R. Soria). (b) Differentially extincted ionized gas as traced by Paα (red; JWST-FEAST) and Hα (green; GO 11360, PI: R. O'Connell). (c) Ionized PAH emission from the 7.7 (red) and 3.3 µm (blue) features (JWST-FEAST). (d) CO(2–1) data from ALMA (2022.1.00951, PI: Thater). Purple–pink contours trace Σ<sub>SFR</sub> using the M.-Y. Zhuang et al. (2019) diagnostic at intervals of [2, 5, 15, 30, 45, 55] M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup>. (e) True-color optical image using WFC3 F814W, F555W, and F336W (GO 14059, PI: R. Soria). (f) Warm H₂ mass distribution (at temperatures > 200 K) from this work.</figcaption></figure>

In the context of the “active ringlet,” the spaxel-level analysis we present here provides additional support for this age gradient. For example, the bulk of SF activity in our FOV (with Σ<sub>SFR</sub> > 50 M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup>) is found in a clump coincident with (or just eastward of) the massive grouping of very young, unobscured star clusters in Region 2 (Figure 8), plus a plume of high SFR extending northward. Conversely, the lowest SFRs are generally found in Region 3, where the [Ne II] emission is significantly broadened and blueshifted.

Each of these structures—the low-SFR patch and the high-SFR clump and plume—also appear in other figures shown in this work. The low-SFR patch, for example, is coincident with a region of faint CO(2–1) and H₂ emission and fairly steep power-law slopes in the TS16 model, suggesting little molecular gas mass and low H₂ excitation. This patch also shows low optical extinction and is coincident with slightly evolved stellar populations (8.6 Myr in Region A of J. H. Knapen et al. 2010). While we do not attempt to distinguish (and indeed cannot in the present analysis) a specific mechanism that drove the creation of this dustless patch (e.g., photoionization versus shocks), the combination of low-density ISM, ionizing outflows, and apparent lack of <5 Myr old

stellar populations in this area strongly suggests a link to previous bursts and cessations of SF.

The high-SFR clump and plume, meanwhile, roughly corresponds to a region with both high H₂ excitation (Figure 5) and modest warm H₂ mass (Figure 10(f)). Interestingly, the CO(2–1) emission is also fairly weak in parts of the clump, where we see bright, ringed structures in Paα, F335M, and F770W imaging from the JWST-FEAST program (Figures 9(c, d) and 10(c)), which suggests that recently formed stars may be destroying CO and exciting H₂ in this area. Conversely, the strongest CO(2–1) emission and highest warm H₂ surface densities are more coincident with a knot of high obscuration which extends to the northeast of Region 2, including the kinematic source seen in H₂ S(1). This knot is dark even in JWST/NIRCam imaging (Figures 10(a, b, e)), suggesting extremely high column densities.

5.5. Spatially Varying Interstellar Medium Properties in M83

To further illustrate our confirmation of the active star-
forming ringlet, we extracted spectra within r = 2 pixel (0.′′26)
apertures centered on five locations of interest which illustrate
<figure><img src="image_16.png" /><figcaption>**Figure 11.** Aperture-extracted spectra (with no attenuation corrections) of five representative types of environments observed in our MRS observations. In all panels spectra have been normalized to the flux at 7.15 µm. Left: full spectra (up to 20.7 µm) of (1) the dusty knot in Region 2 (brown); (2) the partially embedded cluster in Region 2 (pink); (3) the optical center (yellow); (4) the center of the ionizing outflow in Region 3 (blue); and (5) an H II region near the string of YMCs in Region 4 (green). The inset image is the τ<sub>warm</sub> map shown in Figure 3 and includes markers showing the location of the apertures (r = 2 pixel or 0"/26). Top right: zoom-in view of the 5–9 µm range, which showcases differences in continuum slope at ~7 µm and in relative strength of emission lines and the 6.2 and 7.7 µm PAH complexes. Bottom right: zoom-in view of the 16–19 µm range, which showcases differences in the relative strength of the 17 µm PAH complex and of the H<sub>2</sub> S(1), [P III] 17.88 µm, and [Fe II] 17.94 µm lines.</figcaption></figure>

the breadth of environments in our FOV. These include (1) a
knot in Region 2 with a continuum dominated by cool dust; (2)
a modestly reddened, likely partially embedded star cluster in
Region 2; (3) the optical nucleus of M83; (4) the center of the
[Ne II] outflow in Region 3; and (5) an H II region near the
string of YMCs in Region 4. We show spectra for these five
locations without any attenuation corrections in Figure 11,
with the inset image showing the locations and labels of the
extraction apertures overlaid on our map of τwarm. The spectra
have been normalized to the flux in a line-free region of the
spectrum around ~7.15 µm for easy comparison of the relative
continuum shapes and feature strengths. Although we defer a
detailed analysis of PAHs and the ionized gas in the core of
M83 to other works (S. Hernandez et al. 2025; A. Togi et al.
2025, in preparation), we briefly highlight spectral features
which support our interpretation of the dust and molecular gas
in these five environments.

The broad dust properties, for example, clearly vary strongly
at the scale of ~100 pc, as can be seen by the variety of MIR
slopes in the left panel of Figure 11. Spectra (1) and (2), which
are respectively drawn from a highly and modestly attenuated
part of our FOV, show deep absorption troughs at 9.7 µm due
to silicates, as well as a substantial amount of flux at the red
end of our spectra. This suggests the presence of cooler dust

and ISM, which is corroborated by the bright CO(2–1)
emission (Figure 10(d)) and by the low best-fit temperatures
inferred by the TS16 model (bottom panel of Figure 5) in the
same location. The relative strengths of the neighboring [P III]
17.88 µm (ionizing potential (IP) = 19.7 eV, tracing moderate
ionization zones) and [Fe II] 17.94 µm lines (IP = 7.9 eV,
tracing low-ionization zones and/or the shock destruction of
iron-rich dust grains; see B. D. Savage & K. R. Sembach 1996)
also vary with location, with strong [P III] and weak [Fe II] in
environments which are cooler, dustier, and host less-evolved
stellar populations (lower-right panel of Figure 11).

In contrast, the [P III]/[Fe II] line ratio approaches unity and
then inverts as the eye travels clockwise through our FOV,
with comparable line strengths in the more typical H II region
and dominant [Fe II] in the high-excitation nucleus and outflow
region. Additionally, spectrum (4) is nearly flat across the
broad wavelength range covered by our MRS spectroscopy
with minimal absorption at 9.7 µm, suggesting little to no dust
in Region 3 (J. A. Marshall et al. 2007). Indeed, an
examination of archival optical imaging (Figure 10(e)) shows
a virtually dustless window through the ISM of M83
coincident with the patch of blueshifted and highly dispersed
[Ne II] gas we identify in this work. Finally, the optical nucleus
and outflow spectra (3 and 4; yellow and blue curves in
Figure 11) also show weak but mildly broadened [Ne v] emission at 14.3 µm, which is absent in the other spectra; spectrum 3 additionally shows emission from the very high ionization [Ne VI] 7.65 µm line. We explore the physical drivers of the [Ne v] and [Ne VI] emission (e.g., fast radiative shocks, X-ray binaries, etc.) in a separate work (S. Hernandez et al. 2025).

In H23, we discussed the distribution of iron (as traced by the [Fe II] 25.99 µm line) and its correspondence to soft X-ray emission, which is thought to be driven by both SNRs and strong winds from young, massive stars (K. S. Long et al. 2014). We found that the [Fe II] emission was generally similar in structure to that of soft X-rays, but had little overall correspondence to the locations of known SNRs. This led us to consider the framework described in the review by D. Zhang (2018), in which hot starburst-driven galactic winds collide with the cooler, surrounding ISM to induce shocks. These shocks would then produce both soft X-rays and diffuse [Fe II] emission (stemming from the destruction of dust grains that release depleted iron back into the ISM).

As in H23, we emphasize that unambiguous confirmation of this scenario in M83's nuclear region will require self-consistent modeling of the photo- versus shock ionization, which is presented in a separate work (S. Hernandez et al. 2025). However, the combination of features discussed above for the high-excitation outflow spectrum—the absence of <5 Myr old YMCs, the absence of dust seen in both optical imaging and our MRS spectroscopy, a low [P III]/[Fe II] line ratio, broadened and blueshifted [Ne II] emission, and the presence of [Ne v] (IP = 97.12 eV)—paint a picture in which previous episodes of massive SF induce shocks and feedback onto the surrounding ISM.

## 6. Conclusions

We used MRS spectroscopy from JWST/MIRI to examine the warm molecular gas content and SF activity in the nuclear region of M83 on the scale of ~3 pc spaxel⁻¹. Our resolved analyses illustrate the importance of high-sensitivity spectroscopic observations with JWST to accurately characterize the multiphase ISM, the formation of massive star clusters, and the effects of feedback at the smallest scales. We summarize our findings as follows.

1. Our continuum and PAH modeling with CAFE shows a remarkable variation in the temperatures and optical depths of dust across our ~200 × 200 pc² FOV. In regions with high dust columns and continuum and line fluxes can be attenuated by nonnegligible amounts (up to 30%) even at the long wavelengths accessible to MRS (Section 3.1).

2. Pure rotational transitions of H₂ show a range of intensities and excitation conditions within M83's nuclear region, with the most excited emission coincident with the optical nucleus and with groupings of young, massive star clusters. Maps of the H₂ emission and line widths reveal complex filamentary and clumpy structures (Section 4).

3. We identify a point-like source in the flux and kinematic maps of the H₂ S(1) line, which we propose is a forming or fully embedded star cluster given its location with

respect to the CO (2–1) emission and apparent size (~5 pc; Section 5.3).

4. Using the power-law temperature distribution model of TS16, we derive H₂ masses down to various temperature limits. The warmest H₂ gas (>200 K), totaling 2.26 × 10⁵ M☉, accounts for ~1% of the total molecular gas mass in our FOV as inferred from CO observations (A. Hirota et al. 2018), while integrating the TS16 power-law models to temperatures Tcal ~ 57–70 K provides a reasonable match to the CO-based mass estimates. Integrating to still-lower temperatures drastically increases the mass of H₂ to over 400% of the CO-based mass estimates, suggesting either the presence of a significant reservoir of gas that is not traced by CO or the need for further constraints on αCO even in the supersolar metallicity environment of M83’s center. Alternative estimates of the molecular gas mass (using, e.g., C I far-IR lines; P. P. Papadopoulos et al. 2004; L. Ramambason et al. 2024) will be critical for confirming either scenario (Sections 4.2 and 5.2).

5. By performing the same analysis on our spaxel-level data and on the integrated-light spectrum of the nuclear region, we find hints that, within the TS16 framework, the inferred H₂ mass may depend weakly on the physical scale probed (<5 versus 200 pc) due to blending of high and low-excitation regions (Section 4.2).

6. The [Ne II] 12.81 µm line shows evidence for a secondary, high-FWHM kinematic component that is ubiquitous across our FOV, with a patch of our Region 3 showing evidence for ionized outflows. Converting the [Ne II] and [Ne III] luminosities to SFR with the diagnostic of M.-Y. Zhuang et al. (2019) reveals modest SFR surface densities in Regions 1, 3, and 4, but fairly high ones in Region 2, reaching up to 75 M☉ yr⁻¹ kpc⁻² in 3 × 3 pc² pixels (Section 4.3).

7. We model the integrated FUV light from four HST/COS pointings centered on groupings of YMCs, finding that the youngest and most massive clusters coincide with regions of high SFR and high H₂ excitation, and are adjacent to extremely dusty, CO-bright clouds. When placed in the context of the high-resolution, multi-wavelength ancillary data available for M83's nuclear region, this supports the notion of a star-forming ringlet with a mild age gradient as first proposed by J. Harris et al. (2001; Section 5.4).

## Acknowledgments

This work is based on observations made with the NASA/ESA/CSA JWST. Support for program JWST-GO-02291 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Associations of Universities for Research in Astronomy, Incorporated, under NASA contract NAS 5-26555. This work is based on observations made with the NASA/ESA/CSA JWST. The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These observations are associated with program #02219. S.H. acknowledges support from the European Space Agency (ESA). L.R. gratefully acknowledges funding from the
DFG through an Emmy Noether Research Group (grant No. CH2137/1-1).

Software: CAFE (J. A. Marshall et al. 2007; T. Diaz-Santos et al. 2025), SciPy (P. Virtanen et al. 2020), numpy (S. van der Walt et al. 2011), Astropy (Astropy Collaboration et al. 2013, 2018, 2022) Matplotlib (J. D. Hunter 2007), cmasher (E. van der Velden 2020), and Jupyter (T. Kluyver et al. 2016).

## Appendix
### Single and Double-Gaussian Fits to [Ne III]

In Figure A1, we show the flux, velocity shift, and FWHM of the [Ne III] 15.5 µm line using both a single and a double-Gaussian component. Details on the line fitting and on the application of the statistical F-test for the two-component fits are given in Sections 3.2 and 4.3, respectively.

<figure><img src="image_17.png" /><figcaption>**Figure A1.** Flux, velocity shift, and FWHM of the [Ne III] 15.5 µm line using either a single-Gaussian fit (top row) or a two-component Gaussian fit (middle and bottom) after application of the statistical F-test and our S/N cut. Cyan circles mark the position of UV-bright star clusters, and other symbols are as defined in Figure 5.</figcaption></figure>
ORCID iDs

Logan H. Jones https://orcid.org/0000-0002-1706-7370

Svea Hernandez https://orcid.org/0000-0003-4857-8699

Linda J. Smith https://orcid.org/0000-0002-0806-168X

Aditya Togi https://orcid.org/0000-0001-5042-3421

Tanio Diaz-Santos https://orcid.org/0000-0003-0699-6083

Alessandra Aloisi https://orcid.org/0000-0003-4137-882X

William Blair https://orcid.org/0000-0003-2379-6518

Alec S. Hirschauer https://orcid.org/0000-0002-2954-8622

Leslie K. Hunt https://orcid.org/0000-0001-9162-2371

Bethan L. James https://orcid.org/0000-0003-4372-2006

Nimisha Kumari https://orcid.org/0000-0002-5320-2568

Vianney Lebouteiller https://orcid.org/0000-0002-7716-6223

Matilde Mingozi https://orcid.org/0000-0003-2589-762X

Lise Ramambason https://orcid.org/0000-0002-9190-9986

References

Appleton, P. N., Xu, K. C., Reach, W., et al. 2006, ApJL, 639, L51

Armus, L., Lai, T., U, V., et al. 2023, ApJL, 942, L37

Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

Astropy Collaboration, Price-Whelan, A. M., Sipöcz, B. M., et al. 2018, AJ, 156, 123

Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

Avni, Y. 1976, ApJ, 210, 642

Béthermin, M., Accard, C., Guillaume, C., et al. 2023, A&A, 680, L8

Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846

Bik, A., Álvarez-Márquez, J., Colina, L., et al. 2024, A&A, 686, A3

Bisbas, T. G., van Dishoeck, E. F., Papadopoulos, P. P., et al. 2017, ApJ, 839, 90

Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207

Chiang, I.-D., Sandstrom, K. M., Chastenet, J., et al. 2024, ApJ, 964, 18

Costa-Souza, J. H., Riffel, R. A., Souza-Oliveira, G. L., et al. 2024, ApJ, 974, 127

Deshmukh, S., Linden, S. T., Calzetti, D., et al. 2024, ApJ, 974, 24

Díaz, R. J., Dottori, H., Aguero, M. P., et al. 2006, ApJ, 652, 1122

Diaz-Santos, T., Lai, T. S. Y., Finnerty, L., et al., 2025 CAFE: Continuum And Feature Extraction Tool, Astrophysics Source Code Library, ascl:2501.001

Donnan, F. R., García-Bernete, I., Rigopoulou, D., et al. 2023a, MNRAS, 519, 3691

Donnan, F. R., García-Bernete, I., Rigopoulou, D., et al. 2024, MNRAS, 529, 1386

Donnan, F. R., Rigopoulou, D., García-Bernete, I., et al. 2023b, A&A, 669, A87

Glover, S. C. O., & Clark, P. C. 2012, MNRAS, 426, 377

Gnedin, N. Y., & Draine, B. T. 2014, ApJ, 795, 37

Harris, J., Calzetti, D., Gallagher, J. S. I., Conselice, C. J., & Smith, D. A. 2001, AJ, 122, 3046

Hernandez, S., Aloisi, A., James, B. L., et al. 2021, ApJ, 908, 226

Hernandez, S., Jones, L., Smith, L. J., et al. 2023, ApJ, 948, 124

Hernandez, S., Smith, L. J., Jones, L. H., et al. 2025, ApJ, 983, 154

Hirota, A., Egusa, F., Baba, J., et al. 2018, PASJ, 70, 73

Ho, L. C., & Keto, E. 2007, ApJ, 658, 314

Hollyhead, K., Bastian, N., Adamo, A., et al. 2015, MNRAS, 449, 1106

Hu, C.-Y., Schrubá, A., Sternberg, A., & van Dishoeck, E. F. 2022, ApJ, 931, 28

Hu, C.-Y., Sternberg, A., & van Dishoeck, E. F. 2021, ApJ, 920, 44

Hunt, Q., Gallo, E., Chandar, R., et al. 2021, ApJ, 912, 31

Hunter, J. D. 2007, CSE, 9, 90

Inami, H., Armus, L., Charmandaris, V., et al. 2013, ApJ, 777, 156

James, B. L., Aloisi, A., Heckman, T., Sohn, S. T., & Wolfe, M. A. 2014, ApJ, 795, 109

Jones, L. H., Hernandez, S., Smith, L. J., et al. 2023a, ApJ, 959, 20

Jones, O. C., Álvarez-Márquez, J., Sloan, G. C., et al. 2023b, MNRAS, 523, 2519

Kennicutt, R. C. J. 1998, ApJ, 498, 541

Kluyver, T., Ragan-Kelley, B., Pérez, F., et al. 2016, Jupyter Notebooks—a publishing format for reproducible computational workflows (Amsterdam: IOS Press), 87

Knapen, J. H., Sharp, R. G., Ryder, S. D., et al. 2010, MNRAS, 408, 797

Koda, J., Hirota, A., Egusa, F., et al. 2023, ApJ, 949, 108

Lai, T. S. Y., Armus, L., U, V., et al. 2022, ApJL, 941, L36

Law, D. R., Morrison, J. E., Argyriou, I., et al. 2023, AJ, 166, 45

Lee, A. M., Koda, J., Hirota, A., Egusa, F., & Heyer, M. 2024, ApJ, 968, 97

Leitherer, C., Schäerer, D., Goldader, J. D., et al. 1999, ApJS, 123, 3

Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2021, ApJS, 257, 43

Long, K. S., Kuntz, K. D., Blair, W. P., et al. 2014, ApJS, 212, 21

Madden, S. C., Cormier, D., Hony, S., et al. 2020, A&A, 643, A141

Maragkoudakis, A., Ivkovich, N., Peeters, E., et al. 2018, MNRAS, 481, 5370

Marshall, J. A., Herter, T. L., Armus, L., et al. 2007, ApJ, 670, 129

Mast, D., Díaz, R. J., & Agüero, M. P. 2006, AJ, 131, 1394

McQuaid, T., Calzetti, D., Linden, S. T., et al. 2024, ApJ, 967, 102

Messa, M., Calzetti, D., Adamo, A., et al. 2021, ApJ, 909, 121

Meyer, M. J., Zwaan, M. A., Webster, R. L., et al. 2004, MNRAS, 350, 1195

Mingozi, M., James, B. L., Arellano-Córdova, K. Z., et al. 2022, ApJ, 939, 110

Ossenkopf, V., Henning, T., & Mathis, J. S. 1992, A&A, 261, 567

Papadopoulos, P. P., Bisbas, T. G., & Zhang, Z.-Y. 2018, MNRAS, 478, 1716

Papadopoulos, P. P., Thi, W. F., & Viti, S. 2004, MNRAS, 351, 147

Pavesi, R., Sharon, C. E., Riechers, D. A., et al. 2018, ApJ, 864, 49

Ramambason, L., Lebouteiller, V., Madden, S. C., et al. 2024, A&A, 681, A14

Rigopoulou, D., Donnan, F. R., García-Bernete, I., et al. 2024, MNRAS, 532, 1598

Rodríguez Del Pino, B., Perna, M., Arribas, S., et al. 2023, A&A, 684, 187

Russell, T. D., White, R. L., Long, K. S., et al. 2020, MNRAS, 495, 479

Saha, A., Thim, F., Tammann, G. A., Reindl, B., & Sandage, A. 2006, ApJS, 165, 108

Sandstrom, K. M., Leroy, A. K., Walter, F., et al. 2013, ApJ, 777, 5

Savage, B. D., & Sembach, K. R. 1996, ApJ, 470, 893

Smith, J. D. T., Draine, B. T., Dale, D. A., et al. 2007, ApJ, 656, 770

Smith, R. J., Glover, S. C. O., Clark, P. C., Klessen, R. S., & Springel, V. 2014, MNRAS, 441, 1628

Teng, Y.-H., Sandstrom, K. M., Sun, J., et al. 2022, ApJ, 925, 72

Teng, Y.-H., Sandstrom, K. M., Sun, J., et al. 2023, ApJ, 950, 119

Thilker, D. A., Bianchi, L., Boissier, S., et al. 2005, ApJL, 619, L79

Togi, A., & Smith, J. D. T. 2016, ApJ, 830, 18

van der Velden, E. 2020, JOSS, 5, 204

van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, CSE, 13, 22

Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, NatMe, 17, 261

Wang, Q. D., Zeng, Y., Bogdán, Á., & Ji, L. 2021, MNRAS, 508, 6155

Westmoquette, M. S., Exter, K. M., Smith, L. J., & Gallagher, J. S. 2007, MNRAS, 381, 894

Whitmore, B. C., Chandar, R., Rodríguez, M. J., et al. 2023, ApJL, 944, L14

Winkler, P. F., Long, K. S., Blair, W. P., & Points, S. D. 2023, ApJ, 943, 15

Wofford, A., Leitherer, C., & Chandar, R. 2011, ApJ, 727, 100

Wolfire, M. G., Hollenbach, D., & McKee, C. F. 2010, ApJ, 716, 1191

Xu, D., Li, D., Yue, N., & Goldsmith, P. F. 2016, ApJ, 819, 22

Young, J., Pope, A., Sajina, A., et al. 2023, ApJL, 958, L5

Zhang, D. 2018, Galax, 6, 114

Zhang, L., & Ho, L. C. 2023, ApJL, 953, L9

Zhuang, M.-Y., Ho, L. C., & Shangguan, J. 2019, ApJ, 873, 103