MUSE-DARK-I: Dark matter halo properties of intermediate-z star-forming galaxies

TL;DR

This paper conducts a comprehensive 3D forward-modeling analysis of DM halos in 127 intermediate-$z$ star-forming galaxies using deep MUSE data, spanning $0.3<z<1.5$ and $ m 8< obreak M_igstar/M_igodot<11$. By decomposing rotation curves into DM, stellar, and gaseous components with a pressure-support correction, the authors compare six DM density profiles and find the DC14 model typically provides the best description, with many galaxies showing DM-dominated inner regions and a substantial fraction having cored profiles ($ abla ho_{ m DM}<0.5$). The DC14 fits yield stellar masses in agreement with SED-based estimates and reveal scaling relations for stellar–halo mass, halo concentration, and halo density that are broadly consistent with $ m \Lambda CDM$ expectations, though with larger scatter and tentative redshift evolution of halo densities. The results imply denser DM halos at intermediate redshift and highlight the effectiveness of 3D kinematic modelling for constraining DM properties in distant, low-mass disks, while also outlining caveats and avenues for future work with larger samples and alternative DM models.

Abstract

[Abridged] We analyse the dark matter (DM) halo properties of 127 0.3<z<1.5 star-forming galaxies (SFGs) down to low stellar masses (8<log(Mstar/Msun)<11), using data from the MUSE Hubble Ultra Deep Field Survey and photometry from HST and JWST. We employ a 3D forward modelling approach to analyse the morpho-kinematics of our sample, enabling measurement of individual rotation curves out to 2-3 times the effective radius. We perform a disk-halo decomposition with a 3D parametric model that includes stellar, gas, and DM components, with pressure support corrections. We validate our methodology on mock data cubes generated from idealised disk simulations. We select the best-fitting DM model among six density profiles, including the Navarro-Frenk-White and the generalised alpha-beta-gamma profile of Di Cintio et al. (2014, DC14). Our Bayesian analysis shows that DC14 performs as well as or better than the other profiles in >80% of the sample. We find that the kinematically inferred stellar masses agree with values from SED fitting. We find that 89% of galaxies have DM fractions >50%. For 66% of SFGs, we infer a DM inner slope, gamma < 0.5, indicating cored DM profiles, but no correlation is found between gamma and star formation rate of the sample. The stellar- and concentration-mass relations agree with theoretical expectations, but with larger scatter. We confirm the anticorrelation between halo scale radius and DM density. The halo scale radii and DM surface densities increase with Mstar, while DM densities stay constant. We find tentative evidence of an evolution of the DM density with z, which suggests that the DM halos of intermediate-z systems are denser than those of local galaxies. In contrast, the halo scale radii are z-invariant.

Figures (24)

• Figure 1: Results from the 3D disk-halo decomposition applied on the mock MUSE cube of the simulated galaxy ID3. Panel (a) shows the mock MUSE white-light image with flux intensity contours overlaid. Panel (b) shows position velocity diagram, with the observed velocity profiles as measured with GalPaK$^{\rm 3D}$ and MPDAFmpdaf overlaid in red and green, respectively. The dotted black line shows the region beyond which the S/N of the emission line falls below unity, whereas the white contours show the flux intensity. Panels (c) and (d) show the observed velocity field obtained using a traditional 2D line fitting code and the modelled velocity field obtained from 3D disk-halo decomposition with GalPaK$^{\rm 3D}$. Panel (e) shows the contribution of the different components (stars-orange, gas-blue, DM-green curve) to the RC (dot-dashed curve; corrected for pressure support) and panel (f) compares the measured DM density profile in green to the true DM density profile in red. The light shaded regions in these 2 panels show the 95% confidence interval. Panels (g) display the posterior distributions (in blue) for a subset of the parameters we fit for: $\log(X) = \log(M_{\star}/M_{\rm{halo}})$, the virial velocity, the concentration, and the disk inclination. Panels (h) show the posterior distributions for parameters derived from the fitted ones, including the DM density profile shape parameters $\alpha$, $\beta$, and $\gamma$ (computed using equations \ref{['alpha']}, \ref{['beta']}, and \ref{['gamma']}, respectively), as well as the stellar mass $\log(M_{\star}/M_{\odot})$. The recovered values are shown as the dark-blue lines, while the values used as inputs for the simulation are shown by the red lines.
• Figure 2: Same as Fig. \ref{['mock']} but for the mock MUSE cube of the simulated galaxy ID982.
• Figure 3: Left: Effective S/N for the brightest emission line using equation \ref{['s']}, as a function of the S/N of the total line flux from the integrated spectrum. Right: Ratio between stellar half-light radius and the PSF radius as a function of the S/N in the brightest spaxel. The grey shaded area in both panels marks the exclusion region, i,e, all galaxies with $\rm{S/N_{eff}<10}$ and $R_{\rm{e}}/R_{PSF}<0.5$ are removed from the sample. The data points are colour-coded according to their half-light radii. The circles, stars, diamonds and squares represent the [OII], $\rm{H\alpha}$, $\rm{H\beta}$ and [OIII] emitters, respectively.
• Figure 4: Left: Histogram showing the redshift distribution of the MHUDF SFGs. Middle: Histogram showing the stellar mass distribution of sample udf2. Right: Histogram showing the distribution of the Sérsic indices (morph).
• Figure 5: Stellar mass - SFR relation for the MHUDF sample. The data points are colour-coded according to their effective S/N (equation \ref{['s']}). The black line shows the star-forming main sequence at $z$=0.85 (the mean redshift of our sample), as derived by bog, whereas the grey shaded region shows the 0.44 dex scatter around the relation. The grey cross in the lower-right corner indicates the median $1\sigma$ statistical uncertainties in $M_{\star}$ and SFR from Mapgpys.