Astrophysics of Galaxies (astro-ph.GA)

Abstract: We present maps and spectra of the HCN(1-0) and HCO$^+$(1-0) lines in the extreme outer Galaxy, at galactocentric radii between 14 and 22 kpc, with the 13.7 meter Delingha telescope. The 9 molecular clouds were selected from a CO/$^{13}$CO survey of the outer quadrants. The goal is to better understand the structure of molecular clouds in these poorly studied subsolar metallicity regions and the relation with star formation. The lines are all narrow, less than 2km/s at half power, enabling detection of the HCN hyperfine structure in the stronger sources and allowing us to observationally test hyperfine collision rates. The hyperfine line ratios show that the HCN emission is optically thin with column densities estimated at N(HCN)~$3x10^{12}$\scm. The HCO$^+$ emission is approximately twice as strong as the HCN (taken as the sum of all components), in contrast with the inner Galaxy and nearby galaxies where they are similarly strong. For an abundance ratio $\chi_{HCN}/\chi_{HCO^+} = 3$, this requires a relatively low density solution for the dense gas, with n(H2) $\sim 10^3 - 10^4$\ccm. The $^{12}$CO/$^{13}$CO line ratios are similar to solar neighborhood values, roughly 7.5, despite the low $^{13}$CO abundance expected at such large radii. The HCO$^+$/CO and HCO$^+$/$^{13}$CO integrated intensity ratios are also standard at about 1/35 and 1/5 respectively. HCN is weak compared to the CO emission, with HCN/CO $\sim 1/70$ even after summing all hyperfine components. At the parsec scales observed here, the correlation between star formation, as traced by 24~$\mu$m emission as is standard in extragalactic work, and dense gas via the HCN or HCO$^+$ emission, is poor, perhaps due to the lack of dynamic range. We find that the lowest dense gas fractions are in the sources at high galactic latitude (b>2, h>300pc above the plane), possibly due to lower pressure.

Abstract: Extragalactic radio continuum surveys play an increasingly more important role in galaxy evolution and cosmology studies. While radio galaxies and radio quasars dominate at the bright end, star-forming galaxies (SFGs) and radio-quiet Active Galactic Nuclei (AGNs) are more common at fainter flux densities. Our aim is to develop a machine learning classifier that can efficiently and reliably separate AGNs and SFGs in radio continuum surveys. We perform supervised classification of SFGs vs AGNs using the Light Gradient Boosting Machine (LGBM) on three LOFAR Deep Fields (Lockman Hole, Bootes and ELAIS-N1), which benefit from a wide range of high-quality multi-wavelength data and classification labels derived from extensive spectral energy distribution (SED) analyses. Our trained model has a precision of 0.92(0.01) and a recall of 0.87(0.02) for SFGs. For AGNs, the model has slightly worse performance, with a precision of 0.87(0.02) and recall of 0.78(0.02). These results demonstrate that our trained model can successfully reproduce the classification labels derived from detailed SED analysis. The model performance decreases towards higher redshifts, mainly due to smaller training sample sizes. To make the classifier more adaptable to other radio galaxy surveys, we also investigate how our classifier performs with a poorer multi-wavelength sampling of the SED. In particular, we find that the far-infrared (FIR) and radio bands are of great importance. We also find that higher S/N in some photometric bands leads to a significant boost in the model's performance. In addition to using the 150 MHz radio data, our model can also be used with 1.4 GHz radio data. Converting 1.4 GHz to 150 MHz radio data reduces performance by about 4% in precision and 3% in recall. The final trained model is publicly available at https://github.com/Jesper-Karsten/MBASC

Abstract: We report the first high-sensitivity HI observation toward the spiral galaxy M94 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). From these observations, we discovered that M94 has a very extended HI disk, twice larger than that observed by THINGS, which is accompanied by an HI filament and seven HVCs (high velocity clouds) at different distances. The projected distances of these clouds and filament are less than 50 kpc from the galactic center. We measured a total integrated flux (including all clouds/filament) of 127.3 ($\pm$1) Jy km s$^{-1}$, corresponding to a H I mass of (6.51$\pm$0.06)$\times$10$^{8}$M$_{\odot}$, which is 63.0% more than that observed by THINGS. By comparing numerical simulations with the HI maps and the optical morphology of M94, we suggest that M94 is likely a remnant of a major merger of two galaxies, and the HVCs and HI filament could be the tidal features originated from the first collision of the merger happened about 5 Gyr ago. Furthermore, we found a seemingly isolated HI cloud at a projection distance of 109 kpc without any optical counterpart detected. We discussed the possibilities of the origin of this cloud, such as dark dwarf galaxy and RELHIC (REionization-Limited HI Cloud). Our results demonstrate that high-sensitivity and wide-field HI imaging is important in revealing the diffuse cold gas structures and tidal debris which is crucial to understanding the dynamical evolution of galaxies.

Abstract: Gaia DR3 has provided the community with about one million RVS spectra covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200M spectra. Thus, stellar spectra are produced on an "industrial scale" with numbers well above those for current and anticipated ground based surveys. However, many of these spectra have low S/N (from 15 to 25 per pixel), such that they pose problems for classical spectral analysis pipelines and therefore alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility/capabilities of machine learning techniques for supercharged stellar parametrization, by combining Gaia RVS spectra with the full set of Gaia products and high-resolution, high-quality spectroscopic reference data sets. We develop a hybrid Convolutional Neural-Network (CNN) which combines the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g), and overall [M/H]) and chemical abundances ([Fe/H] and [$\alpha$/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 841300 stars, that remarkably compared to external data-sets. The CNN is robust against noise in the RVS data, and very precise labels are derived down to S/N=15. We managed to characterize the [$\alpha$/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine-learning with such diverse datasets (spectroscopy, astrometry, and photometry), and paves the way for the large scale machine-learning analysis of Gaia-RVS spectra from future data releases.

Abstract: We investigate the correlation between OH and H2 column densities in diffuse Galactic clouds, in order to identify potential molecular tracers of interstellar H2. For this, we analyse near-UV spectra extracted from the ESO/VLT archives towards seventeen sightlines (five of them new) with known N(H2), along with nine sightlines with no H2 information. N(OH) shows only marginal correlation with N(H2) (10$^{20}$ to 2 x 10$^{21}$ cm$^{-2}$), at the 95 per cent confidence level. We use orthogonal distance regression analysis to obtain N(OH)/N(H2) = (1.32+/-0.15) x 10$^{-7}$, which is ~ 33 per cent higher than the previous estimates based on near-UV data. We also obtain N(CH)/N(H2) = (3.83+/-0.23) x 10$^{-8}$ and a significant correlation between N(OH) and N(CH), with N(OH) = (2.61+/-0.19) x N(CH), both of which are consistent with previous results. Comparison with predictions of numerical models indicate that OH absorption arises from diffuse gas (nH ~ 50 cm$^{-3}$) illuminated by radiation fields ~ 0.5-5 G0, while CH is associated with higher density of 500 cm$^{-3}$. We posit that the apparent dichotomy in the properties of the diffuse clouds giving rise to OH and CH absorption could be due to either (a) the presence of multiple spectroscopically unresolved clouds along the line-of-sight, or, (b) density gradients along the line-of-sight within a single cloud.

Abstract: Reionization is thought to be driven by faint star-forming galaxies, but characterizing this population in detail has long remained very challenging. Here we utilize deep nine-band NIRCam imaging from JADES to study the star-forming and ionizing properties of 756 $z\sim6-9$ galaxies, including hundreds of very UV-faint objects ($M_\mathrm{UV}>-18$). The faintest ($m\sim30$) galaxies in our sample typically have stellar masses of $M_\ast\sim(1-3)\times10^7$ $M_\odot$ and young light-weighted ages ($\sim$50 Myr), though some show strong Balmer breaks implying much older ages ($\sim$500 Myr). We find no evidence for extremely massive galaxies ($>3\times10^{10}$ $M_\odot$) in our sample. We infer a strong (factor $>$2) decline in the typical [OIII]$+$H$\beta$ EWs towards very faint $z\sim6-9$ galaxies, yet a weak UV luminosity dependence on the H$\alpha$ EWs at $z\sim6$. We demonstrate that these EW trends can be explained if fainter galaxies have systematically lower metallicities as well as more recently-declining star formation histories relative to the most UV-luminous galaxies in our sample. Our data provide evidence that the brightest galaxies are frequently experiencing a recent strong upturn in SFR. We also discuss how the EW trends may be influenced by a strong correlation between $M_\mathrm{UV}$ and Lyman continuum escape fraction. This alternative explanation has dramatically different implications for the contribution of galaxies along the luminosity function to cosmic reionization, highlighting the need for deep spectroscopic follow-up. Finally, we quantify the photometric overdensities around two $z>7$ strong Ly$\alpha$ emitters in the JADES footprint. One Ly$\alpha$ emitter lies close to a strong photometric overdensity while the other shows no significant nearby overdensity, perhaps implying that not all strong $z>7$ Ly$\alpha$ emitters reside in large ionized bubbles.

Abstract: The repeating fast radio burst FRB20190520B is an anomaly of the FRB population thanks to its high dispersion measure (DM$=1205\,pc\,cm^{-3}$) despite its low redshift of $z_\mathrm{frb}=0.241$. This excess has been attributed to a host contribution of ${DM_{host}} \approx 900\,\mathrm{pc\,cm^{-3}}$, far larger than any other known FRB. In this paper, we describe spectroscopic observations of the FRB20190520B field obtained as part of the FLIMFLAM survey on the 2dF/AAOmega facility, which yielded 701 galaxies redshifts in a field of $\approx 3\,\mathrm{deg}^2$. Applying a friends-of-friends group finder reveals multiple galaxy groups and clusters, for which we then estimated halo masses by comparing their richness with forward-modeled mocks from numerical simulations. We discover two separate $M_\mathrm{halo} >10^{14}\,M_\odot$ galaxy clusters, at $z=0.1867$ and $z=0.2170$, respectively, that are directly intersected by the FRB sightline within their characteristic radius $r_{200}$. Subtracting off their estimated DM contributions as well that of the diffuse intergalactic medium, we estimate a host contribution of $DM_{host}=467^{+140}_{-230}\,\mathrm{pc\,cm^{-3}}$ or ${DM_{host}} = 339^{+122}_{-174}\,\mathrm{pc\,cm^{-3}}$ (observed frame) depending on whether we assume the halo gas extends to $r_{200}$ or $2\times r_{200}$. This significantly smaller $DM_{host}$ -- no longer the largest known value -- is now consistent with H$\alpha$ emission measure estimates of the host galaxy without having to invoke unusually high gas temperatures. We also re-estimate the turbulent fluctuation and geometric amplification factor of the scattering layer to be $FG \approx 3.9 - 7.5\,(\mathrm{pc^2\;km})^{-1/3}$. This result illustrates the importance of incorporating foreground data for FRB analyses, both for understanding the nature of FRBs and to realize their potential as a cosmological probe.