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# Insights into the collapse and expansion of molecular clouds in outflows from observable pressure gradients

## Abstract

The jets launched by actively accreting black holes can generate massive outflows in galaxies, which could suppress or enhance star formation by rarefying or compressing clouds of molecular gas. To study the stability of such jet-impacted clouds, we performed astrochemical, thermally balanced, radiative transfer modelling of the CO and HCO+ emission of the galaxy IC 5063. We found that jet-related mechanical heating and cosmic rays contribute to the molecular gas heating rate and could even individually sustain it. Clouds excited by these mechanisms have temperatures and densities reflecting an order-of-magnitude increase in their internal pressure. Variations of their external pressure, deduced from [S ii] and [N ii] ionized gas emission, further reveal that some clouds are undergoing rarefaction and others compression. Our work shows a new viewpoint on plausible links between galactic outflows and star formation conditions: that of observable pressure gradients. It also emphasizes the role of cosmic rays in contributing to these gradients.

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## Data availability

All ALMA raw data can be retrieved from the ALMA archive using the project identifiers 2015.1.00420.S, 2015.1.00467.S, 2012.1.00435.S and 2016.1.01279.S. The line images and all other data presented in Fig. 1 can be obtained in .fits format from Figshare at https://doi.org/10.6084/m9.figshare.19742125. Fully reduced data cubes can be provided from the corresponding author upon reasonable request. The pipeline-processed MUSE data can be directly downloaded from the ESO data portal using the project identifier 60.A-9339.

## Code availability

The RT code 3D-PDR is a publicly available radiative transfer code that can be obtained at https://uclchem.github.io/3dpdr.

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## Acknowledgements

K.M.D., G.F.P. and T.G.B. acknowledge financial support by the Hellenic Foundation for Research and Innovation (HFRI), under the first call for the creation of research groups by postdoctoral researchers that was launched by the General Secretariat For Research and Innovation (project number 1882, PI K.M.D.). G.F.P. also acknowledges support for this research by the International Max-Planck Research School for Astronomy and Astrophysics at the University of Bonn and Cologne. T.G.B. further acknowledges support from Deutsche Forschungsgemeinschaft (DFG grant No. 424563772, T.G.B.). Financial support by the Spanish Ministry of Science and Innovation and the European Union—NextGenerationEU through the Recovery and Resilience Facility project ICTS-MRR-2021-03-CEFCA is acknowledged for J.A.F.-O. This paper makes use of the ALMA data with identifiers 2012.1.00435.S, 2015.1.00420.S, 2015.1.00467.S and 2016.1.01279.S. ALMA is a partnership of the ESO (representing its member states), National Science Foundation (United States) and National Institute of Natural Sciences (NINS; Japan), together with the National Research Council (NRC; Canada), National Science Council (NSC) and Academia Sinica Institute of Astronomy and Astrophysics (ASIAA; Taiwan), and Korea Astronomy and Space Science Institute (KASI; Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, Associated Universities Inc. (AUI)/National Radio Astronomy Observatory (NRAO) and National Astronomical Observatory of Japan (NAOJ).

## Author information

Authors

### Contributions

K.M.D. led the project, prepared the HFRI grant proposal, reduced the ALMA and SINFONI data and led their interpretation and write-up. G.F.P. wrote and ran the codes for the spatially resolved CO and HCO+ SLED modelling and benchmarked 3D-PDR and RADEX. T.G.B., an author of 3D-PDR, provided the appropriate grids following astrochemical and radiative transfer calculations for the different gas-excitation sources. F.C. led the ALMA proposal providing one of the datasets and contributed to the data interpretation. J.A.F.-O. assisted with the fitting of the optical data.

### Corresponding author

Correspondence to Kalliopi M. Dasyra.

## Ethics declarations

### Competing interests

The authors financed by HFRI grant 1882 (K.M.D., G.F.P., T.G.B.) for this project declare the existence of a financial/non-financial competing interest (radiative transfer modelling of some common datasets) with ERC grant 320745.

## Peer review

### Peer review information

Nature Astronomy thanks Roberto Maiolino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data

### Extended Data Fig. 1 SLED models.

Top left: CO SLED results for characteristic regions. The SLEDs are shown as velocity-integrated line fluxes as a function of the rotational number J of the upper energy level. Mechanical plus CR heating models are shown in purple, mechanical heating models are shown in orange, and CR heating models are shown in blue. Top right panels: $${{{{\rm{T}}}}}_{{{{\rm{kin}}}}}-{n}_{{{{{\rm{H}}}}}_{2}}$$ grid and results for each model. Bottom left: Corresponding Ntot and $${n}_{{{{{\rm{H}}}}}_{2}}$$ solutions plotted over the input grid $${N}_{{{{\rm{tot}}}}}-{n}_{{{{{\rm{H}}}}}_{2}}$$ projection. The cyan curve depicts a relation for stable clouds46 suggested by hydrodynamic simulations (see Methods for more information). Shaded areas indicate the areas occupied by virialized clouds (see equation (1)): the blue area is for Tkin=20 K and 0 < vturb < 1 km s−1, the purple area is for Tkin=100 K and 0 < vturb < 1 km s−1. Orange, blue, and purple points correspond to mechanical, CR, and combined heating model results, respectively. Other bottom panels: $${N}_{{{{\rm{CO}}}}}-{n}_{{{{{\rm{H}}}}}_{2}}$$ grid projection for all models. The initial grids and the grids trimmed for dynamical considerations are shown in light and dark grey, respectively in all pertinent panels.

### Extended Data Fig. 2 Spatially-resolved SLED fitting results for the mechanical heating model.

In this model, Γm is variable and ζCR=10−16 s−1. The quantities shown are the heating rate (total, mechanical, and FUV), the molecular gas kinetic temperature and volume density (top panels), and the cloud internal pressure, the CO column density along the line of sight, the CO beam filling factor, the CO(1-0) optical depth, and XCO (bottom panels).

### Extended Data Fig. 3 Spatially-resolved SLED fitting results for the CR heating model.

In this model, ζCR is variable and Γm=0. The quantities shown are similar to those in Extended Data Fig. 2.

### Extended Data Fig. 4 Predictions of the CO(7-6)/CI(2-1) flux ratio.

This ratio can be used as a diagnostic between mechanical and CR heating.

### Extended Data Fig. 5 Optical line diagnostics of ionized gas conditions.

[Fe II] and [S II] flux ratios used as density diagnostics (left and center) and [N II] flux ratio used as temperature diagnostic (right) as obtained by PyNeb38.

### Extended Data Fig. 6 Gas properties derived from NIR VLT SINFONI data.

Left: Temperature based on the H2 (1-0) S(1) and S(3) line fluxes. The highest Tkin values are close to regions of high turbulence perpendicular to the jet, associated with it9,66. Right: Density based on the [Fe II] 1.533 and 1.644 μm line fluxes.

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Dasyra, K.M., Paraschos, G.F., Bisbas, T.G. et al. Insights into the collapse and expansion of molecular clouds in outflows from observable pressure gradients. Nat Astron 6, 1077–1084 (2022). https://doi.org/10.1038/s41550-022-01725-9

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• DOI: https://doi.org/10.1038/s41550-022-01725-9