PRUSSIC II -- ALMA imaging of dense-gas tracers in SDP.81: Evidence for low mechanical heating and a sub-solar metallicity in a z=3.04 dusty galaxy

Authors: M. Rybak, J. van Marrewijk, J. A. Hodge, P. Andreani, G. Calistro Rivera, L. Graziani, J. P. McKean, S. Viti, P. P. van der Werf

arXiv: 2308.02886v1 - DOI (astro-ph.GA)
Submitted to A&A. 10 pages, 10 figures
License: CC BY 4.0

Abstract: We present deep ALMA Band 3 observations of the HCN, HCO+, and HNC (4-3) emission in SDP.81, a well-studied z = 3.042 strongly lensed galaxy. These lines trace the high-density gas, which remains almost entirely unexplored in z$\geq$1 galaxies. Additionally, these dense-gas tracers are potentially powerful diagnostics of the mechanical heating of the interstellar medium. While the HCN(4-3) and HNC(4-3) lines are not detected, the HCO+(4-3) emission is clearly detected and resolved. This is the third detection of this line in a high-redshift star-forming galaxy. We find an unusually high HCO+/HCN intensity ratio of $\geq$2.2. Based on the photodissociation region modelling, the most likely explanation for the elevated HCO+/HCN ratio is that SDP.81 has low mechanical heating - less than 10% of the total energy budget - and a sub-solar metallicity, Z=0.5 Z$_\odot$. While such conditions might not be representative of the general population of high-redshift dusty galaxies, lower-than-solar metallicity might have a significant impact on gas masses inferred from CO observations. In addition, we report the detection of CO(0-1) absorption from the foreground lensing galaxy and CO(1-0) emission from a massive companion to the lensing galaxy, approximately 50 kpc to the southeast.

Submitted to arXiv on 05 Aug. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.