Hi! I am currently trying to compare real (quiescent) galaxy morphologies up to z = 2.5 to tng50 galaxies (using a cut for quiescent objects of log sSFR<-11) to see if tng shows any evidence for diskier (low sersic index) galaxies at low stellar masses (>10^8) and what this can tell us about quenching processes. I was wondering what the best parameter(s) from the catalogs would be to use to compare to sersic index? Would appreciate any suggestions!
Dylan Nelson
15 Jul
It would be best to derive sersic index for the simulated galaxies from 2D stellar light maps, to be somewhat similar to the measurements from data. There are a few supplementary data catalogs which might cover TNG50, but possibly not at (all the) redshifts you are interested in.
Massissilia Hamadouche
15 Jul
Hi Dylan, thank you for the reply! I did have a look at some of the supplementary data catalogs (e.g. the deep learn morphologies catalog for tng50) which does cover the redshift range I'm looking for, though I was wondering how complicated it would be if I did derive my own measurements for better comparison to my observational data. How would I obtain realistic stellar light maps to run Galfit on (I derived measurements for my current data using the JWST f356w images)? Also, would there be a way to determine the effect of environment on the galaxies, particularly at the low-mass end? I just wanted to see if there was any evidence in simulations that the quiescent galaxy size-mass relation at the lower stellar masses can actually be explained by environmental quenching.
Dylan Nelson
15 Jul
There are two options to generate f356w maps of the TNG50 galaxies you are interested in: (i) you could run a code like SKIRT, which is a fair bit of work to get set up, but then gives you the option of incorporating some sophisticated physics like dust attenuation, or (ii) you could manually compute the luminosities of each stellar particle, using e.g. FSPS, and then do a 2d histogram over the star positions to make an image.
After you run such images through galfit or statmorph, you can definitely connect to environment, since you can measure/define a number of environmental metrics for each galaxy of interest.
Hi! I am currently trying to compare real (quiescent) galaxy morphologies up to z = 2.5 to tng50 galaxies (using a cut for quiescent objects of log sSFR<-11) to see if tng shows any evidence for diskier (low sersic index) galaxies at low stellar masses (>10^8) and what this can tell us about quenching processes. I was wondering what the best parameter(s) from the catalogs would be to use to compare to sersic index? Would appreciate any suggestions!
It would be best to derive sersic index for the simulated galaxies from 2D stellar light maps, to be somewhat similar to the measurements from data. There are a few supplementary data catalogs which might cover TNG50, but possibly not at (all the) redshifts you are interested in.
Hi Dylan, thank you for the reply! I did have a look at some of the supplementary data catalogs (e.g. the deep learn morphologies catalog for tng50) which does cover the redshift range I'm looking for, though I was wondering how complicated it would be if I did derive my own measurements for better comparison to my observational data. How would I obtain realistic stellar light maps to run Galfit on (I derived measurements for my current data using the JWST f356w images)? Also, would there be a way to determine the effect of environment on the galaxies, particularly at the low-mass end? I just wanted to see if there was any evidence in simulations that the quiescent galaxy size-mass relation at the lower stellar masses can actually be explained by environmental quenching.
There are two options to generate f356w maps of the TNG50 galaxies you are interested in: (i) you could run a code like SKIRT, which is a fair bit of work to get set up, but then gives you the option of incorporating some sophisticated physics like dust attenuation, or (ii) you could manually compute the luminosities of each stellar particle, using e.g. FSPS, and then do a 2d histogram over the star positions to make an image.
The second option is essentially what the Visualize Tool (and API function) are doing.
After you run such images through galfit or statmorph, you can definitely connect to environment, since you can measure/define a number of environmental metrics for each galaxy of interest.