@misc{williams2022baryonic,
  abstract = {Where the cosmic baryons lie in and around galactic dark matter halos is only
weakly constrained. We develop a method to quickly paint on models for their
distribution. Our approach uses the statistical advantages of $N$-body
simulations, while painting on the profile of gas around individual halos in
ways that can be motivated by semi-analytic models or zoom-in hydrodynamic
simulations of galaxies. Possible applications of the algorithm include
extragalactic dispersion measures to fast radio bursts (FRBs), the
Sunyaev-Zeldovich effect, baryonic effects on weak lensing, and cosmic metal
enrichment. As an initial application, we use this tool to investigate how the
baryonic profile of foreground galactic-mass halos affects the statistics of
the dispersion measure (DM) towards cosmological FRBs.
  We show that the distribution of DM is sensitive to the distribution of
baryons in galactic halos, with viable gas profile models having significantly
different probability distributions for DM to a given redshift. We also
investigate the requirements to statistically measure the circumgalactic
electron profile for FRB analyses that stack DM with impact parameter to
foreground galaxies, quantifying the size of the contaminating "two-halo" term
from correlated systems and the number of FRBs for a high significance
detection. Publicly available Python modules implement our CGMBrush algorithm.},
  added-at = {2022-07-13T08:48:37.000+0200},
  author = {Williams, Ian and Khan, Adnan and McQuinn, Matthew},
  biburl = {https://www.bibsonomy.org/bibtex/238aa1f8f3803698f124b37733901469d/gpkulkarni},
  description = {Baryonic Post-Processing of N-body Simulations, with Application to Fast Radio Bursts},
  interhash = {7070ae1b3e746eda595dc9359b714e01},
  intrahash = {38aa1f8f3803698f124b37733901469d},
  keywords = {library},
  note = {cite arxiv:2207.05233Comment: 23 pages, 10 figures, public python code at  https://github.com/ianw89/cgm-brush},
  timestamp = {2022-07-13T08:48:37.000+0200},
  title = {Baryonic Post-Processing of N-body Simulations, with Application to Fast
  Radio Bursts},
  url = {http://arxiv.org/abs/2207.05233},
  year = 2022
}

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As an initial application, we use this tool to investigate how the baryonic profile of foreground galactic-mass halos affects the statistics of the dispersion measure (DM) towards cosmological FRBs. We show that the distribution of DM is sensitive to the distribution of baryons in galactic halos, with viable gas profile models having significantly different probability distributions for DM to a given redshift. We also investigate the requirements to statistically measure the circumgalactic electron profile for FRB analyses that stack DM with impact parameter to foreground galaxies, quantifying the size of the contaminating \"two-halo\" term from correlated systems and the number of FRBs for a high significance detection. Publicly available Python modules implement our CGMBrush algorithm.","added-at":"2022-07-13T08:48:37.000+0200","author":[{"propositions":[],"lastnames":["Williams"],"firstnames":["Ian"],"suffixes":[]},{"propositions":[],"lastnames":["Khan"],"firstnames":["Adnan"],"suffixes":[]},{"propositions":[],"lastnames":["McQuinn"],"firstnames":["Matthew"],"suffixes":[]}],"biburl":"https://www.bibsonomy.org/bibtex/238aa1f8f3803698f124b37733901469d/gpkulkarni","description":"Baryonic Post-Processing of N-body Simulations, with Application to Fast Radio Bursts","interhash":"7070ae1b3e746eda595dc9359b714e01","intrahash":"38aa1f8f3803698f124b37733901469d","keywords":"library","note":"cite arxiv:2207.05233Comment: 23 pages, 10 figures, public python code at https://github.com/ianw89/cgm-brush","timestamp":"2022-07-13T08:48:37.000+0200","title":"Baryonic Post-Processing of N-body Simulations, with Application to Fast Radio Bursts","url":"http://arxiv.org/abs/2207.05233","year":"2022","bibtex":"@misc{williams2022baryonic,\n abstract = {Where the cosmic baryons lie in and around galactic dark matter halos is only\r\nweakly constrained. We develop a method to quickly paint on models for their\r\ndistribution. Our approach uses the statistical advantages of $N$-body\r\nsimulations, while painting on the profile of gas around individual halos in\r\nways that can be motivated by semi-analytic models or zoom-in hydrodynamic\r\nsimulations of galaxies. Possible applications of the algorithm include\r\nextragalactic dispersion measures to fast radio bursts (FRBs), the\r\nSunyaev-Zeldovich effect, baryonic effects on weak lensing, and cosmic metal\r\nenrichment. As an initial application, we use this tool to investigate how the\r\nbaryonic profile of foreground galactic-mass halos affects the statistics of\r\nthe dispersion measure (DM) towards cosmological FRBs.\r\n We show that the distribution of DM is sensitive to the distribution of\r\nbaryons in galactic halos, with viable gas profile models having significantly\r\ndifferent probability distributions for DM to a given redshift. We also\r\ninvestigate the requirements to statistically measure the circumgalactic\r\nelectron profile for FRB analyses that stack DM with impact parameter to\r\nforeground galaxies, quantifying the size of the contaminating \"two-halo\" term\r\nfrom correlated systems and the number of FRBs for a high significance\r\ndetection. Publicly available Python modules implement our CGMBrush algorithm.},\n added-at = {2022-07-13T08:48:37.000+0200},\n author = {Williams, Ian and Khan, Adnan and McQuinn, Matthew},\n biburl = {https://www.bibsonomy.org/bibtex/238aa1f8f3803698f124b37733901469d/gpkulkarni},\n description = {Baryonic Post-Processing of N-body Simulations, with Application to Fast Radio Bursts},\n interhash = {7070ae1b3e746eda595dc9359b714e01},\n intrahash = {38aa1f8f3803698f124b37733901469d},\n keywords = {library},\n note = {cite arxiv:2207.05233Comment: 23 pages, 10 figures, public python code at https://github.com/ianw89/cgm-brush},\n timestamp = {2022-07-13T08:48:37.000+0200},\n title = {Baryonic Post-Processing of N-body Simulations, with Application to Fast\r\n Radio Bursts},\n url = {http://arxiv.org/abs/2207.05233},\n year = 2022\n}\n\n","author_short":["Williams, I.","Khan, A.","McQuinn, M."],"key":"williams2022baryonic","id":"williams2022baryonic","bibbaseid":"williams-khan-mcquinn-baryonicpostprocessingofnbodysimulationswithapplicationtofastradiobursts-2022","role":"author","urls":{"Paper":"http://arxiv.org/abs/2207.05233"},"keyword":["library"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"misc","biburl":"http://www.bibsonomy.org/bib/author/williams?items=1000","dataSources":["9yEa5Kjt9P75Q7HKD"],"keywords":["library"],"search_terms":["baryonic","post","processing","body","simulations","application","fast","radio","bursts","williams","khan","mcquinn"],"title":"Baryonic Post-Processing of N-body Simulations, with Application to Fast Radio Bursts","year":2022}