A New Census of the 0.2\textless z \textless3.0 Universe, Part II: The Star-Forming Sequence. Leja, J., Speagle, J. S., Ting, Y., Johnson, B. D., Conroy, C., Whitaker, K. E., Nelson, E. J., van Dokkum, P., & Franx, M. arXiv:2110.04314 [astro-ph], October, 2021. arXiv: 2110.04314![A New Census of the 0.2\textless z \textless3.0 Universe, Part II: The Star-Forming Sequence [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex We use the panchromatic SED-fitting code Prospector to measure the galaxy logM\${\textasciicircum}*\$-logSFR relationship (the `star-forming sequence') across \$0.2 {\textless} z {\textless} 3.0\$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at \${\textbackslash}sim 0.5\$ dex and \${\textbackslash}sim0.2\$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM\${\textasciicircum}*\$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization \$0.2-0.5\$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing \${\textbackslash}sim0.2-0.5\$ dex offset with observations at \$0.5{\textless}z{\textless}3\$. The fully trained normalizing flow including a nonparametric description of \${\textbackslash}rho({\textbackslash}log\{{\textbackslash}rm M\}{\textasciicircum}*,{\textbackslash}log\{{\textbackslash}rm SFR\},z)\$ is made available online to facilitate straightforward comparisons with future work.
@article{leja_new_2021,
title = {A {New} {Census} of the 0.2{\textless} z {\textless}3.0 {Universe}, {Part} {II}: {The} {Star}-{Forming} {Sequence}},
shorttitle = {A {New} {Census} of the 0.2{\textless} z {\textless}3.0 {Universe}, {Part} {II}},
url = {http://arxiv.org/abs/2110.04314},
abstract = {We use the panchromatic SED-fitting code Prospector to measure the galaxy logM\${\textasciicircum}*\$-logSFR relationship (the `star-forming sequence') across \$0.2 {\textless} z {\textless} 3.0\$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at \${\textbackslash}sim 0.5\$ dex and \${\textbackslash}sim0.2\$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM\${\textasciicircum}*\$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization \$0.2-0.5\$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing \${\textbackslash}sim0.2-0.5\$ dex offset with observations at \$0.5{\textless}z{\textless}3\$. The fully trained normalizing flow including a nonparametric description of \${\textbackslash}rho({\textbackslash}log\{{\textbackslash}rm M\}{\textasciicircum}*,{\textbackslash}log\{{\textbackslash}rm SFR\},z)\$ is made available online to facilitate straightforward comparisons with future work.},
urldate = {2021-10-25},
journal = {arXiv:2110.04314 [astro-ph]},
author = {Leja, Joel and Speagle, Joshua S. and Ting, Yuan-Sen and Johnson, Benjamin D. and Conroy, Charlie and Whitaker, Katherine E. and Nelson, Erica J. and van Dokkum, Pieter and Franx, Marijn},
month = oct,
year = {2021},
note = {arXiv: 2110.04314},
keywords = {Astrophysics - Astrophysics of Galaxies},
}
Downloads: 0
{"_id":"3kZrjGFdgKH3pzCwv","bibbaseid":"leja-speagle-ting-johnson-conroy-whitaker-nelson-vandokkum-etal-anewcensusofthe02textlessztextless30universepartiithestarformingsequence-2021","author_short":["Leja, J.","Speagle, J. S.","Ting, Y.","Johnson, B. D.","Conroy, C.","Whitaker, K. E.","Nelson, E. J.","van Dokkum, P.","Franx, M."],"bibdata":{"bibtype":"article","type":"article","title":"A New Census of the 0.2\\textless z \\textless3.0 Universe, Part II: The Star-Forming Sequence","shorttitle":"A New Census of the 0.2\\textless z \\textless3.0 Universe, Part II","url":"http://arxiv.org/abs/2110.04314","abstract":"We use the panchromatic SED-fitting code Prospector to measure the galaxy logM\\${\\textasciicircum}*\\$-logSFR relationship (the `star-forming sequence') across \\$0.2 {\\textless} z {\\textless} 3.0\\$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at \\${\\textbackslash}sim 0.5\\$ dex and \\${\\textbackslash}sim0.2\\$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM\\${\\textasciicircum}*\\$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization \\$0.2-0.5\\$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing \\${\\textbackslash}sim0.2-0.5\\$ dex offset with observations at \\$0.5{\\textless}z{\\textless}3\\$. The fully trained normalizing flow including a nonparametric description of \\${\\textbackslash}rho({\\textbackslash}log\\{{\\textbackslash}rm M\\}{\\textasciicircum}*,{\\textbackslash}log\\{{\\textbackslash}rm SFR\\},z)\\$ is made available online to facilitate straightforward comparisons with future work.","urldate":"2021-10-25","journal":"arXiv:2110.04314 [astro-ph]","author":[{"propositions":[],"lastnames":["Leja"],"firstnames":["Joel"],"suffixes":[]},{"propositions":[],"lastnames":["Speagle"],"firstnames":["Joshua","S."],"suffixes":[]},{"propositions":[],"lastnames":["Ting"],"firstnames":["Yuan-Sen"],"suffixes":[]},{"propositions":[],"lastnames":["Johnson"],"firstnames":["Benjamin","D."],"suffixes":[]},{"propositions":[],"lastnames":["Conroy"],"firstnames":["Charlie"],"suffixes":[]},{"propositions":[],"lastnames":["Whitaker"],"firstnames":["Katherine","E."],"suffixes":[]},{"propositions":[],"lastnames":["Nelson"],"firstnames":["Erica","J."],"suffixes":[]},{"propositions":["van"],"lastnames":["Dokkum"],"firstnames":["Pieter"],"suffixes":[]},{"propositions":[],"lastnames":["Franx"],"firstnames":["Marijn"],"suffixes":[]}],"month":"October","year":"2021","note":"arXiv: 2110.04314","keywords":"Astrophysics - Astrophysics of Galaxies","bibtex":"@article{leja_new_2021,\n\ttitle = {A {New} {Census} of the 0.2{\\textless} z {\\textless}3.0 {Universe}, {Part} {II}: {The} {Star}-{Forming} {Sequence}},\n\tshorttitle = {A {New} {Census} of the 0.2{\\textless} z {\\textless}3.0 {Universe}, {Part} {II}},\n\turl = {http://arxiv.org/abs/2110.04314},\n\tabstract = {We use the panchromatic SED-fitting code Prospector to measure the galaxy logM\\${\\textasciicircum}*\\$-logSFR relationship (the `star-forming sequence') across \\$0.2 {\\textless} z {\\textless} 3.0\\$ using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at \\${\\textbackslash}sim 0.5\\$ dex and \\${\\textbackslash}sim0.2\\$ dex respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM\\${\\textasciicircum}*\\$-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization \\$0.2-0.5\\$ dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing \\${\\textbackslash}sim0.2-0.5\\$ dex offset with observations at \\$0.5{\\textless}z{\\textless}3\\$. The fully trained normalizing flow including a nonparametric description of \\${\\textbackslash}rho({\\textbackslash}log\\{{\\textbackslash}rm M\\}{\\textasciicircum}*,{\\textbackslash}log\\{{\\textbackslash}rm SFR\\},z)\\$ is made available online to facilitate straightforward comparisons with future work.},\n\turldate = {2021-10-25},\n\tjournal = {arXiv:2110.04314 [astro-ph]},\n\tauthor = {Leja, Joel and Speagle, Joshua S. and Ting, Yuan-Sen and Johnson, Benjamin D. and Conroy, Charlie and Whitaker, Katherine E. and Nelson, Erica J. and van Dokkum, Pieter and Franx, Marijn},\n\tmonth = oct,\n\tyear = {2021},\n\tnote = {arXiv: 2110.04314},\n\tkeywords = {Astrophysics - Astrophysics of Galaxies},\n}\n\n","author_short":["Leja, J.","Speagle, J. S.","Ting, Y.","Johnson, B. D.","Conroy, C.","Whitaker, K. E.","Nelson, E. J.","van Dokkum, P.","Franx, M."],"key":"leja_new_2021","id":"leja_new_2021","bibbaseid":"leja-speagle-ting-johnson-conroy-whitaker-nelson-vandokkum-etal-anewcensusofthe02textlessztextless30universepartiithestarformingsequence-2021","role":"author","urls":{"Paper":"http://arxiv.org/abs/2110.04314"},"keyword":["Astrophysics - Astrophysics of Galaxies"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/polyphant","dataSources":["7gvjSdWrEu7z5vjjj"],"keywords":["astrophysics - astrophysics of galaxies"],"search_terms":["new","census","textless","textless3","universe","part","star","forming","sequence","leja","speagle","ting","johnson","conroy","whitaker","nelson","van dokkum","franx"],"title":"A New Census of the 0.2\\textless z \\textless3.0 Universe, Part II: The Star-Forming Sequence","year":2021}