Stochastic prior for non-parametric star-formation histories
ABSTRACT The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance o...
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Veröffentlicht in: | Monthly notices of the Royal Astronomical Society 2024-08, Vol.532 (4), p.4002-4025 |
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creator | Wan, Jenny T Tacchella, Sandro Johnson, Benjamin D Iyer, Kartheik G Maiolino, Roberto |
description | ABSTRACT
The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, $z = 0.7$ galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) $z = 8$ galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is $\gtrsim 2\times$ more accurate than the continuity prior in measuring the recent star-formation rates (log SFR$_{100}$ and log SFR$_{10}$) of both the $z = 0.7$ and $z = 8$ mock systems. While the PSD parameters of individual galaxies are difficult to constrain, the stochastic prior implementation presented in this work allows for the development of hierarchical models in the future, i.e. simultaneous SED-modelling of an ensemble of galaxies to measure their underlying PSD. |
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The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, $z = 0.7$ galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) $z = 8$ galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is $\gtrsim 2\times$ more accurate than the continuity prior in measuring the recent star-formation rates (log SFR$_{100}$ and log SFR$_{10}$) of both the $z = 0.7$ and $z = 8$ mock systems. While the PSD parameters of individual galaxies are difficult to constrain, the stochastic prior implementation presented in this work allows for the development of hierarchical models in the future, i.e. simultaneous SED-modelling of an ensemble of galaxies to measure their underlying PSD.</description><identifier>ISSN: 0035-8711</identifier><identifier>EISSN: 1365-2966</identifier><identifier>DOI: 10.1093/mnras/stae1734</identifier><language>eng</language><publisher>London: Oxford University Press</publisher><subject>Astronomical models ; Galaxy distribution ; Metallicity ; Parameters ; Photometry ; Power spectral density ; Spectral energy distribution ; Star & galaxy formation ; Star formation ; Stars & galaxies ; Stellar mass ; Stochastic processes</subject><ispartof>Monthly notices of the Royal Astronomical Society, 2024-08, Vol.532 (4), p.4002-4025</ispartof><rights>2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. 2024</rights><rights>2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c186t-cfcbdaf2adb06b27ccf62d4971db8ff471274713d85527664e85d71d30cad5f23</cites><orcidid>0000-0001-9298-3523 ; 0000-0002-8224-4505 ; 0000-0001-8872-4991</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,782,786,866,1586,1606,27931,27932</link.rule.ids></links><search><creatorcontrib>Wan, Jenny T</creatorcontrib><creatorcontrib>Tacchella, Sandro</creatorcontrib><creatorcontrib>Johnson, Benjamin D</creatorcontrib><creatorcontrib>Iyer, Kartheik G</creatorcontrib><creatorcontrib>Maiolino, Roberto</creatorcontrib><title>Stochastic prior for non-parametric star-formation histories</title><title>Monthly notices of the Royal Astronomical Society</title><description>ABSTRACT
The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, $z = 0.7$ galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) $z = 8$ galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is $\gtrsim 2\times$ more accurate than the continuity prior in measuring the recent star-formation rates (log SFR$_{100}$ and log SFR$_{10}$) of both the $z = 0.7$ and $z = 8$ mock systems. 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The amount of power contained in the variations in galaxy star-formation histories (SFHs) across a range of time-scales encodes key information about the physical processes which modulate star formation. Modelling the SFHs of galaxies as stochastic processes allows the relative importance of different time-scales to be quantified via the power spectral density (PSD). In this paper, we build upon the PSD framework and develop a physically motivated, ‘stochastic’ prior for non-parametric SFHs in the spectral energy distribution (SED)-modelling code prospector. We test this prior in two different regimes: (1) massive, $z = 0.7$ galaxies with both photometry and spectra, analogous to those observed with the LEGA-C survey, and (2) $z = 8$ galaxies with photometry only, analogous to those observed with NIRCam on JWST. We find that it is able to recover key galaxy parameters (e.g. stellar mass, stellar metallicity) to the same level of fidelity as the commonly used continuity prior. Furthermore, the realistic variability information incorporated by the stochastic SFH model allows it to fit the SFHs of galaxies more accurately and precisely than traditional non-parametric models. In fact, the stochastic prior is $\gtrsim 2\times$ more accurate than the continuity prior in measuring the recent star-formation rates (log SFR$_{100}$ and log SFR$_{10}$) of both the $z = 0.7$ and $z = 8$ mock systems. While the PSD parameters of individual galaxies are difficult to constrain, the stochastic prior implementation presented in this work allows for the development of hierarchical models in the future, i.e. simultaneous SED-modelling of an ensemble of galaxies to measure their underlying PSD.</abstract><cop>London</cop><pub>Oxford University Press</pub><doi>10.1093/mnras/stae1734</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0001-9298-3523</orcidid><orcidid>https://orcid.org/0000-0002-8224-4505</orcidid><orcidid>https://orcid.org/0000-0001-8872-4991</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Astronomical models Galaxy distribution Metallicity Parameters Photometry Power spectral density Spectral energy distribution Star & galaxy formation Star formation Stars & galaxies Stellar mass Stochastic processes |
title | Stochastic prior for non-parametric star-formation histories |
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