Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes
Galaxy formation and evolution involve a variety of effectively stochastic processes that operate over different timescales. The extended regulator model provides an analytic framework for the resulting variability (or “burstiness”) in galaxy-wide star formation due to these processes. It does this...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/acff64 |
| _version_ | 1797356571217362944 |
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| author | Kartheik G. Iyer Joshua S. Speagle Neven Caplar John C. Forbes Eric Gawiser Joel Leja Sandro Tacchella |
| author_facet | Kartheik G. Iyer Joshua S. Speagle Neven Caplar John C. Forbes Eric Gawiser Joel Leja Sandro Tacchella |
| author_sort | Kartheik G. Iyer |
| collection | DOAJ |
| description | Galaxy formation and evolution involve a variety of effectively stochastic processes that operate over different timescales. The extended regulator model provides an analytic framework for the resulting variability (or “burstiness”) in galaxy-wide star formation due to these processes. It does this by relating the variability in Fourier space to the effective timescales of stochastic gas inflow, equilibrium, and dynamical processes influencing giant molecular clouds' creation and destruction using the power spectral density (PSD) formalism. We use the connection between the PSD and autocovariance function for general stochastic processes to reformulate this model as an autocovariance function, which we use to model variability in galaxy star formation histories (SFHs) using physically motivated Gaussian processes in log star formation rate (SFR) space. Using stellar population synthesis models, we then explore how changes in model stochasticity can affect spectral signatures across galaxy populations with properties similar to the Milky Way and present-day dwarfs, as well as at higher redshifts. We find that, even at fixed scatter, perturbations to the stochasticity model (changing timescales vs. overall variability) leave unique spectral signatures across both idealized and more realistic galaxy populations. Distributions of spectral features including H α and UV-based SFR indicators, H δ and Ca H and K absorption-line strengths, D _n (4000), and broadband colors provide testable predictions for galaxy populations from present and upcoming surveys with the Hubble Space Telescope, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. The Gaussian process SFH framework provides a fast, flexible implementation of physical covariance models for the next generation of spectral energy distribution modeling tools. Code to reproduce our results can be found at https://github.com/kartheikiyer/GP-SFH . |
| first_indexed | 2024-03-08T14:29:37Z |
| format | Article |
| id | doaj.art-f241ed4620c24613b0e4edaab5ffd511 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-08T14:29:37Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-f241ed4620c24613b0e4edaab5ffd5112024-01-12T15:09:26ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-0196115310.3847/1538-4357/acff64Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian ProcessesKartheik G. Iyer0https://orcid.org/0000-0001-9298-3523Joshua S. Speagle1https://orcid.org/0000-0003-2573-9832Neven Caplar2https://orcid.org/0000-0003-3287-5250John C. Forbes3https://orcid.org/0000-0002-1975-4449Eric Gawiser4https://orcid.org/0000-0003-1530-8713Joel Leja5https://orcid.org/0000-0001-6755-1315Sandro Tacchella6https://orcid.org/0000-0002-8224-4505Columbia Astrophysics Laboratory, Columbia University , 550 West 120th Street, New York, NY 10027, USA ; kgi2103@columbia.edu; Dunlap Institute for Astronomy & Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON M5S 3H4, CanadaDunlap Institute for Astronomy & Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON M5S 3H4, Canada; Department of Statistical Sciences, University of Toronto , 9th Floor, Ontario Power Building, 700 University Avenue, Toronto, ON M5G 1Z5, CA, Canada ; j.speagle@utoronto.ca; David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto , 50 St. George Street, Toronto ON M5S 3H4, CA, Canada; Data Sciences Institute, University of Toronto , 17th Floor, Ontario Power Building, 700 University Avenue, Toronto, ON M5G 1Z5, CA, CanadaDepartment of Astrophysical Sciences, Princeton University , 4 Ivy Lane, Princeton, NJ 08544, USA; Department of Astronomy and the DiRAC Institute, University of Washington , 3910 15th Avenue NE, Seattle, WA 98195, USACenter for Computational Astrophysics, Flatiron Institute , 162 Fifth Avenue, New York, NY 10010, USADepartment of Physics and Astronomy, Rutgers, The State University of New Jersey , Piscataway, NJ 08854, USADepartment of Astronomy & Astrophysics, The Pennsylvania State University , University Park, PA 16802, USA; Institute for Computational & Data Sciences, The Pennsylvania State University , University Park, PA, USA; Institute for Gravitation and the Cosmos, The Pennsylvania State University , University Park, PA 16802, USAKavli Institute for Cosmology, University of Cambridge , Madingley Road, Cambridge CB3 0HA, UK; Cavendish Laboratory, University of Cambridge , 19 J J Thomson Avenue, Cambridge CB3 0HE, UKGalaxy formation and evolution involve a variety of effectively stochastic processes that operate over different timescales. The extended regulator model provides an analytic framework for the resulting variability (or “burstiness”) in galaxy-wide star formation due to these processes. It does this by relating the variability in Fourier space to the effective timescales of stochastic gas inflow, equilibrium, and dynamical processes influencing giant molecular clouds' creation and destruction using the power spectral density (PSD) formalism. We use the connection between the PSD and autocovariance function for general stochastic processes to reformulate this model as an autocovariance function, which we use to model variability in galaxy star formation histories (SFHs) using physically motivated Gaussian processes in log star formation rate (SFR) space. Using stellar population synthesis models, we then explore how changes in model stochasticity can affect spectral signatures across galaxy populations with properties similar to the Milky Way and present-day dwarfs, as well as at higher redshifts. We find that, even at fixed scatter, perturbations to the stochasticity model (changing timescales vs. overall variability) leave unique spectral signatures across both idealized and more realistic galaxy populations. Distributions of spectral features including H α and UV-based SFR indicators, H δ and Ca H and K absorption-line strengths, D _n (4000), and broadband colors provide testable predictions for galaxy populations from present and upcoming surveys with the Hubble Space Telescope, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. The Gaussian process SFH framework provides a fast, flexible implementation of physical covariance models for the next generation of spectral energy distribution modeling tools. Code to reproduce our results can be found at https://github.com/kartheikiyer/GP-SFH .https://doi.org/10.3847/1538-4357/acff64Galaxy evolutionGalaxy processesSpectral energy distributionComputational methodsAstrostatistics techniques |
| spellingShingle | Kartheik G. Iyer Joshua S. Speagle Neven Caplar John C. Forbes Eric Gawiser Joel Leja Sandro Tacchella Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes The Astrophysical Journal Galaxy evolution Galaxy processes Spectral energy distribution Computational methods Astrostatistics techniques |
| title | Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes |
| title_full | Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes |
| title_fullStr | Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes |
| title_full_unstemmed | Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes |
| title_short | Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes |
| title_sort | stochastic modeling of star formation histories iii constraints from physically motivated gaussian processes |
| topic | Galaxy evolution Galaxy processes Spectral energy distribution Computational methods Astrostatistics techniques |
| url | https://doi.org/10.3847/1538-4357/acff64 |
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