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Going beyond galaxy ages with dense basis star formation history reconstruction

Published online by Cambridge University Press:  10 June 2020

Kartheik G. Iyer
Affiliation:
Department of Physics and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen Road, Piscataway - 08854, New Jersey, USA Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St George St, Toronto, ON M5S 3H4, Canada email: kartheik.iyer@dunlap.utoronto.ca
Eric Gawiser
Affiliation:
Department of Physics and Astronomy, Rutgers, the State University of New Jersey, 136 Frelinghuysen Road, Piscataway - 08854, New Jersey, USA
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Abstract

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Panchromatic SED fitting allows us to better resolve degeneracies between quantities like the star formation rate and dust. This in turn allows us to more robustly extract information about the different stellar populations that comprise a galaxy’s Star Formation History (SFH). Using the Dense Basis SED fitting method (Iyer & Gawiser 2017), we reconstruct the SFHs with uncertainties for a large sample of galaxies using an atlas of SEDs corresponding to a physically motivated basis of SFHs. Using Gaussian Process Regression, we encode the parameters describing these SFHs in a functionally independent form. This give us more robust estimates for quantities like Stellar Masses and Star Formation Rates, that directly depend on the SFH. These SFHs can additionally be used to answer questions like the time at which a galaxy’s star formation peaked, and how many major episodes of star formation occurred in a galaxy’s past, allowing us to go beyond the traditionally estimated ‘Galaxy Age’, which is often poorly constrained. They also allow us to probe the high-redshift low-stellar mass regime of the SFR-M* correlation by constructing trajectories in SFR-M* space for each galaxy.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Iyer, Kartheik & Eric, Gawiser 2017, ApJ, 838.2 (2017): 127CrossRefGoogle Scholar
Iyer, Kartheik, et al. 2018, ApJ 866.2 (2018): 120CrossRefGoogle Scholar
Iyer, K., Gawiser, E., et al. 2019, arXiv preprint, https://iopscience.iop.org/article/10.3847/1538-4357/ab2052Google Scholar
Somerville, R. S., Popping, G. & Trager, S. C. 2015, MNRAS, 453(4), 4337436710.1093/mnras/stv1877CrossRefGoogle Scholar
Rasmussen, C. E., & Williams, C. K. 2006, Gaussian process for machine learning. MIT pressCrossRefGoogle Scholar
Pacifici, C. 2019, in prep.Google Scholar
Grogin, Norman A., et al. 2011, ApJS, 197.2 (2011): 3510.1088/0067-0049/197/2/35CrossRefGoogle Scholar
Koekemoer, Anton M., et al. 2011, ApJS, 197.2 (2011): 36CrossRefGoogle Scholar
Santini, P., et al. 2015, ApJ, 801.2 (2015): 9710.1088/0004-637X/801/2/97CrossRefGoogle Scholar
Baldry, I. K., Glazebrook, K. & Driver, S. P.. 2008, MNRAS, 388.3 (2008): 945959Google Scholar
Madau, P. & Dickinson, M. 2014, ARAA, 52, 41548610.1146/annurev-astro-081811-125615CrossRefGoogle Scholar
Speagle, J. S., Steinhardt, C. L., Capak, P. L. & Silverman, J. D. (2014), ApJS, 214(2), 15CrossRefGoogle Scholar
Tremonti, C. A., et al. 2004, ApJ 613.2 (2004): 89810.1086/423264CrossRefGoogle Scholar
Smith, D. J. B. & Hayward, C. C. (2015), MNRAS 453.2 (2015): 15971607CrossRefGoogle Scholar
Rivera, G. C., et al. 2016, ApJ, 833(1), 9810.3847/1538-4357/833/1/98CrossRefGoogle Scholar
Dye, S. 2008, MNRAS, 389(3), 12931305CrossRefGoogle Scholar
Skelton, R. E., et al. 2014, ApJS, 214(2), 2410.1088/0067-0049/214/2/24CrossRefGoogle Scholar
Gladders, M. D., et al. 2013, ApJ, 770(1), 64CrossRefGoogle Scholar