2020
DOI: 10.3847/1538-4357/ab7a91
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The Lyman Alpha Reference Sample. X. Predicting Lyα Output from Star-forming Galaxies Using Multivariate Regression*

Abstract: Understanding the production and escape of Lyman α (Lyα) radiation from star-forming galaxies is a long standing problem in astrophysics. The ability to predict the Lyα luminosity of galaxies would open up new ways of exploring the Epoch of Reionization (EoR), and to estimate Lyα emission from galaxies in cosmological simulations where radiative transfer calculations cannot be done. We apply multivariate regression methods to the Lyman Alpha Reference Sample dataset to obtain a relation between the galaxy prop… Show more

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Cited by 39 publications
(35 citation statements)
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References 77 publications
(108 reference statements)
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“…It is well-understood that starburst regions, escaping Lyα photons, are often associated with dust (Atek et al 2009;Hayes 2015;Runnholm et al 2020). The distribution of these dust around starburst regions may be dense and/or porous, as also seen in the present study (see Fig.…”
Section: Discussionsupporting
confidence: 80%
“…It is well-understood that starburst regions, escaping Lyα photons, are often associated with dust (Atek et al 2009;Hayes 2015;Runnholm et al 2020). The distribution of these dust around starburst regions may be dense and/or porous, as also seen in the present study (see Fig.…”
Section: Discussionsupporting
confidence: 80%
“…In observations there is a large scatter between the starformation rate inferred from Lyα luminosity compared to UV based estimates (Santos et al 2020;Runnholm et al 2020). Typically, the median SFR estimate from Lyα exceeds that from UV measurements below ∼ 10 M /yr, but decreases above this value.…”
Section: Model Limitationsmentioning
confidence: 99%
“…In the face of this complexity, it seems that the determination of leakiness based on indirect signposts can only be done on a statistical basis rather than for individual objects (cf. Runnholm et al 2020 for predicting Lyα radiation using multivariate regression). This reinforces the need for large samples, which was the −0.2) are LCEs, but only weak (albeit statistically significant) correlations are found with fesc (see Table 1).…”
Section: Implications For Escape Fractionsmentioning
confidence: 99%