Validating Posteriors Obtained by an Emulator When Jointly Fitting Mock Data of the Global 21 cm Signal and High-z Galaxy UV Luminosity Function
Creators
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1.
University of Colorado Boulder
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2.
Ames Research Center
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3.
Universities Space Research Association
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4.
Jet Propulsion Lab
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5.
California Institute of Technology
Abstract
Although neural-network-based emulators enable efficient parameter estimation in 21 cm cosmology, the accuracy of such constraints is poorly understood. We employ nested sampling to fit mock data of the global 21 cm signal and high-z galaxy ultraviolet luminosity function (UVLF) and compare for the first time the emulated posteriors obtained using the global signal emulator globalemu to the "true" posteriors obtained using the full model on which the emulator is trained using ARES. Of the eight model parameters we employ, four control the star formation efficiency (SFE) and thus can be constrained by UVLF data, while the remaining four control UV and X-ray photon production and the minimum virial temperature of star-forming halos (Tₘᵢₙ) and thus are uniquely probed by reionization and 21 cm measurements. For noise levels of 50 and 250 mK in the 21 cm data being jointly fit, the emulated and "true" posteriors are consistent to within 1σ. However, at lower noise levels of 10 and 25 mK, globalemu overpredicts Tₘᵢₙ and underpredicts γlo, an SFE parameter, by ≈3σ–4σ, while the "true" ARES posteriors capture their fiducial values within 1σ. We find that jointly fitting the mock UVLF and 21 cm data significantly improves constraints on the SFE parameters by breaking degeneracies in the ARES parameter space. Our results demonstrate the astrophysical constraints that can be expected for global 21 cm experiments for a range of noise levels from pessimistic to optimistic, as well as the potential for probing redshift evolution of SFE parameters by including UVLF data.
Copyright and License
© 2023. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
We thank the anonymous reviewer for their detailed comments that helped improve the manuscript. We thank Harry Bevins for useful discussions. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement 80ARC017M0006. This work was also partially supported by the Universities Space Research Association via D.R. using internal funds for research development. We also acknowledge support by NASA grant 80NSSC23K0013. J.M. was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory/California Institute of Technology, administered by Oak Ridge Associated Universities under contract with NASA. This work utilized the Blanca condo computing resource at the University of Colorado Boulder. Blanca is jointly funded by computing users and the University of Colorado Boulder.
Software References
This research relies heavily on the python (Van Rossum & Drake 1995) open-source community, in particular, numpy (Harris et al. 2020), matplotlib (Hunter 2007), scipy (Virtanen et al. 2020), and jupyter (Kluyver et al. 2016). This research also utilized MultiNest (Feroz & Hobson 2008; Feroz et al. 2009, 2019), PolyChord (Handley et al. 2015a, 2015b), and globalemu (Bevins et al. 2021)
Files
Dorigo_Jones_2023_ApJ_959_49.pdf
Additional details
Identifiers
- ISSN
- 1538-4357
Funding
- National Aeronautics and Space Administration
- 80ARC017M0006
- National Aeronautics and Space Administration
- 80NSSC23K0013
- National Aeronautics and Space Administration
- NASA Postdoctoral Fellowship