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Original
1904
Earlier work this paper cites.
Meng-Xiang Lin, Giampaolo Benevento, Wayne Hu, and Marco Raveri, “Acoustic Dark Energy: Potential Conversion of the Hubble Tension,” Phys. Rev. D 100
Original
1905
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L. Verde, T. Treu, and A. G. Riess, “Tensions between the Early and the Late Universe,” Nature Astron. 3
Original
1907
Earlier work this paper cites.
N. Aghanim et al. (Planck), “Planck 2018 results. V. CMB power spectra and likelihoods,” Astron. Astrophys. 641
Original
1907
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1908
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Jesús Torrado and Antony Lewis, “Cobaya: Bayesian analysis in cosmology,” Astrophysics Source Code Library, record ascl:1910.019 (2019), ascl:1910.019
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1910
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1911
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Mark Gonzalez, Mark P. Hertzberg, and Fabrizio Rompineve, “Ultralight Scalar Decay and the Hubble Tension,” JCAP 10
Original
2006
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Tristan L. Smith, Vivian Poulin, José Luis Bernal, Kimberly K. Boddy, Marc Kamionkowski, and Riccardo Murgia, “Early dark energy is not excluded by current large-scale structure data,” Phys. Rev. D 103
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2011
Cited alongside, same era.

Then

Kevin Aylor, MacKenzie Joy, Lloyd Knox, Marius Millea, Srinivasan Raghunathan, and W. L. Kimmy Wu, “Sounds Discordant: Classical Distance Ladder \& Λ \Lambda CDM -based Determinations of the Cosmological Sound Horizon,” Astrophys. J. 874
Original
2019
Later among the works it cites.
N. Aghanim et al. (Planck), “Planck 2018 results. VI. Cosmological parameters,” Astron. Astrophys. 641
Original
2021
Later among the works it cites.
Elcio Abdalla et al. , “Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies,” JHEAp 34
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2022
Later among the works it cites.
A. Amon et al. (DES), “Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to data calibration,” Phys. Rev. D 105
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2024

Procoli: Profiles of cosmological likelihoods

Karwal, Tanvi, Patel, Yashvi, Bartlett, Alexa et al.

Understand

Frequentist profile likelihoods have seen a resurgence in cosmology, offering an alternative to Bayesian methods as they can circumvent the impact of prior-volume effects.

This paper presents Procoli, a fast and accessible package to obtain profile likelihoods in cosmology, available on GitHub and PyPI.
Procoli seamlessly integrates with MontePython, incorporating all its available data likelihoods, as well as any modified versions of CLASS.
This paper provides a comprehensive overview of the Procoli code, detailing the simulated-annealing optimizer at its core and the sequential computation of the profile.