Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 45 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 96 tok/s Pro
Kimi K2 206 tok/s Pro
GPT OSS 120B 457 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

PROSPECT: A profile likelihood code for frequentist cosmological parameter inference (2312.02972v3)

Published 5 Dec 2023 in astro-ph.CO, astro-ph.IM, and hep-ph

Abstract: Cosmological parameter inference has been dominated by the Bayesian approach for the past two decades, primarily due to its computational efficiency. However, the Bayesian approach involves integration of the posterior probability and therefore depends on both the choice of model parametrisation and the choice of prior on the model parameter space. In some cases, this can lead to conclusions which are driven by choice of parametrisation and priors rather than by data. The profile likelihood method provides a complementary frequentist tool which can be used to investigate this effect. In this paper, we present the code PROSPECT for computing profile likelihoods in cosmology. We showcase the code using a phenomenological model for converting dark matter into dark radiation that suffers from large volume effects and prior dependence. PROSPECT is compatible with both cobaya and MontePython, and is publicly available at https://github.com/AarhusCosmology/prospect_public.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (66)
  1. Chapman and Hall/CRC, 3 ed., 2013.
  2. Planck Collaboration, P. A. R. Ade et al., “Planck intermediate results. XVI. Profile likelihoods for cosmological parameters,” Astron. Astrophys. 566 (2014) A54, arXiv:1311.1657 [astro-ph.CO].
  3. Planck Collaboration, N. Aghanim et al., “Planck 2018 results. VI. Cosmological parameters,” Astron. Astrophys. 641 (2020) A6, arXiv:1807.06209 [astro-ph.CO]. [Erratum: Astron.Astrophys. 652, C4 (2021)].
  4. S. Gariazzo et al., “Neutrino mass and mass ordering: no conclusive evidence for normal ordering,” JCAP 10 (2022) 010, arXiv:2205.02195 [hep-ph].
  5. E. B. Holm, T. Tram, and S. Hannestad, “Decaying warm dark matter revisited,” JCAP 08 no. 08, (2022) 044, arXiv:2205.13628 [astro-ph.CO].
  6. E. B. Holm, L. Herold, S. Hannestad, A. Nygaard, and T. Tram, “Decaying dark matter with profile likelihoods,” Phys. Rev. D 107 no. 2, (2023) L021303, arXiv:2211.01935 [astro-ph.CO].
  7. L. Herold, E. G. M. Ferreira, and E. Komatsu, “New Constraint on Early Dark Energy from Planck and BOSS Data Using the Profile Likelihood,” Astrophys. J. Lett. 929 no. 1, (2022) L16, arXiv:2112.12140 [astro-ph.CO].
  8. L. Herold and E. G. M. Ferreira, “Resolving the Hubble tension with early dark energy,” Phys. Rev. D 108 no. 4, (2023) 043513, arXiv:2210.16296 [astro-ph.CO].
  9. J. S. Cruz, S. Hannestad, E. B. Holm, F. Niedermann, M. S. Sloth, and T. Tram, “Profiling cold new early dark energy,” Phys. Rev. D 108 no. 2, (2023) 023518, arXiv:2302.07934 [astro-ph.CO].
  10. P. Carrilho, C. Moretti, and A. Pourtsidou, “Cosmology with the EFTofLSS and BOSS: dark energy constraints and a note on priors,” JCAP 01 (2023) 028, arXiv:2207.14784 [astro-ph.CO].
  11. T. Simon, P. Zhang, V. Poulin, and T. L. Smith, “Consistency of effective field theory analyses of the BOSS power spectrum,” Phys. Rev. D 107 no. 12, (2023) 123530, arXiv:2208.05929 [astro-ph.CO].
  12. J. Donald-McCann, R. Gsponer, R. Zhao, K. Koyama, and F. Beutler, “Analysis of unified galaxy power spectrum multipole measurements,” Mon. Not. Roy. Astron. Soc. 526 no. 3, (2023) 3461–3481, arXiv:2307.07475 [astro-ph.CO].
  13. R. Zhao et al., “A Multi-tracer Analysis for the eBOSS galaxy sample based on the Effective Field Theory of Large-scale Structure,” arXiv:2308.06206 [astro-ph.CO].
  14. C. Moretti, M. Tsedrik, P. Carrilho, and A. Pourtsidou, “Modified gravity and massive neutrinos: constraints from the full shape analysis of BOSS galaxies and forecasts for Stage IV surveys,” arXiv:2306.09275 [astro-ph.CO].
  15. E. B. Holm, L. Herold, T. Simon, E. G. M. Ferreira, S. Hannestad, V. Poulin, and T. Tram, “Bayesian and frequentist investigation of prior effects in EFTofLSS analyses of full-shape BOSS and eBOSS data,” arXiv:2309.04468 [astro-ph.CO].
  16. A. Reeves, L. Herold, S. Vagnozzi, B. D. Sherwin, and E. G. M. Ferreira, “Restoring cosmological concordance with early dark energy and massive neutrinos?,” Mon. Not. Roy. Astron. Soc. 520 no. 3, (2023) 3688–3695, arXiv:2207.01501 [astro-ph.CO].
  17. A. Nygaard, E. B. Holm, S. Hannestad, and T. Tram, “Fast and effortless computation of profile likelihoods using CONNECT,” arXiv:2308.06379 [astro-ph.CO].
  18. B. Audren, J. Lesgourgues, K. Benabed, and S. Prunet, “Conservative Constraints on Early Cosmology: an illustration of the Monte Python cosmological parameter inference code,” JCAP 1302 (2013) 001, arXiv:1210.7183 [astro-ph.CO].
  19. T. Brinckmann and J. Lesgourgues, “MontePython 3: boosted MCMC sampler and other features,” arXiv:1804.07261 [astro-ph.CO].
  20. J. Torrado and A. Lewis, “Cobaya: Bayesian analysis in cosmology,” Astrophysics Source Code Library, record ascl:1910.019, Oct., 2019.
  21. J. Torrado and A. Lewis, “Cobaya: code for Bayesian analysis of hierarchical physical models,” JCAP 2021 no. 5, (May, 2021) 057, arXiv:2005.05290 [astro-ph.IM].
  22. A. Lewis, A. Challinor, and A. Lasenby, “Efficient computation of CMB anisotropies in closed FRW models,” APJ 538 (2000) 473–476, arXiv:astro-ph/9911177 [astro-ph].
  23. D. Blas, J. Lesgourgues, and T. Tram, “The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approximation schemes,” Journal of Cosmology and Astroparticle Physics 2011 no. 07, (Jul, 2011) 034–034. https://doi.org/10.1088%2F1475-7516%2F2011%2F07%2F034.
  24. B. Hadzhiyska, K. Wolz, S. Azzoni, D. Alonso, C. García-García, J. Ruiz-Zapatero, and A. Slosar, “Cosmology with 6 parameters in the Stage-IV era: efficient marginalisation over nuisance parameters,” arXiv:2301.11895 [astro-ph.CO].
  25. R. Gsponer, R. Zhao, J. Donald-McCann, D. Bacon, K. Koyama, R. Crittenden, T. Simon, and E.-M. Mueller, “Cosmological constraints on early dark energy from the full shape analysis of eBOSS DR16,” arXiv:2312.01977 [astro-ph.CO].
  26. Y. Pawitan, In All Likelihood. Oxford University Press, 2013.
  27. A. Spurio Mancini, D. Piras, J. Alsing, B. Joachimi, and M. P. Hobson, “CosmoPower: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys,” Mon. Not. Roy. Astron. Soc. 511 no. 2, (2022) 1771–1788, arXiv:2106.03846 [astro-ph.CO].
  28. A. Nygaard, E. B. Holm, S. Hannestad, and T. Tram, “CONNECT: a neural network based framework for emulating cosmological observables and cosmological parameter inference,” JCAP 05 (2023) 025, arXiv:2205.15726 [astro-ph.IM].
  29. S. Günther, J. Lesgourgues, G. Samaras, N. Schöneberg, F. Stadtmann, C. Fidler, and J. Torrado, “CosmicNet II: emulating extended cosmologies with efficient and accurate neural networks,” JCAP 11 (2022) 035, arXiv:2207.05707 [astro-ph.CO].
  30. C.-H. To, E. Rozo, E. Krause, H.-Y. Wu, R. H. Wechsler, and A. N. Salcedo, “LINNA: Likelihood Inference Neural Network Accelerator,”. https://arxiv.org/abs/2203.05583.
  31. J. E. Gammal, N. Schöneberg, J. Torrado, and C. Fidler, “Fast and robust Bayesian Inference using Gaussian Processes with GPry,” arXiv:2211.02045 [astro-ph.CO].
  32. M. Bonici, F. Bianchini, and J. Ruiz-Zapatero, “Capse.jl: efficient and auto-differentiable CMB power spectra emulation,” arXiv:2307.14339 [astro-ph.CO].
  33. S. Günther, “Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA,” arXiv:2307.01138 [astro-ph.CO].
  34. Z. Li, J. Sullivan, and M. Millea, “xzackli/Bolt.jl: citation.” Nov., 2023. https://doi.org/10.5281/zenodo.10065126.
  35. O. Hahn, F. List, and N. Porqueres, “DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology,” arXiv:2311.03291 [astro-ph.CO].
  36. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science 220 (1983) 671–680.
  37. L. Knox, “Determination of inflationary observables by cosmic microwave background anisotropy experiments,” Phys. Rev. D 52 (1995) 4307–4318, arXiv:astro-ph/9504054.
  38. S. Hannestad, “Stochastic optimization methods for extracting cosmological parameters from cosmic microwave background radiation power spectra,” Phys. Rev. D 61 (2000) 023002, arXiv:astro-ph/9911330.
  39. N. Schöneberg, G. Franco Abellán, A. Pérez Sánchez, S. J. Witte, V. Poulin, and J. Lesgourgues, “The H0 Olympics: A fair ranking of proposed models,” Phys. Rept. 984 (2022) 1–55, arXiv:2107.10291 [astro-ph.CO].
  40. S. Goldstein, J. C. Hill, V. Iršič, and B. D. Sherwin, “Canonical Hubble-Tension-Resolving Early Dark Energy Cosmologies are Inconsistent with the Lyman-α𝛼\alphaitalic_α Forest,” arXiv:2303.00746 [astro-ph.CO].
  41. G. Efstathiou, E. Rosenberg, and V. Poulin, “Improved Planck constraints on axion-like early dark energy as a resolution of the Hubble tension,” arXiv:2311.00524 [astro-ph.CO].
  42. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of State Calculations by Fast Computing Machines,” The Journal of Chemical Physics 21 no. 6, (12, 2004) 1087–1092, https://pubs.aip.org/aip/jcp/article-pdf/21/6/1087/8115285/1087_1_online.pdf. https://doi.org/10.1063/1.1699114.
  43. E. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. John Wiley & Sons, Inc., USA, 1989.
  44. L. Ingber, “Simulated annealing: Practice versus theory,” Mathematical and Computer Modelling 18 no. 11, (1993) 29–57. https://www.sciencedirect.com/science/article/pii/089571779390204C.
  45. L. Ingber, “Adaptive simulated annealing (ASA): Lessons learned.” 2000.
  46. B. Audren, J. Lesgourgues, G. Mangano, P. D. Serpico, and T. Tram, “Strongest model-independent bound on the lifetime of Dark Matter,” JCAP 12 (2014) 028, arXiv:1407.2418 [astro-ph.CO].
  47. V. Poulin, P. D. Serpico, and J. Lesgourgues, “A fresh look at linear cosmological constraints on a decaying dark matter component,” JCAP 08 (2016) 036, arXiv:1606.02073 [astro-ph.CO].
  48. A. Nygaard, T. Tram, and S. Hannestad, “Updated constraints on decaying cold dark matter,” JCAP 05 (2021) 017, arXiv:2011.01632 [astro-ph.CO].
  49. J. Hamann, “Evidence for extra radiation? Profile likelihood versus Bayesian posterior,” Journal of Cosmology and Astroparticle Physics 2012 no. 03, (Mar, 2012) 021–021. https://doi.org/10.1088%2F1475-7516%2F2012%2F03%2F021.
  50. A. Gómez-Valent, “Fast test to assess the impact of marginalization in Monte Carlo analyses and its application to cosmology,” Phys. Rev. D 106 no. 6, (2022) 063506, arXiv:2203.16285 [astro-ph.CO].
  51. M. M. Ivanov, E. McDonough, J. C. Hill, M. Simonović, M. W. Toomey, S. Alexander, and M. Zaldarriaga, “Constraining Early Dark Energy with Large-Scale Structure,” Phys. Rev. D 102 no. 10, (2020) 103502, arXiv:2006.11235 [astro-ph.CO].
  52. E. McDonough, J. C. Hill, M. M. Ivanov, A. La Posta, and M. W. Toomey, “Observational constraints on early dark energy,” arXiv:2310.19899 [astro-ph.CO].
  53. T. Bringmann, F. Kahlhoefer, K. Schmidt-Hoberg, and P. Walia, “Converting nonrelativistic dark matter to radiation,” Phys. Rev. D 98 no. 2, (2018) 023543, arXiv:1803.03644 [astro-ph.CO].
  54. F. McCarthy and J. C. Hill, “Converting dark matter to dark radiation does not solve cosmological tensions,” Phys. Rev. D 108 no. 6, (2023) 063501, arXiv:2210.14339 [astro-ph.CO].
  55. C.-P. Ma and E. Bertschinger, “Cosmological perturbation theory in the synchronous and conformal Newtonian gauges,” Astrophys. J. 455 (1995) 7–25, arXiv:astro-ph/9506072.
  56. A. G. Riess et al., “A 2.4% Determination of the Local Value of the Hubble Constant,” Astrophys. J. 826 no. 1, (2016) 56, arXiv:1604.01424 [astro-ph.CO].
  57. E. Abdalla et al., “Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies,” JHEAp 34 (2022) 49–211, arXiv:2203.06142 [astro-ph.CO].
  58. A. Gelman and D. B. Rubin, “Inference from Iterative Simulation Using Multiple Sequences,” Statistical Science 7 no. 4, (1992) 457 – 472. https://doi.org/10.1214/ss/1177011136.
  59. BOSS Collaboration, S. Alam et al., “The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample,” Mon. Not. Roy. Astron. Soc. 470 no. 3, (2017) 2617–2652, arXiv:1607.03155 [astro-ph.CO].
  60. A. J. Ross, L. Samushia, C. Howlett, W. J. Percival, A. Burden, and M. Manera, “The clustering of the SDSS DR7 main Galaxy sample – I. A 4 per cent distance measure at z=0.15𝑧0.15z=0.15italic_z = 0.15” Mon. Not. Roy. Astron. Soc. 449 no. 1, (2015) 835–847, arXiv:1409.3242 [astro-ph.CO].
  61. F. Beutler, C. Blake, M. Colless, D. H. Jones, L. Staveley-Smith, L. Campbell, Q. Parker, W. Saunders, and F. Watson, “The 6dF Galaxy Survey: baryon acoustic oscillations and the local Hubble constant,” Monthly Notices of the Royal Astronomical Society 416 no. 4, (Jul, 2011) 3017–3032. https://doi.org/10.1111%2Fj.1365-2966.2011.19250.x.
  62. Pan-STARRS1 Collaboration, D. M. Scolnic et al., “The Complete Light-curve Sample of Spectroscopically Confirmed SNe Ia from Pan-STARRS1 and Cosmological Constraints from the Combined Pantheon Sample,” Astrophys. J. 859 no. 2, (2018) 101, arXiv:1710.00845 [astro-ph.CO].
  63. A. G. Riess et al., “A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team,” Astrophys. J. Lett. 934 no. 1, (2022) L7, arXiv:2112.04510 [astro-ph.CO].
  64. DES Collaboration, T. M. C. Abbott et al., “Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing,” Phys. Rev. D 105 no. 2, (2022) 023520, arXiv:2105.13549 [astro-ph.CO].
  65. Dover, New York, ninth dover printing, tenth gpo printing ed., 1964.
  66. A. Lewis, “GetDist: a Python package for analysing Monte Carlo samples,” arXiv:1910.13970 [astro-ph.IM].
Citations (4)
View on Semantic Scholar
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

Explore Trending Papers

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube