Snowmass2021 - Letter of interest cosmology intertwined I: Perspectives for the next decade

Valentino, Eleonora Di; Anchordoqui, Luis A.; Akarsu, Özgür; Ali-Haı̈moud, Yacine; Amendola, Luca; Arendse, Nikki; Asgari, Marika; Ballardini, M.; Basilakos, Spyros; Battistelli, E. S.; Benetti, Micol; Birrer, Simon; Bouchet, F. R.; Bruni, Marco; Calabrese, E.; Camarena, David; Capozziello, Salvatore; Chen, Angela; Chluba, Jens; Chudaykin, Anton; Colgáin, Eoin Ó; Cyr-Racine, Francis-Yan; Bernardis, P. de; Pérez, Javier de Cruz; Delabrouille, J.; Dunkley, Jo; Escamilla-Rivera, Celia; Ferté, A.; Finelli⋆, F.; Freedman, Wendy L.; Frusciante, Noemi; Giusarma, Elena; Gómez-Valent, Adrià; Handley, Will; Harrison, I.; Hart, Luke; Heavens, Alan; Hildebrandt, Hendrik; Holz, D. E.; Huterer, Dragan; Ivanov, Mikhail M.; Joudaki, Shahab; Kamionkowski, Marc; Karwal, Tanvi; Knox, L.; Kumar, Suresh; Lamagna, L.; Lesgourgues, Julien; Lucca, Matteo; Marra, Valerio; Masi, S.; Matarrese, S.; Mazumdar, Arindam; Melchiorri, A.; Mena, Olga; Mersini-Houghton, Laura; Miranda, Vivian; Moreno-Pulido, Cristian; Mota, David F.; Muir, J.; Mukherjee, Ankan; Niedermann, Florian; Notari, Alessio; Nunes, Rafael C.; Pace, Francesco; Paliathanasis, Andronikos; Palmese, A.; Pan, Supriya; Paoletti, D.; Pettorino, V.; Piacentini, F.; Poulin, Vivian; Raveri, Marco; Riess, Adam G.; Salzano, Vincenzo; Saridakis, Emmanuel N.; Sen, Anjan A.; Shafieloo, Arman; Shajib, Anowar J.; Silk, Joseph; Silvestri, Alessandra; Sloth, Martin S.; Smith, Tristan L.; Peracaula, Joan Solà; Bruck, Carsten van de; Verde, Licia; Visinelli, Luca; Wandelt, B. D.; Wang, Deng; Wang, Jianmin; Yadav, Anil Kumar; Yang, Weiqiang

doi:10.1016/j.astropartphys.2021.102606

Cited by 62 publications

(58 citation statements)

References 25 publications

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“…When likelihood evaluation is not tractable, we call the distribution an implicit likelihood function. Using this stochastic simulator, sbi methods generate a set of N sampleparameter pairs {(x (1) , θ (1) ), (x (2) , θ (2) ), . .…”

Section: Simulation-based Inference Methodologymentioning

confidence: 99%

“…An impending flood of high-quality data from cosmological surveys promises to revolutionize our understanding of the universe (see e.g. [1] for a review of up-coming surveys). The collection of this data alone, however, is not sufficient to perform fundamental science; in order to extract cosmological information one must also "solve the inverse problem," i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Novel approaches in the field of simulation-based inference (sbi) 1 are starting to overcome these obstacles (see [5] for a recent review). sbi exploits the fact that data generated by a stochastic simulator can be regarded as sampling an exact likelihood, even when this function is not explicitly known.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Cole,

Miller,

Witte

et al. 2021

Preprint

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Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (tmnre) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the (i) efficiency, (ii) scalability, and (iii) trustworthiness of the inference. Using measurements of the Cosmic Microwave Background (CMB), we show that tmnre can achieve converged posteriors using orders of magnitude fewer simulator calls than conventional Markov Chain Monte Carlo (mcmc) methods. Remarkably, the required number of samples is effectively independent of the number of nuisance parameters. In addition, a property called local amortization allows the performance of rigorous statistical consistency checks that are not accessible to sampling-based methods. tmnre promises to become a powerful tool for cosmological data analysis, particularly in the context of extended cosmologies, where the timescale required for conventional sampling-based inference methods to converge can greatly exceed that of simple cosmological models such as ΛCDM. To perform these computations, we use an implementation of tmnre via the open-source code swyft. ‡ *

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Section: Simulation-based Inference Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Cole,

Miller,

Witte

et al. 2021

Preprint

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“…The revisiting of the foundations of ΛCDM has come about due to the growing observational crisis primarily centered on the value of the Hubble constant H 0 , which appears to be the most serious of the emerging cosmological tensions [7][8][9][10]. The discrepancy between early and late time measurements of the value of H 0 came to the fore with the reporting of local values of H 0 from the SH0ES [11,12], the latest of which gives H R21 0 = 73.04 ± 1.04 km s −1 Mpc −1 [11], which are brought down by strong lensing measurements [13] and measurements from the tip of the red giant branch [14,15].…”

Section: Introductionmentioning

confidence: 99%

On the Robustness of the Constancy of the Supernova Absolute Magnitude: Non-parametric Reconstruction \& Bayesian approaches

Benisty¹,

Mifsud²,

Said³

et al. 2022

Preprint

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In this work, we test the robustness of the constancy of the Supernova absolute magnitude MB using Non-parametric Reconstruction Techniques (NRT). We isolate the luminosity distance parameter dL(z) from the Baryon Acoustic Oscillations (BAO) data set and cancel the expansion part from the observed distance modulus µ(z). Consequently, the degeneracy between the absolute magnitude with the Hubble constant H0, is replaced by a degeneracy with the sound horizon at drag epoch r d . When imposing the r d value, this yields the MB(z) = MB + δMB(z) value from NRT. We perform the respective reconstructions using the model independent Artificial Neural Network (ANN) technique and Gaussian processes (GP) regression. For the ANN we infer MB = −19.22 ± 0.20, and for the GP we get MB = −19.25 ± 0.39 when using the sound horizon from late time measurements. These estimations provide a 1 σ possibility of a nuisance parameter presence δMB(z) at higher redshifts. We also tested different known nuisance models with the Markov Chain Monte Carlo (MCMC) technique which showed a strong preference for the constant model, but it was not possible not single out a best fit nuisance model.

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“…Cosmology have been enhanced in the last decade from an enormous increase of new observational surveys whose methods for handling them are still a challeng-ing endeavor, not only because the fundamental nature of the dark sector is still an open question, but also from the persisting tensions between the values measured/inferred of H 0 and f σ 8 and anomalies in early-time observations [1,2]. On the local universe side, there is a wide variety of dark energy proposals to explain the cosmic accelerated expansion under the General Relativity (GR) framework while considering gauge invariant cosmological perturbations on a Friedmann-Lemaître-Robertson-Walker (FLRW) background.…”

Section: Introductionmentioning

confidence: 99%

Inhomogeneous solutions in $f(T,B)$ gravity

Nájera,

Aguilar,

Rave-Franco

et al. 2022

Preprint

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In this paper we explore the possibility to find exact solutions for Teleparallel Gravity (TG) of the type of spherically symmetric Lemaître-Tolman-Bondi (LTB) dust models. We apply to the LTB metric the formalism of Teleparallel Gravity in its extension to f (T, B) models, which can be seen it as the analagous from the Schwarzschild solution in General Relativity. An exact LTB solution is obtained which is compatible with a specific f (T, B) model whose observational constraints are cosmological viable in a standard spatially flat Robertson-Walker geometry.

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Snowmass2021 - Letter of interest cosmology intertwined I: Perspectives for the next decade

Cited by 62 publications

References 25 publications

Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

On the Robustness of the Constancy of the Supernova Absolute Magnitude: Non-parametric Reconstruction \& Bayesian approaches

Inhomogeneous solutions in $f(T,B)$ gravity

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