Astro2020 APC White Paper: The MegaMapper: a z &gt; 2 spectroscopic instrument for the study of Inflation and Dark Energy

Schlegel, David J.; Kollmeier, Juna A.; Aldering, Greg; Bailey, S.; Baltay, C.; Bebek, C.; BenZvi, S.; Besuner, Robert; Blanc, Guillermo A.; Bolton, A.; Bouri, Mohamed; Brooks, David H.; Buckley-Geer, E.; Cai, Zheng; Crane, Jeffrey D.; Dey, Arjun; Doel, P.; Fan, Xiaohui; Ferraro, Simone; Font-Ribera, Andreu; Gutiérrez, G.; Guy, Julien; Heetderks, H.; Huterer, Dragan; Infante, L.; Jelinsky, Patrick; Johns, Matt; Karagiannis, Dionysios; Kent, Stephen M.; Kim, Alex; Kneib, Jean-Paul; Kronig, Luzius; Konidaris, Nick; Lahav, O.; Lampton, M.; Lang, Dustin; Leauthaud, Alexie; Linder, Eric V.; Magneville, C.; Martini, Paul; Mateo, Mario; McDonald, Patrick; Miller, Christopher J.; Moustakas, John; Myers, Adam D.; Mulchaey, John S.; Newman, Jeffrey A.; Nugent, P.; Palanque-Delabrouille, N.; Padmanabhan, Nikhil; Piro, Anthony L.; Poppett, Claire; Prochaska, J. X.; Pullen, Anthony R.; Rabinowitz, D.; Ramirez, Solange; Rix, Hans─Walter; Ross, Ashley J.; Samushia, Lado; Schaan, Emmanuel; Schubnell, M.; Seljak, Uroš; Seo, Hee-Jong; Shectman, Stephen; Silber, Joseph H.; Simon, Joshua D.; Slepian, Zachary; Soares-Santos, M.; Tarlè, G.; Thompson, Ian; Valluri, Monica; White, Martin; Wilson, Michael J.; Yèche, Ch; Zaritsky, Dennis

doi:10.48550/arxiv.1907.11171

Cited by 52 publications

(52 citation statements)

References 18 publications

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“…(20) Figure 4 shows the equation of state as a function of redshift w u (z) up to redshift z = 6 as expected from the Megamapper proposal [14,15], after setting Ω u0 = 0.6911, Ω r0 = 8.97 × 10 −5 [16]. The different lines correspond to ∆N ef f = 0.19 and (−δ = 0.001, 0.01, 0.1, 1) from top to bottom, respectively.…”

Section: Consistency Conditionmentioning

confidence: 85%

More on Emergent Dark Energy from Unparticles

Artymowski,

Ben-Dayan,

Kumar

2021

Preprint

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In a recent paper [1] we suggested the possibility that the present acceleration of the Universe is due to thermodynamical behavior of unparticles. The model is free of scalar fields, modified gravity, coincidence problem, initial conditions problem and possesses interesting distinct predictions regarding the equation of state of Dark Energy, the growth rate and the number of relativistic degrees of freedom at BBN and CMB decoupling. We further suggested that the model can remove some of the Hubble tension. A recent paper mostly considering a different range of parameter space criticized the idea, claiming the model is unstable and does not fit the data [2]. We demonstrate that these claims are due to incorrect use of approximations and initial conditions and that the model stands tall. We further suggest a consistency condition of the model in terms of observables. We then fit publicly available supernovae data to derive the expected Hubble parameter and constrain the parameters of the model.

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Section: Consistency Conditionmentioning

confidence: 85%

More on Emergent Dark Energy from Unparticles

Artymowski,

Ben-Dayan,

Kumar

2021

Preprint

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“…We consider two CMB surveys: the current Planck 2018 data and a future CMB-S4 experiment [32,33]. For galaxy redshift surveys, we consider two variations of the proposed next generation MegaMapper survey, called the Ideal and Fiducial versions [34,35].…”

Section: Next Generation Surveysmentioning

confidence: 99%

Exploring Early and Late Cosmology with Next Generation Surveys

Brando,

Linder

2020

Preprint

Self Cite

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“…Furthermore, an as extension, in this work we design a new neural network to add a new GRB calibrated dataset, which can therefore help to compare the cosmographic µ(z) relation inferred from supernovae observations (assuming flatness) with the cosmographic µ(z) relation inferred from supernovae and GRBs, extending to higher forecast redshifts. In the near future, high-redshift spectroscopic surveys [25,26] and/or gravitational-wave missions and projects [27,28] will provide accurate data in the redshift range of 2 < z < 5. Meanwhile, our trained RNN+BNN method will allow us to acquire a higher density of precise distance modulus data, for both SNeIa and GRBs, over the wide redshift range of 0.01 < z < 10.…”

Section: Introductionmentioning

confidence: 99%

Neural networks and standard cosmography with newly calibrated high redshift GRB observations

Escamilla-Rivera,

Carvajal,

Zamora

et al. 2021

Preprint

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Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the cosmic expansion history. However, the calibration of their luminosity distances is not an easy task in comparison to Type Ia Supernovae (SNeIa). To calibrate these data, correlations between their luminosity and other observed properties of GRBs need to be identified, and we must consider the validity of our assumptions about these correlations over their entire observed redshift range. In this work, we propose a new method to calibrate GRBs as cosmological distance indicators using SNeIa observations with a completely model-independent deep learning architecture. An overview of this machine learning technique was developed in [1] to study the evolution of dark energy models at high redshift. The aim of the method developed in this work is to combine two networks: a Recurrent Neural Network (RNN) and a Bayesian Neural Network (BNN). Using this computational approach, denoted RNN+BNN, we extend the network's efficacy by adding the computation of covariance matrices to the Bayesian process. Once this is done, the SNeIa distance-redshift relation can be tested on the full GRB sample and therefore used to implement a cosmographic reconstruction of the distance-redshift relation in different regimes. Thus, our newly-trained neural network is used to constrain the parameters describing the kinematical state of the Universe via a cosmographic approach at high redshifts (up to z ≈ 10), wherein we require a very minimal set of assumptions that do not rely on dynamical equations for any specific theory of gravity.

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Astro2020 APC White Paper: The MegaMapper: a z > 2 spectroscopic instrument for the study of Inflation and Dark Energy

Cited by 52 publications

References 18 publications

More on Emergent Dark Energy from Unparticles

More on Emergent Dark Energy from Unparticles

Exploring Early and Late Cosmology with Next Generation Surveys

Neural networks and standard cosmography with newly calibrated high redshift GRB observations

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