Dark Energy Survey Year 3 Results: Exploiting small-scale information with lensing shear ratios

Sánchez, C.; Prat, J.; Zacharegkas, G.; Pandey, S.; Baxter, E.; Bernstein, G. M.; Blazek, J.; Cawthon, R.; Chang, C.; Krause, E.; Lemos, P.; Park, Y.; Raveri, M.; Sanchez, J.; Troxel, M. A.; Amon, A.; Fang, X.; Friedrich, O.; Gruen, D.; Porredon, A.; Secco, L. F.; Samuroff, S.; Alarcon, A.; Alves, O.; Andrade-Oliveira, F.; Bechtol, K.; Becker, M. R.; Camacho, H.; Campos, A.; Rosell, A. Carnero; Kind, M. Carrasco; Chen, R.; Choi, A.; Crocce, M.; Davis, C.; De Vicente, J.; DeRose, J.; Di Valentino, E.; Diehl, H. T.; Dodelson, S.; Doux, C.; Drlica-Wagner, A.; Eckert, K.; Eifler, T. F.; Elsner, F.; Elvin-Poole, J.; Everett, S.; Ferté, A.; Fosalba, P.; Gatti, M.; Giannini, G.; Gruendl, R. A.; Harrison, I.; Hartley, W. G.; Herner, K.; Huff, E. M.; Huterer, D.; Jarvis, M.; Jain, B.; Kuropatkin, N.; Leget, P. -F.; MacCrann, N.; McCullough, J.; Muir, J.; Myles, J.; Navarro-Alsina, A.; Rollins, R. P.; Roodman, A.; Rosenfeld, R.; Rykoff, E. S.; Sevilla-Noarbe, I.; Sheldon, E.; Shin, T.; Troja, A.; Tutusaus, I.; Varga, T. N.; Wechsler, R. H.; Yanny, B.; Yin, B.; Zhang, Y.; Zuntz, J.; Abbott, T. M. C.; Aguena, M.; Allam, S.; Bacon, D.; Bertin, E.; Bhargava, S.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Carretero, J.; Costanzi, M.; da Costa, L. N.; Pereira, M. E. S.; Desai, S.; Dietrich, J. P.; Doel, P.; Evrard, A. E.; Ferrero, I.; Flaugher, B.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Gerdes, D. W.; Giannantonio, T.; Gschwend, J.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Hoyle, B.; James, D. J.; Kuehn, K.; Lahav, O.; Lima, M.; Lin, H.; Maia, M. A. G.; Marshall, J. L.; Martini, P.; Melchior, P.; Menanteau, F.; Miquel, R.; Mohr, J. J.; Morgan, R.; Palmese, A.; Paz-Chinchón, F.; Petravick, D.; Pieres, A.; Malagón, A. A. Plazas; Rodriguez-Monroy, M.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Serrano, S.; Smith, M.; Soares-Santos, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; To, C.

doi:10.48550/arxiv.2105.13542

Cited by 10 publications

(28 citation statements)

References 43 publications

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“…These include an improved treatment of galaxy intrinsic alignments, inclusion of magnification effects on the lens galaxy density, and application of the so-called lensing ratio likelihood described in [17].…”

Section: Introductionmentioning

confidence: 99%

Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck II: Cross-correlation measurements and cosmological constraints

Chang¹,

Omori²,

Baxter³

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck II: Cross-correlation measurements and cosmological constraints

Chang¹,

Omori²,

Baxter³

et al. 2022

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“…The improvement is particularly noticeable in the S 8 direction, which is roughly proportional to the amplitude of the lensing power spectrum. This improvement is not surprising since the SR likelihood can significantly improve constraints on IA parameters, as demonstrated in [84,91,92]. We see in Figure 13 the corresponding IA constraints and how a 1 is strongly degenerate with S 8 .…”

Section: Forecastsmentioning

confidence: 65%

“…source galaxy redshifts and intrinsic alignment model parameters. Since such ratios are essentially independent of the galaxy-matter power spectrum, these ratios can be used at much smaller scales than are employed in the standard 3×2pt analysis [84]. We refer to these lensing ratios as shear ratios (SR).…”

Section: Shear Ratio Informationmentioning

confidence: 99%

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Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck I: Construction of CMB Lensing Maps and Modeling Choices

Omori,

Baxter,

Chang

et al. 2022

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“…The degeneracy breaking is not as pronounced as in past studies due to the scale cut of FWHM ≥ 7.9 arcmin. Another way to help to break the Ω m − 𝜎 8 degeneracy is the addition of small-scale shear ratios (Sánchez et al 2021) as demonstrated in Amon et al (2021); Secco et al (2021) and Gatti (2021). However, we do not include shear ratios in this study.…”

Section: Fiducial Resultsmentioning

confidence: 99%

Dark Energy Survey Year 3 results: Cosmology with peaks using an emulator approach

Zürcher,

Fluri,

Sgier

et al. 2021

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We constrain the matter density Ω m and the amplitude of density fluctuations 𝜎 8 within the ΛCDM cosmological model with shear peak statistics and angular convergence power spectra using mass maps constructed from the first three years of data of the Dark Energy Survey (DES Y3). We use tomographic shear peak statistics, including cross-peaks: peak counts calculated on maps created by taking a harmonic space product of the convergence of two tomographic redshift bins. Our analysis follows a forward-modelling scheme to create a likelihood of these statistics using N-body simulations, using a Gaussian process emulator. We take into account the uncertainty from the remaining, largely unconstrained ΛCDM parameters (Ω b , 𝑛 s and ℎ). We include the following lensing systematics: multiplicative shear bias, photometric redshift uncertainty, and galaxy intrinsic alignment. Stringent scale cuts are applied to avoid biases from unmodelled baryonic physics. We find that the additional non-Gaussian information leads to a tightening of the constraints on the structure growth parameter yielding 𝑆 8 ≡ 𝜎 8 √︁ Ω m /0.3 = 0.797 +0.015 −0.013 (68% confidence limits), with a precision of 1.8%, an improvement of 38% compared to the angular power spectra only case.The results obtained with the angular power spectra and peak counts are found to be in agreement with each other and no significant difference in 𝑆 8 is recorded. We find a mild tension of 1.5 𝜎 between our study and the results from Planck 2018, with our analysis yielding a lower 𝑆 8 . Furthermore, we observe that the combination of angular power spectra and tomographic peak counts breaks the degeneracy between galaxy intrinsic alignment 𝐴 IA and 𝑆 8 , improving cosmological constraints. We run a suite of tests concluding that our results are robust and consistent with the results from other studies using DES Y3 data.

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Dark Energy Survey Year 3 Results: Exploiting small-scale information with lensing shear ratios

Cited by 10 publications

References 43 publications

Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck II: Cross-correlation measurements and cosmological constraints

Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck II: Cross-correlation measurements and cosmological constraints

Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck I: Construction of CMB Lensing Maps and Modeling Choices

Dark Energy Survey Year 3 results: Cosmology with peaks using an emulator approach

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