Dark Energy Survey Year 3 Results: Three-Point Shear Correlations and Mass Aperture Moments

Secco, L. F.; Jarvis, M.; Jain, B.; Chang, C.; Gatti, M.; Frieman, J.; Adhikari, S.; Alarcon, A.; Amon, A.; Bechtol, K.; Becker, M. R.; Bernstein, G. M.; Blazek, J.; Campos, A.; Rosell, A. Carnero; Kind, M. Carrasco; Choi, A.; Cordero, J.; DeRose, J.; Dodelson, S.; Doux, C.; Drlica-Wagner, A.; Everett, S.; Giannini, G.; Gruen, D.; Gruendl, R. A.; Harrison, I.; Hartley, W. G.; Herner, K.; Krause, E.; MacCrann, N.; McCullough, J.; Myles, J.; Navarro-Alsina, A.; Prat, J.; Rollins, R. P.; Samuroff, S.; Sanchez, C.; Sevilla-Noarbe, I.; Sheldon, E.; Troxel, M. A.; Zeurcher, D.; Aguena, M.; Andrade-Oliveira, F.; Annis, J.; Bacon, D.; Bertin, E.; Bocquet, S.; Brooks, D.; Burke, D. L.; Carretero, J.; Castander, F. J.; Crocce, M.; da Costa, L. N.; Pereira, M. E. S.; De Vicente, J.; Diehl, H. T.; Doel, P.; Eckert, K.; Ferrero, I.; Flaugher, B.; Friedel, D.; Garcia-Bellido, J.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Huterer, D.; Kuehn, K.; Kuropatkin, N.; Maia, M. A. G.; Marshall, J. L.; Menanteau, F.; Miquel, R.; Mohr, J. J.; Morgan, R.; Muir, J.; Paz-Chinchon, F.; Pieres, A.; Malagon, A. A. Plazas; Rodriguez-Monroy, M.; Roodman, A.; Sanchez, E.; Serrano, S.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; To, C.; Weller, J.

doi:10.48550/arxiv.2201.05227

Cited by 2 publications

(2 citation statements)

References 64 publications

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“…There are several potential applications of our IA simulation not explored here. For instance, studies of IA in third-order lensing correlations (Pyne & Joachimi 2021, Schmitz et al 2018, in particular because such statistics have been detected at high signal-to-noise in recent DES Y3 data (Secco et al 2022a). Another application would be to study priors on the IA modeling, in particular on TATT parameters which are poorly constrained otherwise.…”

Section: Discussionmentioning

confidence: 99%

Modeling Intrinsic Galaxy Alignment in the MICE Simulation

Hoffmann,

Secco,

Blazek

et al. 2022

Preprint

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The intrinsic alignment (IA) of galaxies is potentially a major limitation in deriving cosmological constraints from weak lensing surveys. In order to investigate this effect we assign intrinsic shapes and orientations to galaxies in the light-cone output of the MICE simulation, spanning ∼ 5000 deg 2 and reaching redshift 𝑧 = 1.4. This assignment is based on a 'semi-analytic' IA model that uses photometric properties of galaxies as well as the spin and shape of their host halos. Advancing on previous work, we include more realistic distributions of galaxy shapes and a luminosity dependent galaxy-halo alignment. The IA model parameters are calibrated against COSMOS and BOSS LOWZ observations. The null detection of IA in observations of blue galaxies is accounted for by setting random orientations for these objects. We compare the two-point alignment statistics measured in the simulation against predictions from the analytical IA models NLA and TATT over a wide range of scales, redshifts and luminosities for red and blue galaxies separately. We find that both models fit the measurements well at scales above 8 ℎ −1 Mpc, while TATT outperforms NLA at smaller scales. The IA parameters derived from our fits are in broad agreement with various observational constraints from red galaxies. Lastly, we build a realistic source sample, mimicking DES Year 3 observations and use it to predict the IA contamination to the observed shear statistics. We find this prediction to be within the measurement uncertainty, which might be a consequence of the random alignment of blue galaxies in the simulation.

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Section: Discussionmentioning

confidence: 99%

Modeling Intrinsic Galaxy Alignment in the MICE Simulation

Hoffmann,

Secco,

Blazek

et al. 2022

Preprint

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“…This is particularly pertinent in the light of advances in the measurement of three-point statistics from survey data. Secco et al (2022a) recently reported high signal-tonoise detections of three-point shear correlations and aperture mass statistics in the first three years of data from the Dark Energy Survey. These measurements lay the foundations for joint two-and threepoint cosmological analyses which will of course need tight control of systematics such as intrinsic alignments.…”

Section: Subhalo Mass Rangementioning

confidence: 99%

Three-point intrinsic alignments of dark matter halos in the IllustrisTNG simulation

Pyne,

Tenneti,

Joachimi

2022

Preprint

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We use the IllustrisTNG suite of cosmological simulations to measure intrinsic alignment (IA) bispectra of dark matter subhalos between redshifts 0 and 1. We decompose the intrinsic shear field into E-and B-modes and find that the bispectra 𝐵 𝛿 𝛿E and 𝐵 𝛿EE , between the matter overdensity field, 𝛿, and the E-mode field, are detected with high significance. We also model the IA bispectra analytically using a method consistent with the two-point non-linear alignment model. We use this model and the simulation measurements to infer the intrinsic alignment amplitude 𝐴 IA and find that values of 𝐴 IA obtained from IA power spectra and bispectra agree well at scales up to 𝑘 max = 2 ℎMpc −1 , for example at 𝑧 = 1 𝐴 IA = 2.13± 0.02 from the cross power spectrum between the matter overdensity and E-mode fields and 𝐴 IA = 2.11± 0.03 from 𝐵 𝛿 𝛿E . This demonstrates that a single physically motivated model can jointly model two-point and three-point statistics of intrinsic alignments, thus enabling a cleaner separation between intrinsic alignments and cosmological weak lensing signals.

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Dark Energy Survey Year 3 Results: Three-Point Shear Correlations and Mass Aperture Moments

Cited by 2 publications

References 64 publications

Modeling Intrinsic Galaxy Alignment in the MICE Simulation

Modeling Intrinsic Galaxy Alignment in the MICE Simulation

Three-point intrinsic alignments of dark matter halos in the IllustrisTNG simulation

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