Dark Energy Survey Year 3: Blue Shear

21 M. Soares-Santos, A. Alarcon, A. Amon, A. A. Plazas Malag\'on, A. Campos, A. Carnero Rosell, A. Choi, A. Drlica-Wagner, A. Farahi, A. Fert\'e, A. J. Ross, A. K. Romer, A. Navarro-Alsina, A. Palmese, A. Pieres, A. Porredon, A. Roodman, B. Flaugher, B. Yanny, B. Yin, C. Chang, C. Conselice, C. Davis, C. Doux, C. S\'anchez, C. To, D. Brooks, DES Collaboration, D. Gruen, D. Huterer, D. J. James, D. L. Burke, D. L. Hollowood, D. Sanchez Cid, E. Buckley-Geer, E. Gaztanaga, E. Legnani, E. M. Huff, E. Sanchez, E. Sheldon, E. S. Rykoff, E. Suchyta, F. Andrade-Oliveira, F. J. Castander, F. Tarsitano, G. Giannini, G. Gutierrez, G. M. Bernstein, G. Tarle, H. Huang, H. T. Diehl, I. Ferrero, I. Harrison, I. Sevilla-Noarbe, I. Tutusaus, J. Blazek, J. Carretero, J. Cordero, J. DeRose, J. De Vicente, J. Elvin-Poole, J. Frieman, J. Garc\'ia-Bellido, J. L. Marshall, J. McCullough, J. Mena-Fern\'andez, J. Muir, J. Myles, J. Prat, J. Weller, J. Zuntz, K. Bechtol, K. Eckert, K. Herner, K. Honscheid, K. Kuehn, L. F. Secco, L. N. da Costa, M. Aguena, M. A. Troxel, M. Carrasco Kind, M. Crocce, M. E. C. Swanson, M. E. S. Pereira, M. Gatti, M. Jarvis, M. Lima, M. Paterno, M. Raveri, M. R. Becker, M. Schubnell, M. Smith, M. Vincenzi, M. Yamamoto, N. Jeffrey, N. MacCrann, N. Weaverdyck, O. Alves, O. Friedrich, O. Lahav, P. Doel, P. Wiseman, R. A. Gruendl, R. Cawthon, R. Miquel, S. Allam, S. Bocquet, S. Desai, S. Dodelson, S. Everett, S. L. Bridle, S. Lee, S. Pandey, S. R. Hinton, S. Samuroff, T. Shin, V. Vikram, Y. Zhang

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keywords bluegalaxiessamplecosmicdarkenergymodelshear

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Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5.

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