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Dark Energy Survey Year 3: Blue shear
Phys. Rev. D 113, 103509 – Published 6 May, 2026
DOI: https://doi.org/10.1103/wmp3-qc3p
Abstract
Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. Using the Dark Energy Survey Year 3 shape catalog, we expect less impact from IA when we limit the sample 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 and better agree with the observations from Planck on the cosmic microwave background. Mitigating IA with sample selection in DES, rather than flexible model choices, can reduce uncertainty in by a factor of 1.5.
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References (79)
- A. Amon, D. Gruen, M. A. Troxel et al., Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to data calibration, Phys. Rev. D 105, 023514 (2022).
- M. Asgari, C.-A. Lin, B. Joachimi et al., KiDS-1000 cosmology: Cosmic shear constraints and comparison between two point statistics, Astron. Astrophys. 645, A104 (2021).
- R. Dalal, X. Li, A. Nicola et al., Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear power spectra, Phys. Rev. D 108, 123519 (2023).
- X. Li, T. Zhang, S. Sugiyama et al., Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear two-point correlation functions, Phys. Rev. D 108, 123518 (2023).
- L. Secco, S. Samuroff, E. Krause et al., Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to modeling uncertainty, Phys. Rev. D 105, 023515 (2022).
- Planck Collaboration, Planck 2018 results VI. Cosmological parameters, Astron. Astrophys. 641, A6(E) (2020).
- A. Amon and G. Efstathiou, A non-linear solution to the S8 tension? Mon. Not. R. Astron. Soc. 516, 5355 (2022).
- T. Abbott, M. Aguena et al. (DES and KiDS Collaborations), DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys, Open J. Astrophys. 6, (2023).
- C. Lamman, E. Tsaprazi, J. Shi et al., The IA guide: A breakdown of intrinsic alignment formalisms, arXiv:2309.08605.
- M. Troxel and M. Ishak, The intrinsic alignment of galaxies and its impact on weak gravitational lensing in an era of precision cosmology, Phys. Rep. 558, 1 (2015).
- L. Bigwood, A. Amon, A. Schneider et al., Weak lensing combined with the kinetic Sunyaev–Zel’dovich effect: a study of baryonic feedback, Mon. Not. R. Astron. Soc. 534, 655 (2024).
- Euclid Collaboration, Euclid preparation VII. Forecast validation for Euclid cosmological probes, Astron. Astrophys. 642, A191 (2020).
- LSST Science Collaboration, LSST science book, version 2.0, arXiv:0912.0201.
- D. Spergel, N. Gehrels, C. Baltay et al., Wide-field infrarred survey telescope-astrophysics focused telescope assets WFIRST-AFTA 2015 report, arXiv:1503.03757.
- R. Mandelbaum, T. Eifler et al. (LSST DESC Collaboration), The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document (2018).
- S. Bridle and L. King, Dark energy constraints from cosmic shear power spectra: impact of intrinsic alignments on photometric redshift requirements, New J. Phys. 9, 444 (2007).
- C. M. Hirata and U. Seljak, Intrinsic alignment-lensing interference as a contaminant of cosmic shear, Phys. Rev. D 70, 063526 (2004).
- J. A. Blazek, N. MacCrann, M. Troxel, and X. Fang, Beyond linear galaxy alignments, Phys. Rev. D 100, 103506 (2019).
- B. Joachimi, R. Mandelbaum, F. B. Abdalla, and S. L. Bridle, Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample, Astron. Astrophys. 527, A26 (2011).
- S. Samuroff, A. Campos, A. Porredon, and J. Blazek, Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error, Open J. Astrophys. 7, 40 (2024).
- S. Samuroff, R. Mandelbaum, J. Blazek et al., The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space, Mon. Not. R. Astron. Soc. 524, 2195 (2023).
- C. Lamman, D. Eisenstein, J. E. Forero-Romero et al., Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey, Mon. Not. R. Astron. Soc. 534, 3540 (2024).
- M. C. Fortuna, H. Hoekstra, B. Joachimi et al., The halo model as a versatile tool to predict intrinsic alignments, Mon. Not. R. Astron. Soc. 501, 2983 (2020).
- T. Bakx, T. Kurita, N. Elisa Chisari, Z. Vlah, and F. Schmidt, Effective field theory of intrinsic alignments at one loop order: A comparison to dark matter simulations, J. Cosmol. Astropart. Phys. 10 (2023) 005.
- S.-F. Chen and N. Kokron, A Lagrangian theory for galaxy shape statistics, J. Cosmol. Astropart. Phys. 01 (2024) 027.
- J. Harnois-Déraps, N. Martinet, and R. Reischke, Cosmic shear beyond 2-point statistics: Accounting for galaxy intrinsic alignment with projected tidal fields, Mon. Not. R. Astron. Soc. 509, 3868 (2022).
- K. Hoffmann, L. F. Secco, J. Blazek et al., Modeling intrinsic galaxy alignment in the MICE simulation, Phys. Rev. D 106, 123510 (2022).
- F. Maion, R. E. Angulo, T. Bakx, N. Elisa Chisari, T. Kurita, and M. Pellejero-Ibáñez, HYMALAIA: A hybrid lagrangian model for intrinsic alignments, Mon. Not. R. Astron. Soc. 531, 2684 (2024).
- Z. Vlah, N. E. Chisari, and F. Schmidt, An EFT description of galaxy intrinsic alignments, J. Cosmol. Astropart. Phys. 01 (2020) 025.
- S. Chen, J. DeRose, R. Zhou et al., Analysis of using the Lagrangian effective theory of LSS, Phys. Rev. D 110, 103518 (2024).
- A. M. Delgado, B. Hadzhiyska, S. Bose et al., The MillenniumTNG project: Intrinsic alignments of galaxies and haloes, Mon. Not. R. Astron. Soc. 523, 5899 (2023).
- S. Samuroff, R. Mandelbaum, and J. Blazek, Advances in constraining intrinsic alignment models with hydrodynamic simulations, Mon. Not. R. Astron. Soc. 508, 637 (2021).
- J. Blazek, M. McQuinn, and U. Seljak, Testing the tidal alignment model of galaxy intrinsic alignment, J. Cosmol. Astropart. Phys. 05 (2011) 010.
- C. M. Hirata, R. Mandelbaum, M. Ishak, U. Seljak , R. Nichol, K. A. Pimbblet, N. P. Ross, and D. Wake, Intrinsic galaxy alignments from the 2SLAQ and SDSS surveys: luminosity and redshift scalings and implications for weak lensing surveys, Mon. Not. R. Astron. Soc. 381, 1197 (2007).
- M. C. Fortuna, H. Hoekstra, H. Johnston et al., KiDS-1000: Constraints on the intrinsic alignment of luminous red galaxies, Astron. Astrophys. 654, A76 (2021).
- H. Johnston, C. Georgiou, B. Joachimi et al., KiDS+GAMA: Intrinsic alignment model constraints for current and future weak lensing cosmology, Astron. Astrophys. 624, A30 (2019).
- R. Mandelbaum, C. Blake, S. Bridle et al., The WiggleZ Dark Energy Survey: direct constraints on blue galaxy intrinsic alignments at intermediate redshifts, Mon. Not. R. Astron. Soc. 410, 844 (2011).
- P. Catelan, M. Kamionkowski, and R. D. Blandford, Intrinsic and extrinsic galaxy alignment, Mon. Not. R. Astron. Soc. 320, L7 (2001).
- J. Mackey, M. White, and M. Kamionkowski, Theoretical estimates of intrinsic galaxy alignment, Mon. Not. R. Astron. Soc. 332, 788 (2002).
- S. Samuroff, J. Blazek, M. A. Troxel et al., Dark Energy Survey Year 1 results: constraints on intrinsic alignments and their colour dependence from galaxy clustering and weak lensing, Mon. Not. R. Astron. Soc. 489, 5453 (2019).
- C. Heymans, E. Grocutt, A. Heavens et al., CFHTLenS tomographic weak lensing cosmological parameter constraints: Mitigating the impact of intrinsic galaxy alignments, Mon. Not. R. Astron. Soc. 432, 2433 (2013).
- S.-S. Li, K. Kuijken, H. Hoekstra et al., KiDS+VIKING-450: An internal-consistency test for cosmic shear tomography with a colour-based split of source galaxies, Astron. Astrophys. 646, A175 (2021).
- E. Krause, T. Eifler, and J. Blazek, The impact of intrinsic alignment on current and future cosmic shear surveys, Mon. Not. R. Astron. Soc. 456, 207 (2015).
- M. Gatti, E. Sheldon, A. Amon et al., Dark energy survey year 3 results: Weak lensing shape catalogue, Mon. Not. R. Astron. Soc. 504, 4312 (2021).
- J. Myles, A. Alarcon, A. Amon et al., Dark Energy Survey Year 3 results: Redshift calibration of the weak lensing source galaxies, Mon. Not. R. Astron. Soc. 505, 4249 (2021).
- W. G. Hartley, A. Choi, A. Amon et al., Dark Energy Survey Year 3 Results: Deep Field optical + near-infrared images and catalogue, Mon. Not. R. Astron. Soc. 509, 3547 (2021).
- S. Everett, B. Yanny, N. Kuropatkin et al., Dark Energy Survey Year 3 Results: Measuring the survey transfer function with Balrog, Astrophys. J. Suppl. Ser. 258, 15 (2022).
- N. MacCrann, M. R. Becker, J. McCullough et al., Dark Energy Survey Y3 results: Blending shear and redshift biases in image simulations, Mon. Not. R. Astron. Soc. 509, 3371 (2021).
- A. C. CarnallR. J. McLure, J. S. DunlopR. Davé, Inferring the star formation histories of massive quiescent galaxies with bagpipes: Evidence for multiple quenching mechanisms, Mon. Not. R. Astron. Soc. 480, 4379 (2018).
- G. B. Brammer, P. G. van Dokkum, and P. Coppi, EAZY: A fast, public photometric redshift code, Astrophys. J. 686, 1503 (2008).
- A. J. Mead, S. Brieden, T. Tröster, and C. Heymans, hmcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback, Mon. Not. R. Astron. Soc. 502, 1401 (2021).
- M. L. Brown, A. N. Taylor, N. C. Hambly, and S. Dye, Measurement of intrinsic alignments in galaxy ellipticities, Mon. Not. R. Astron. Soc. 333, 501 (2002).
- M. A. Troxel, N. MacCrann, J. Zuntz et al., Dark Energy Survey Year 1 results: Cosmological constraints from cosmic shear, Phys. Rev. D 98, 043528 (2018).
- T. M. C. Abbott, M. Aguena, A. Alarcon et al., Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing, Phys. Rev. D 105, 023520 (2022).
- G. Efstathiou and S. Gratton, A detailed description of the CAMSPEC likelihood pipeline and a reanalysis of the Planck high frequency maps, Open J. Astrophys. 4 (2021).
- M. Raveri and W. Hu, Concordance and discordance in cosmology, Phys. Rev. D 99, 043506 (2019).
- C. García-García, M. Zennaro, G. Aricò, D. Alonso, and R. E. Angulo, Cosmic shear with small scales: DES-Y3, KiDS-1000 and HSC-DR1, J. Cosmol. Astropart. Phys. 08 (2024) 024.
- G. Aricò, R. E. Angulo, M. Zennaro et al., DES Y3 cosmic shear down to small scales: Constraints on cosmology and baryons, Astron. Astrophys. 678, A109 (2023).
- Ž. Ivezić, S. M. Kahn, J. A. Tyson et al., LSST: From science drivers to reference design and anticipated data products, Astrophys. J. 873, 111 (2019).
- Euclid Collaboration, I. The euclid wide survey, Astron. Astrophys. 662, A112 (2022).
- R. Akeson, L. Armus, E. Bachelet et al., The wide field infrared survey telescope: 100 Hubbles for the 2020s, arXiv:1902.05569.
- J. Siegel, J. McCullough, A. Amon et al., Intrinsic alignment demographics for next-generation lensing: Revealing galaxy property trends with DESI Y1 direct measurements, arXiv:2507.11530.
- R. Buchs, C. Davis, D. Gruen et al., Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing, Mon. Not. R. Astron. Soc. 489, 820 (2019).
- O. Friedrich, F. Andrade-Oliveira, H. Camacho et al., Dark Energy Survey year 3 results: Covariance modelling and its impact on parameter estimation and quality of fit, Mon. Not. R. Astron. Soc. 508, 3125 (2021).
- X. Fang, T. Eifler, and E. Krause, 2D-FFTLog: Efficient computation of real-space covariance matrices for galaxy clustering and weak lensing, Mon. Not. R. Astron. Soc. 497, 2699 (2020).
- A. Stebbins, Weak lensing on the celestial sphere, arXiv:astro-ph/9609149.
- M. LoVerde and N. Afshordi, Extended Limber approximation, Phys. Rev. D 78, 123506 (2008).
- C. Howlett, A. Lewis, A. Hall, and A. Challinor, CMB power spectrum parameter degeneracies in the era of precision cosmology, J. Cosmol. Astropart. Phys. 04 (2012) 027.
- A. Lewis, A. Challinor, and A. Lasenby, Efficient computation of cosmic microwave background anisotropies in closed Friedmann-Robertson-Walker models, Astrophys. J. 538, 473 (2000).
- J. E. McEwen, X. Fang, C. M. Hirata, and J. A. Blazek, FAST-PT: A novel algorithm to calculate convolution integrals in cosmological perturbation theory, J. Cosmol. Astropart. Phys. 09 (2016) 015.
- J. Zuntz, M. Paterno, E. Jennings et al., CosmoSIS: Modular cosmological parameter estimation, Astron. Comput. 12, 45 (2015).
- P. Lemos, N. Weaverdyck, R. P. Rollins et al., Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey, Mon. Not. R. Astron. Soc. 521, 1184 (2023).
- W. J. Handley, M. P. Hobson, and A. N. Lasenby, polychord: Next-generation nested sampling, Mon. Not. R. Astron. Soc. 453, 4385 (2015).
- W. J. Handley, M. P. Hobson, and A. N. Lasenby, polychord: Nested sampling for cosmology, Mon. Not. R. Astron. Soc.: Lett. 450, L61 (2015).
- A. Lewis and A. Challinor, CAMB: Code for Anisotropies in the Microwave Background, Astrophysics Source Code Library, record ascl:1102.026 (2011).
- C. Patrignani, Review of particle physics, Chin. Phys. C 40, 100001 (2016).
- B. Hadzhiyska, S. Ferraro, R. Pakmor et al., Interpreting Sunyaev–Zel’dovich observations with MillenniumTNG: Mass and environment scaling relations, Mon. Not. R. Astron. Soc. 526, 369 (2023).
- C. D. Leonard, M. M. Rau, and R. Mandelbaum, Photometric redshifts and intrinsic alignments: Degeneracies and biases in the 3 ×2pt analysis, Phys. Rev. D 109, 083528 (2024).
- J. McCullough, DES Y3: Blue Shear Datavector and Cosmology Chain [Data set] (2026), 10.5281/zenodo.18762855; https://jamiemccullough.github.io/data/blueshear/.