{"paper":{"title":"redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.GA"],"primary_cat":"astro-ph.IM","authors_text":"A. Abate, A. A. Plazas, A. Benoit-L\\'evy, A. Carnero Rosell, A. E. Evrard, A. Fausti Neto, A. G. Kim, A. H. Bauer, A. K. Romer, A. Roodman, A. R. Walker, B. Flaugher, B. Hoyle, B. Leistedt, B. Nord, B. Santiago, C. B. D'Andrea, C. Bonnett, C. Davis, C. E. Cunha, C. J. Miller, C. Lidman, C. R. O'Neill, D. Brooks, D. Capozzi, D. Carollo, D. Gruen, D. J. James, D. L. Burke, D. L. DePoy, D. Thomas, D. W. Gerdes, E. Bertin, E. Buckley-Geer, E. Gaztanaga, E. Rozo, E. Sanchez, E. S. Rykoff, E. Suchyta, F. B. Abdalla, F. J. Castander, F. Sobreira, G. M. Bernstein, H. T. Diehl, H.V. Peiris, I. Sevilla-Noarbe, J. Carretero, J. Frieman, J. J. Mohr, J. P. Dietrich, J. Thaler, K. Glazebrook, K. Honscheid, K. Kuehn, L. N. da Costa, M. A. G. Maia, M. Banerji, M. Carrasco Kind, M. Crocce, M. E. C. Swanson, M. Jarvis, M. J. Childress, M. Lima, M. March, M. Sako, M. Schubnell, M. Soares-Santos, N. Kuropatkin, O. Lahav, P. Doel, P. Fosalba, P. Martini, P. Melchior, R. A. Gruendl, R. C. Nichol, R. C. Smith, R. H. Wechsler, R. Miquel, R. Ogando, S. Desai, s. Uddin, T. Abbott, T. Davis, T. F. Eifler, V. Vikram, W. Wester, Y. Zhang","submitted_at":"2015-07-20T12:00:54Z","abstract_excerpt":"We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the color-cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photozs are very nearly as accurate as the best machine-learning based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.05460","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}