Search Results for author: S. Seitz

Found 4 papers, 2 papers with code

Dark Energy Survey Year 1 Results: Weak Lensing Mass Calibration of redMaPPer Galaxy Clusters

1 code implementation30 Apr 2018 T. McClintock, T. N. Varga, D. Gruen, E. Rozo, E. S. Rykoff, T. Shin, P. Melchior, J. DeRose, S. Seitz, J. P. Dietrich, E. Sheldon, Y. Zhang, A. von der Linden, T. Jeltema, A. Mantz, A. K. Romer, S. Allen, M. R. Becker, A. Bermeo, S. Bhargava, M. Costanzi, S. Everett, A. Farahi, N. Hamaus, W. G. Hartley, D. L. Hollowood, B. Hoyle, H. Israel, P. Li, N. MacCrann, G. Morris, A. Palmese, A. A. Plazas, G. Pollina, M. M. Rau, M. Simet, M. Soares-Santos, M. A. Troxel, C. Vergara Cervantes, R. H. Wechsler, J. Zuntz, T. M. C. Abbott, F. B. Abdalla, S. Allam, J. Annis, S. Avila, S. L. Bridle, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, M. Crocce, C. E. Cunha, C. B. D'Andrea, L. N. da Costa, C. Davis, J. De Vicente, H. T. Diehl, P. Doel, A. Drlica-Wagner, A. E. Evrard, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. W. Gerdes, T. Giannantonio, R. A. Gruendl, G. Gutierrez, K. Honscheid, D. J. James, D. Kirk, E. Krause, K. Kuehn, O. Lahav, T. S. Li, M. Lima, M. March, J. L. Marshall, F. Menanteau, R. Miquel, J. J. Mohr, B. Nord, R. L. C. Ogando, A. Roodman, E. Sanchez, V. Scarpine, R. Schindler, I. Sevilla-Noarbe, M. Smith, R. C. Smith, F. Sobreira, E. Suchyta, M. E. C. Swanson, G. Tarle, D. L. Tucker, V. Vikram, A. R. Walker, J. Weller

Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects.

Cosmology and Nongalactic Astrophysics

MegaFace: A Million Faces for Recognition at Scale

no code implementations8 May 2015 D. Miller, E. Brossard, S. Seitz, I. Kemelmacher-Shlizerman

Specifically, we have collected from Flickr a \textbf{Million} faces and evaluated state of the art face recognition algorithms on this dataset.

Face Recognition

Bias-Free Shear Estimation using Artificial Neural Networks

no code implementations3 Feb 2010 D. Gruen, S. Seitz, J. Koppenhoefer, A. Riffeser

Expressed in terms of the quality parameter defined by GREAT08 we achieve a Q = 40, 140 and 1300 without and 50, 200 and 1300 with circularization for low, medium and high signal-to-noise data sets, respectively.

Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics

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