Search Results for author: H. Hildebrandt

Found 7 papers, 2 papers with code

Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing

no code implementations14 Dec 2020 B. Stölzner, B. Joachimi, A. Korn, H. Hildebrandt, A. H. Wright

We fit this model to pre-calibrated redshift distributions and implement an analytic marginalisation over the potentially several hundred redshift nuisance parameters in the weak lensing likelihood, which is demonstrated to accurately recover the cosmological posterior.

Cosmology and Nongalactic Astrophysics

Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4

no code implementations26 Oct 2020 S. J. Nakoneczny, M. Bilicki, A. Pollo, M. Asgari, A. Dvornik, T. Erben, B. Giblin, C. Heymans, H. Hildebrandt, A. Kannawadi, K. Kuijken, N. R. Napolitano, E. Valentijn

We find that XGB is the most robust model for classification, while ANN performs the best for combined classification and redshift.

Cosmology and Nongalactic Astrophysics

The PAU Survey: Photometric redshifts using transfer learning from simulations

no code implementations16 Apr 2020 M. Eriksen, A. Alarcon, L. Cabayol, J. Carretero, R. Casas, F. J. Castander, J. De Vicente, E. Fernandez, J. Garcia-Bellido, E. Gaztanaga, H. Hildebrandt, H. Hoekstra, B. Joachimi, R. Miquel, C. Padilla, E. Sanchez, I. Sevilla-Noarbe, P. Tallada

In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-$z$) code.

Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics

KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear

1 code implementation21 Jun 2019 S. Joudaki, H. Hildebrandt, D. Traykova, N. E. Chisari, C. Heymans, A. Kannawadi, K. Kuijken, A. H. Wright, M. Asgari, T. Erben, H. Hoekstra, B. Joachimi, L. Miller, T. Tröster, J. L. van den Busch

We present a combined tomographic weak gravitational lensing analysis of the Kilo Degree Survey (KV450) and the Dark Energy Survey (DES-Y1).

Cosmology and Nongalactic Astrophysics

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

no code implementations13 Sep 2017 M. Bilicki, H. Hoekstra, M. J. I. Brown, V. Amaro, C. Blake, S. Cavuoti, J. T. A. de Jong, C. Georgiou, H. Hildebrandt, C. Wolf, A. Amon, M. Brescia, S. Brough, M. V. Costa-Duarte, T. Erben, K. Glazebrook, A. Grado, C. Heymans, T. Jarrett, S. Joudaki, K. Kuijken, G. Longo, N. Napolitano, D. Parkinson, C. Vellucci, G. A. Verdoes Kleijn, L. Wang

The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

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