Search Results for author: Ben Hoyle

Found 7 papers, 1 papers with code

Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing

no code implementations25 Feb 2020 DES Collaboration, Tim Abbott, Michel Aguena, Alex Alarcon, Sahar Allam, Steve Allen, James Annis, Santiago Avila, David Bacon, Alberto Bermeo, Gary Bernstein, Emmanuel Bertin, Sunayana Bhargava, Sebastian Bocquet, David Brooks, Dillon Brout, Elizabeth Buckley-Geer, David Burke, Aurelio Carnero Rosell, Matias Carrasco Kind, Jorge Carretero, Francisco Javier Castander, Ross Cawthon, Chihway Chang, Xinyi Chen, Ami Choi, Matteo Costanzi, Martin Crocce, Luiz da Costa, Tamara Davis, Juan De Vicente, Joseph DeRose, Shantanu Desai, H. Thomas Diehl, Jörg Dietrich, Scott Dodelson, Peter Doel, Alex Drlica-Wagner, Kathleen Eckert, Tim Eifler, Jack Elvin-Poole, Juan Estrada, Spencer Everett, August Evrard, Arya Farahi, Ismael Ferrero, Brenna Flaugher, Pablo Fosalba, Josh Frieman, Juan Garcia-Bellido, Marco Gatti, Enrique Gaztanaga, David Gerdes, Tommaso Giannantonio, Paul Giles, Sebastian Grandis, Daniel Gruen, Robert Gruendl, Julia Gschwend, Gaston Gutierrez, Will Hartley, Samuel Hinton, Devon L. Hollowood, Klaus Honscheid, Ben Hoyle, Dragan Huterer, David James, Mike Jarvis, Tesla Jeltema, Margaret Johnson, Stephen Kent, Elisabeth Krause, Richard Kron, Kyler Kuehn, Nikolay Kuropatkin, Ofer Lahav, Ting Li, Christopher Lidman, Marcos Lima, Huan Lin, Niall MacCrann, Marcio Maia, Adam Mantz, Jennifer Marshall, Paul Martini, Julian Mayers, Peter Melchior, Juan Mena, Felipe Menanteau, Ramon Miquel, Joe Mohr, Robert Nichol, Brian Nord, Ricardo Ogando, Antonella Palmese, Francisco Paz-Chinchon, Andrés Plazas Malagón, Judit Prat, Markus Michael Rau, Kathy Romer, Aaron Roodman, Philip Rooney, Eduardo Rozo, Eli Rykoff, Masao Sako, Simon Samuroff, Carles Sanchez, Alexandro Saro, Vic Scarpine, Michael Schubnell, Daniel Scolnic, Santiago Serrano, Ignacio Sevilla, Erin Sheldon, J. Allyn Smith, Eric Suchyta, Molly Swanson, Gregory Tarle, Daniel Thomas, Chun-Hao To, Michael A. Troxel, Douglas Tucker, Tamas Norbert Varga, Anja von der Linden, Alistair Walker, Risa Wechsler, Jochen Weller, Reese Wilkinson, Hao-Yi Wu, Brian Yanny, Zhuowen Zhang, Joe Zuntz

We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.

Cosmology and Nongalactic Astrophysics

Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification

no code implementations11 Dec 2017 Xan Morice-Atkinson, Ben Hoyle, David Bacon

The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Data Analysis, Statistics and Probability

Stacking for machine learning redshifts applied to SDSS galaxies

no code implementations19 Feb 2016 Roman Zitlau, Ben Hoyle, Kerstin Paech, Jochen Weller, Markus Michael Rau, Stella Seitz

We observe a significant improvement of between 1. 9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees).

Measuring photometric redshifts using galaxy images and Deep Neural Networks

no code implementations27 Apr 2015 Ben Hoyle

We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Data Analysis, Statistics and Probability

Anomaly detection for machine learning redshifts applied to SDSS galaxies

no code implementations27 Mar 2015 Ben Hoyle, Markus Michael Rau, Kerstin Paech, Christopher Bonnett, Stella Seitz, Jochen Weller

We present an analysis of anomaly detection for machine learning redshift estimation.

Cosmology and Nongalactic Astrophysics

Feature importance for machine learning redshifts applied to SDSS galaxies

no code implementations17 Oct 2014 Ben Hoyle, Markus Michael Rau, Roman Zitlau, Stella Seitz, Jochen Weller

When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36% from 0. 066 to 0. 041, and decreases the fraction of catastrophic outliers by 57% from 2. 32% to 0. 99%.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

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