Publications by Christoph Ernst René Schäfer

Measurement of the inclusive and differential t(t)over-bar gamma cross sections in the dilepton channel and effective field theory interpretation in proton-proton collisions at root s=13 TeV

Jean-François Carlin, Rakesh Chawla, Richard Pitts, Arvind Shah, Ian Smith, Randoald Müller, Carlos Andres Peña Reyes, Jung Eung Park, Thomas Mountford, Matthias Finger, Jin Won Seo, Johannes Steen, Josef Andreas Schuler, Olivier Martin, Melody Swartz, Kyumin Lee, Hoang-Trang Lam, Maria Isabel Cordero Campana, Ananda Sankar Roy, Bertrand François, Yogesh Singh Chauhan, Xiaoqiang Wang, Miguel Fernández Ruiz, Sun Hee Kim, Jian Zhao, João Miguel das Neves Duarte, Justin Terence Notz Pekkanen, Joo Yeon Kim, Alexis Kalogeropoulos, Jae-Woo Choi, Pedro Camilo de Oliveira e Silva, Cristopher Ariel Zeballos Avila, Myun Sik Kim, Chao Wang, Yixing Chen, Aravind Kumar Chandiran, Tian Cheng, Ana Margarida Da Costa Ricardo, , , , , , , , , , , ,

Galaxy cluster cores as seen with VLT/MUSE: New strong-lensing analyses of RX J2129.4+0009, MS 0451.6-0305, and MACS J2129.4-0741

Christoph Ernst René Schäfer, Jean-Paul Richard Kneib, Markus Rexroth

We present strong-lensing analyses of three galaxy clusters, RX J2129.4 + 0009 (z = 0.235), MS 0451.6 - 0305 (z = 0.55), and MACS J2129.4 - 0741 (z = 0.589), using the powerful combination of Hubble Space Telescope (HSI) multiband observations, and Multi-U ...

OXFORD UNIV PRESS2021

Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

Christoph Ernst René Schäfer

The study of strong gravitational lenses is a relatively new scientific field in astronomy with many applications in cosmology. Its unique observables allow astronomers to trace dark matter, determine the expansion of the universe and study galaxy evolutio ...

EPFL2020

Lenstool-HPC: A High Performance Computing based mass modelling tool for cluster-scale gravitational lenses

Christoph Ernst René Schäfer, Jean-Paul Richard Kneib

With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires faster and more effic ...

ELSEVIER2020

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Christoph Ernst René Schäfer, Jean-Paul Richard Kneib, Markus Rexroth

Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...

ELSEVIER2020

Machine-learning Classifiers for Intermediate Redshift Emission-line Galaxies

Johan Comparat, Christoph Ernst René Schäfer, Jean-Paul Richard Kneib, David Schlegel

Classification of intermediate redshift (z = 0.3-0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGNs), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was impossible because ...

IOP PUBLISHING LTD2019

Target-based fiber assignment for large survey spectrographs

Laleh Makarem, Christoph Ernst René Schäfer, Jean-Paul Richard Kneib

Next generation massive spectroscopic survey projects have to process a massive amount of targets. The preparation of subsequent observations should be feasible in a reasonable amount of time. We present a fast algorithm for target assignment that scales a ...

Spie-Int Soc Optical Engineering2016

The PCA Lens-Finder: application to CFHTLS

Frédéric Courbin, Christoph Ernst René Schäfer, Jean-Paul Richard Kneib, Rémy Elie Joseph, Danuta Janina Paraficz

We present the results of a new search for galaxy-scale strong lensing systems in CFHTLS Wide. Our lens-finding technique involves a preselection of potential lens galaxies, applying simple cuts in size and magnitude. We then perform a Principal Component ...

Edp Sciences S A2016