Institution
IFAE
Other•Barcelona, Spain•
About: IFAE is a other organization based out in Barcelona, Spain. It is known for research contribution in the topics: Large Hadron Collider & Galaxy. The organization has 664 authors who have published 1270 publications receiving 51097 citations. The organization is also known as: Instituto de Fisica de Altas Energias & IFAE.
Topics: Large Hadron Collider, Galaxy, Higgs boson, Redshift, MAGIC (telescope)
Papers published on a yearly basis
Papers
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TL;DR: An exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected) in combination with the results at sqrt[s]=7 and 8 TeV.
Abstract: Dark matter particles, if sufficiently light, may be produced in decays of the Higgs boson. This Letter presents a statistical combination of searches for H→invisible decays where H is produced according to the standard model via vector boson fusion, Z(ll)H, and W/Z(had)H, all performed with the ATLAS detector using 36.1 fb^{-1} of pp collisions at a center-of-mass energy of sqrt[s]=13 TeV at the LHC. In combination with the results at sqrt[s]=7 and 8 TeV, an exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected).
234 citations
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University of Valencia1, Aligarh Muslim University2, University of Turin3, Colorado State University4, Hampton University5, Fermilab6, Massachusetts Institute of Technology7, University of Pittsburgh8, Northwestern University9, University of Kentucky10, Virginia Tech11, Ghent University12, Queen Mary University of London13, Michigan State University14, Université Paris-Saclay15, University of South Carolina16, Thomas Jefferson National Accelerator Facility17, IFAE18, University of Wrocław19
TL;DR: In this paper, the neutrino properties are modeled as a nuclear model and its theoretical uncertainties play an important role in interpreting every result, which is essential to every phase of experimental analyses.
233 citations
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University of Illinois at Urbana–Champaign1, National Center for Supercomputing Applications2, Stanford University3, Fermilab4, SLAC National Accelerator Laboratory5, Brookhaven National Laboratory6, Institut d'Astrophysique de Paris7, University of Pennsylvania8, IFAE9, University College London10, ETH Zurich11, Max Planck Society12, Austin Peay State University13, Rhodes University14, New York University15, Texas A&M University16, Indian Institute of Technology, Hyderabad17, Ludwig Maximilian University of Munich18, Ohio State University19, Autonomous University of Madrid20, University of Michigan21, University of Cambridge22, University of Washington23, Santa Cruz Institute for Particle Physics24, Australian Astronomical Observatory25, Argonne National Laboratory26, University of São Paulo27, Catalan Institution for Research and Advanced Studies28, Institut de Ciències de l'Espai29, University of Southampton30, State University of Campinas31, Princeton University32, California Institute of Technology33, University of Sussex34, Oak Ridge National Laboratory35
TL;DR: The Dark Energy Survey (DES) photometric data set Y3 GOLD as discussed by the authors contains nearly 5000 deg2 of grizY imaging in the south Galactic cap including nearly 390 million objects, with depth reaching a signal-to-noise ratio ∼10 for extended objects up to i AB ∼ 23.0, and top-of-the-atmosphere photometric uniformity 98% and purity >99% for galaxies with 19 < i AB < 22.5.
Abstract: We describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes aimed at the broad astrophysics community. Y3 GOLD improves on previous releases from DES, Y1 GOLD, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 GOLD comprises nearly 5000 deg2 of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ∼10 for extended objects up to i AB ∼ 23.0, and top-of-the-atmosphere photometric uniformity 98% and purity >99% for galaxies with 19 < i AB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis samples.
231 citations
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TL;DR: In this article, an improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail, including corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √s=13 TeV between 2015 and 2017.
227 citations
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Monash University, Clayton campus1, California Institute of Technology2, Massachusetts Institute of Technology3, Cardiff University4, University of Glasgow5, Northwestern University6, Lancaster University7, Swinburne University of Technology8, University of Paris9, University of Minnesota10, Indian Institute of Technology Bombay11, York University12, Georgia Institute of Technology13, IAC14, Albert Einstein Institution15, University of Oregon16, University of Wisconsin–Milwaukee17, IFAE18, Australia Telescope National Facility19, University of Florida20, Sapienza University of Rome21, University of Melbourne22, University of Tokyo23, University of Birmingham24
TL;DR: This work demonstrates that bilby produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the 11 GWTC-1 signals.
Abstract: Gravitational waves provide a unique tool for observational astronomy. While the first LIGO–Virgo catalogue of gravitational-wave transients (GWTC-1) contains 11 signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced bilby: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that bilby produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the 11 GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that bilby is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.
226 citations
Authors
Showing all 672 results
Name | H-index | Papers | Citations |
---|---|---|---|
J. S. Lange | 160 | 2083 | 145919 |
Diego F. Torres | 137 | 948 | 72180 |
M. I. Martínez | 134 | 1251 | 79885 |
Jose Flix | 133 | 1257 | 90626 |
Matteo Cavalli-Sforza | 129 | 1273 | 89442 |
Ilya Korolkov | 128 | 884 | 75312 |
Martine Bosman | 128 | 942 | 73848 |
Maria Pilar Casado | 128 | 981 | 78550 |
Clement Helsens | 128 | 870 | 74899 |
Imma Riu | 128 | 954 | 73842 |
Sebastian Grinstein | 128 | 1222 | 79158 |
Remi Zaidan | 126 | 744 | 71647 |
Arely Cortes-Gonzalez | 124 | 774 | 68755 |
Trisha Farooque | 124 | 841 | 69620 |
Martin Tripiana | 124 | 716 | 69652 |