scispace - formally typeset
Search or ask a question
Institution

University of Maryland, College Park

EducationCollege Park, Maryland, United States
About: University of Maryland, College Park is a education organization based out in College Park, Maryland, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 60446 authors who have published 155900 publications receiving 7273683 citations. The organization is also known as: The University of Maryland & College Park.


Papers
More filters
Journal ArticleDOI
B. P. Abbott1, R. Abbott1, T. D. Abbott2, Sheelu Abraham3  +1271 moreInstitutions (145)
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.

1,189 citations

Journal ArticleDOI
TL;DR: In this article, exact generalized Langevin equations are derived for arbitrarily nonlinear systems interacting with specially chosen heat baths, and an example is presented in which the Langevin equation is nonlinear but approximately Markovian.
Abstract: Exact generalized Langevin equations are derived for arbitrarily nonlinear systems interacting with specially chosen heat baths. An example is displayed in which the Langevin equation is nonlinear but approximately Markovian.

1,187 citations

Journal ArticleDOI
TL;DR: In this paper, it was shown that there is a supergravity contribution to the quantum level of the superconformal anomaly to the A terms proportional to the beta function of the corresponding Yukawa coupling.
Abstract: In models with dynamical supersymmetry breaking in the hidden sector, the gaugino masses in the observable sector have been believed to be extremely suppressed (below 1 keV), unless there is a gauge singlet in the hidden sector with specific couplings to the observable sector gauge multiplets. We point out that there is a pure supergravity contribution to gaugino masses at the quantum level arising from the superconformal anomaly. Our results are valid to all orders in perturbation theory and are related to the ‘exact’ beta functions for soft terms. There is also an anomaly contribution to the A terms proportional to the beta function of the corresponding Yukawa coupling. The gaugino masses are proportional to the corresponding gauge beta functions, and so do not satisfy the usual GUT relations.

1,185 citations

Proceedings ArticleDOI
06 Nov 2011
TL;DR: This paper presents one of the first studies on unsupervised domain adaptation in the context of object recognition, where data has been labeled only from the source domain (and therefore do not have correspondences between object categories across domains).
Abstract: Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.

1,185 citations

Journal ArticleDOI
M. G. Aartsen1, Markus Ackermann, Jenni Adams2, Juanan Aguilar3  +299 moreInstitutions (41)
TL;DR: Results from an analysis with a third year of data from the complete IceCube detector are consistent with the previously reported astrophysical flux in the 100 TeV-PeV range at the level of 10(-8) GeV cm-2 s-1 sr-1 per flavor and reject a purely atmospheric explanation for the combined three-year data at 5.7σ.
Abstract: A search for high-energy neutrinos interacting within the IceCube detector between 2010 and 2012 provided the first evidence for a high-energy neutrino flux of extraterrestrial origin. Results from an analysis using the same methods with a third year (2012-2013) of data from the complete IceCube detector are consistent with the previously reported astrophysical flux in the 100 TeV-PeV range at the level of 10(-8) GeV cm(-2) s(-1) sr(-1) per flavor and reject a purely atmospheric explanation for the combined three-year data at 5.7 sigma. The data are consistent with expectations for equal fluxes of all three neutrino flavors and with isotropic arrival directions, suggesting either numerous or spatially extended sources. The three-year data set, with a live time of 988 days, contains a total of 37 neutrino candidate events with deposited energies ranging from 30 to 2000 TeV. The 2000-TeV event is the highest-energy neutrino interaction ever observed.

1,183 citations


Authors

Showing all 60868 results

NameH-indexPapersCitations
Timothy M. Heckman170754141237
Donald G. Truhlar1651518157965
Tobin J. Marks1591621111604
Yongsun Kim1562588145619
Richard J. Davidson15660291414
Terrence J. Sejnowski155845117382
Roberto Romero1511516108321
Jongmin Lee1502257134772
Kevin J. Gaston15075085635
Bernard Moss14783076991
Steven L. Salzberg147407231756
Gregory R Snow1471704115677
Fabian Walter14699983016
Timothy P. Hughes14583191357
Marco Zanetti1451439104610
Network Information
Related Institutions (5)
University of Illinois at Urbana–Champaign
225.1K papers, 10.1M citations

98% related

University of California, Berkeley
265.6K papers, 16.8M citations

96% related

Cornell University
235.5K papers, 12.2M citations

95% related

Massachusetts Institute of Technology
268K papers, 18.2M citations

95% related

Princeton University
146.7K papers, 9.1M citations

95% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022754
20216,744
20207,208
20197,072
20186,716