A. Carnes
Impact in
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- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Particle Detector Development and Performance
- Neutrino Physics Research
- Economics and Econometrics top 2%
- Energy, Environment, Economic Growth
Papers in
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- Particle physics theoretical and experimental studies 550
- High-Energy Particle Collisions Research 94
- Particle Detector Development and Performance 18
- Quantum Chromodynamics and Particle Interactions 14
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- Computational Physics and Python Applications 1
- Co-authors
- Darin Acosta (5 shared papers)Sergei Gleyzer (5 shared papers)A. Madorsky (3 shared papers)Jamal Rorie (2 shared papers)Wei Shi (2 shared papers)Ivan Furic (2 shared papers)Bobby Scurlock (3 shared papers)Andrew Brinkerhoff (3 shared papers)
- Journals
- Journal of High Energy Physics (202 papers)Physics Letters B (116 papers)The European Physical Journal C (88 papers)Physical review. D (58 papers)Physical Review Letters (51 papers)
- Partner nations
- AustriaGermanySwitzerland
In The Last Decade
A. Carnes
607 papers receiving 22.1k citations
A. Carnes's Hit Papers
Peers
Comparison fields: 5 of 105
- Nuclear and High Energy Physics 27.5k
- Economics and Econometrics 420
- Astronomy and Astrophysics 271
- Statistical and Nonlinear Physics 106
- Hardware and Architecture 61
Countries citing papers authored by A. Carnes
This map shows the geographic impact of A. Carnes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. Carnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Carnes more than expected).
Fields of papers citing papers by A. Carnes
This network shows the impact of papers produced by A. Carnes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. Carnes. The network helps show where A. Carnes may publish in the future.
Co-authors
The 25 scholars most cited alongside A. Carnes, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 644 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Combined Measurement of the Higgs Boson Mass in p p Collisions at s = 7 and 8 TeV with the ATLAS and CMS Experiments Hit paper breakdown → | 2015 | 965 |
| 2 | Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC pp collision data at s = 7 $$ \sqrt{s}=7 $$ and 8 TeV Hit paper breakdown → | 2016 | 848 |
| 3 | Particle-flow reconstruction and global event description with the CMS detector Hit paper breakdown → | 2017 | 736 |
| 4 | The CMS trigger system Hit paper breakdown → | 2017 | 642 |
| 5 | Event generator tunes obtained from underlying event and multiparton scattering measurements Hit paper breakdown → | 2016 | 536 |
| 6 | Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV Hit paper breakdown → | 2017 | 512 |
| 7 | Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV Hit paper breakdown → | 2018 | 483 |
| 8 | Measurement of the top quark mass using single top quark events in proton-proton collisions at s√=8 \n TeV Hit paper breakdown → | 2017 | 446 |
| 9 | Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √ s =13 TeV Hit paper breakdown → | 2018 | 398 |
| 10 | Combined measurements of Higgs boson couplings in proton–proton collisions at $$\sqrt{s}=13\,\text {Te}\text {V} $$ Hit paper breakdown → | 2019 | 359 |
| 11 | Evidence for collectivity in pp collisions at the LHC Hit paper breakdown → | 2016 | 304 |
| 12 | Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements Hit paper breakdown → | 2020 | 299 |
| 13 | 2018 | 235 | |
| 14 | Performance of the CMS Level-1 trigger in proton-proton collisions at √s = 13 TeV Hit paper breakdown → | 2020 | 216 |
| 15 | 2019 | 214 | |
| 16 | 2019 | 198 | |
| 17 | 2017 | 175 | |
| 18 | 2018 | 171 | |
| 19 | 2017 | 168 | |
| 20 | 2015 | 166 |
About A. Carnes
A. Carnes is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 644 papers that have together received 28.5k indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (550 papers), High-Energy Particle Collisions Research (94 papers), Particle Detector Development and Performance (18 papers), Quantum Chromodynamics and Particle Interactions (14 papers) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (27.5k citations), Economics and Econometrics (420 citations), Astronomy and Astrophysics (271 citations), Statistical and Nonlinear Physics (106 citations) and Hardware and Architecture (61 citations). A. Carnes has collaborated with scholars based in Austria, Germany and Switzerland. Frequent co-authors include Darin Acosta, Sergei Gleyzer, A. Madorsky, Jamal Rorie, Wei Shi, Ivan Furic, Bobby Scurlock, Andrew Brinkerhoff, J. F. Low and Elena Busch. Their work appears in journals such as Journal of High Energy Physics, Physics Letters B, The European Physical Journal C, Physical review. D and Physical Review Letters.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.