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Author |
CDF Collaboration (Aaltonen, T. et al); Cabrera, S. |
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Title |
Observation of the Baryonic Flavor-Changing Neutral Current Decay Lambda(0)(b) -> Lambda mu(+)mu(-) |
Type |
Journal Article |
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Year |
2011 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
107 |
Issue |
20 |
Pages  |
201802 - 8pp |
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Abstract |
We report the first observation of the baryonic flavor-changing neutral current decay Lambda(0)(b) -> Lambda mu(+)mu(-) with 24 signal events and a statistical significance of 5.8 Gaussian standard deviations. This measurement uses a p (p) over bar collisions data sample corresponding to 6.8 fb(-1) at root s = 1.96 TeV collected by the CDF II detector at the Tevatron collider. The total and differential branching ratios for Lambda(0)(b) -> Lambda mu(+)mu(-) are measured. We find B(Lambda(0)(b) -> Lambda mu(+)mu(-)) = [1.73 +/- 0.42(stat) +/- (syst)] x 10(-6). We also report the first measurement of the differential branching ratio of B(s)(0) -> phi mu(+)mu(-), using 49 signal events. In addition, we report branching ratios for B(+) -> K(+)mu(+)mu(-), B(0) -> K(0)mu(+)mu(-), and B -> K*(892)mu(+)mu(-) decays. |
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Address |
[Aaltonen, T; Brucken, E; Devoto, F; Mehtala, P; Orava, R] Univ Helsinki, Div High Energy Phys, Dept Phys, FIN-00014 Helsinki, Finland |
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Amer Physical Soc |
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English |
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0031-9007 |
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Notes |
WOS:000297130800003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ elepoucu @ |
Serial |
803 |
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ATLAS Collaboration (Aad, G. et al); Cabrera Urban, S.; Castillo Gimenez, V.; Costa, M.J.; Fassi, F.; Ferrer, A.; Fiorini, L.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gonzalez de la Hoz, S.; Hernandez Jimenez, Y.; Higon-Rodriguez, E.; Irles Quiles, A.; Kaci, M.; King, M.; Lacasta, C.; Lacuesta, V.R.; March, L.; Marti-Garcia, S.; Miñano, M.; Mitsou, V.A.; Moles-Valls, R.; Moreno Llacer, M.; Oliver Garcia, E.; Pedraza Lopez, S.; Perez Garcia-Estañ, M.T.; Romero Adam, E.; Ros, E.; Salt, J.; Sanchez Martinez, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valladolid Gallego, E.; Valls Ferrer, J.A.; Villaplana Perez, M.; Vos, M. |
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Title |
Search for Invisible Decays of a Higgs Boson Produced in Association with a Z Boson in ATLAS |
Type |
Journal Article |
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Year |
2014 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
112 |
Issue |
20 |
Pages |
201802 - 19pp |
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Keywords |
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Abstract |
A search for evidence of invisible-particle decay modes of a Higgs boson produced in association with a Z boson at the Large Hadron Collider is presented. No deviation from the standard model expectation is observed in 4.5 fb(-1) (20.3 fb(-1)) of 7 (8) TeV pp collision data collected by the ATLAS experiment. Assuming the standard model rate for ZH production, an upper limit of 75%, at the 95% confidence level is set on the branching ratio to invisible-particle decay modes of the Higgs boson at a mass of 125.5 GeV. The limit on the branching ratio is also interpreted in terms of an upper limit on the allowed dark matter-nucleon scattering cross section within a Higgs-portal dark matter scenario. Within the constraints of such a scenario, the results presented in this Letter provide the strongest available limits for low-mass dark matter candidates. Limits are also set on an additional neutral Higgs boson, in the mass range 110 < m(H) < 400 GeV, produced in association with a Z boson and decaying to invisible particles. |
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[Jackson, P.; Soni, N.; White, M. J.] Univ Adelaide, Sch Chem & Phys, Adelaide, SA, Australia |
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Amer Physical Soc |
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English |
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0031-9007 |
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Notes |
WOS:000339554800003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1890 |
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Author |
CDF Collaboration (Aaltonen, T. et al); Cabrera, S. |
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Title |
Measurement of the W+W- Production Cross Section and Search for Anomalous WW gamma and WWZ Couplings in p(p)over-bar Collisions at root s 1.96 TeV |
Type |
Journal Article |
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Year |
2010 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
104 |
Issue |
20 |
Pages |
201801 - 8pp |
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Abstract |
This Letter describes the current most precise measurement of the W boson pair production cross section and most sensitive test of anomalous WW gamma and WWZ couplings in p (p) over bar collisions at a center-of-mass energy of 1.96 TeV. The WW candidates are reconstructed from decays containing two charged leptons and two neutrinos. Using data collected by the CDF II detector from 3: 6 fb(-1) of integrated luminosity, a total of 654 candidate events are observed with an expected background of 320 +/- 47 events. The measured cross section is sigma(p (p) over bar -> W+W- +X) = 12.1 +/- 0.9(stat)(-1.4)(+1.6)(syst) pb, which is in good agreement with the standard model prediction. The same data sample is used to place constraints on anomalous WW gamma and WWZ couplings. |
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Address |
[Aaltonen, T.; Mehtala, P.; Orava, R.; Osterberg, K.; Saarikko, H.; van Remortel, N.] Univ Helsinki, Div High Energy Phys, Dept Phys, FIN-00014 Helsinki, Finland |
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Amer Physical Soc |
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English |
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0031-9007 |
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Notes |
ISI:000277945900011 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
285 |
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Author |
BABAR Collaboration (Lees, J.P. et al); Martinez-Vidal, F.; Oyanguren, A.; Villanueva-Perez, P. |
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Title |
Search for a Dark Photon in e(+)e(-) Collisions at BABAR |
Type |
Journal Article |
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Year |
2014 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
113 |
Issue |
20 |
Pages |
201801 - 8pp |
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Keywords |
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Abstract |
Dark sectors charged under a new Abelian interaction have recently received much attention in the context of dark matter models. These models introduce a light new mediator, the so-called dark photon (A'), connecting the dark sector to the standard model. We present a search for a dark photon in the reaction e(+)e(-) ->gamma A', A' -> e(+)e(-), mu(+) mu(-) using 514 fb(-1) of data collected with the BABAR detector. We observe no statistically significant deviations from the standard model predictions, and we set 90% confidence level upper limits on the mixing strength between the photon and dark photon at the level of 10(-4) – 10(-3) for dark photon masses in the range 0.02-10.2 GeV. We further constrain the range of the parameter space favored by interpretations of the discrepancy between the calculated and measured anomalous magnetic moment of the muon. |
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Address |
[Lees, J. P.; Poireau, V.; Tisserand, V.] Univ Savoie, Lab Annecy le Vieux Phys Particules LAPP, IN2P3, CNRS, F-74941 Annecy Le Vieux, France |
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Publisher |
Amer Physical Soc |
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English |
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0031-9007 |
ISBN |
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Notes |
WOS:000344919200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2004 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
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Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Publisher |
Amer Medical Assoc |
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English |
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Edition |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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