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Author |
El-Neaj, Y.A. et al; Bernabeu, J. |
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Title |
AEDGE: Atomic Experiment for Dark Matter and Gravity Exploration in Space |
Type |
Journal Article |
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Year |
2020 |
Publication |
EPJ Quantum Technology |
Abbreviated Journal |
EPJ Quantum Technol. |
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Volume |
7 |
Issue |
1 |
Pages |
6 - 27pp |
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Abstract |
We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity. KCL-PH-TH/2019-65, CERN-TH-2019-126 |
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Address |
[El-Neaj, Yousef Abou] Harvard Univ, Phys Dept, Cambridge, MA 02138 USA, Email: o.buchmueller@imperial.ac.uk |
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Springeropen |
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English |
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ISSN  |
2662-4400 |
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Conference |
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Notes |
WOS:000519468200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4325 |
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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 |
Type |
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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Keywords |
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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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Amer Medical Assoc |
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English |
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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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Author |
LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. |
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Title |
Reinterpretation of LHC results for new physics: status and recommendations after run 2 |
Type |
Journal Article |
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Year |
2020 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
9 |
Issue |
2 |
Pages |
022 - 45pp |
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Keywords |
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Abstract |
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data. |
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Address |
[Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.ac.uk; |
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Scipost Foundation |
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English |
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ISSN |
2542-4653 |
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Notes |
WOS:000573102600007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4547 |
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Author |
ATLAS Collaboration (Aaboud, M. et al); Alvarez Piqueras, D.; Aparisi Pozo, J.A.; Bailey, A.J.; Barranco Navarro, L.; Cabrera Urban, S.; Castillo, F.L.; Castillo Gimenez, V.; Cerda Alberich, L.; Costa, M.J.; Escobar, C.; Estrada Pastor, O.; Ferrer, A.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gonzalez de la Hoz, S.; Higon-Rodriguez, E.; Jimenez Pena, J.; Lacasta, C.; Lozano Bahilo, J.J.; Madaffari, D.; Mamuzic, J.; Marti-Garcia, S.; Melini, D.; Miñano, M.; Mitsou, V.A.; Rodriguez Bosca, S.; Rodriguez Rodriguez, D.; Ruiz-Martinez, A.; Salt, J.; Santra, A.; Soldevila, U.; Sanchez, J.; Valero, A.; Valls Ferrer, J.A.; Vos, M. |
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Title |
Combined measurements of Higgs boson production and decay using up to 80 fb(-1) of proton-proton collision data at root S=13 TeV collected with the ATLAS experiment |
Type |
Journal Article |
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Year |
2020 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
101 |
Issue |
1 |
Pages |
012002 - 48pp |
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Keywords |
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Abstract |
Combined measurements of Higgs boson production cross sections and branching fractions arc presented. The combination is based on the analyses of the Higgs boson decay modes H -> gamma gamma, ZZ*, WW*, tau tau, b (b) over bar, μmu, searches for decays into invisible final states, and on measurements of off-shell Higgs boson production. Up to 79.8 fb(-1) of proton-proton collision data collected at root S = 13 TeV with the ATLAS detector are used. Results are presented for the gluon-gluon fusion and vector-boson fusion processes, and for associated production with vector bosons or top-quarks. The global signal strength is determined to be μ= 1.11(-0.08)(+0.09). The combined measurement yields an observed (expected) significance for the vector-boson fusion production process of 6.5 sigma (5.3 sigma). Measurements in kinematic regions defined within the simplified template cross section framework are also shown. The results are interpreted in terms of modifiers applied to the Standard Model couplings of the Higgs boson to other particles, and are used to set exclusion limits on parameters in two-Higgs-doublet models and in the simplified minimal supersynunetric Standard Model. No significant deviations from Standard Model predictions are observed. |
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Address |
[Banerjee, S.; Dang, N. P.; Duvnjak, D.; Jackson, P.; Oliver, J. L.; Petridis, A.; Qureshi, A.; Sharma, A. S.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
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Publisher |
Amer Physical Soc |
Place of Publication |
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English |
Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2470-0010 |
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Area |
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Conference |
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Notes |
WOS:000505485600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4245 |
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Author |
Sakai, S.; Liang, W.H.; Toledo, G.; Oset, E. |
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Title |
J/psi -> gamma pi pi, gamma pi(0)eta reactions and the f(0)(980) and a(0)(980) resonances |
Type |
Journal Article |
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Year |
2020 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
101 |
Issue |
1 |
Pages |
014005 - 9pp |
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Keywords |
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Abstract |
We study the J/psi -> gamma pi(+)pi(-), gamma pi(0)eta reactions from the perspective that they come from the J/psi -> phi(omega)pi(+)pi(-), rho(0)pi(0)eta reactions, where the rho(0), psi, and phi get converted into a photon via vector meson dominance. Using models successfully used previously to study the J/psi -> omega(phi)pi pi reactions, we make determinations of the invariant mass distributions for pi(+)pi(-) in the regions of the f(0)(500), f(0)(980), and for pi(0)eta in the region of the a(0)(980). The integrated differential widths lead to branching ratios below present upper bounds, but they are sufficiently large for future check in updated facilities. |
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Address |
[Sakai, S.; Liang, Wei-Hong; Oset, E.] Guangxi Normal Univ, Dept Phys, Guilin 541004, Peoples R China, Email: shsakai@itp.ac.cn; |
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Publisher |
Amer Physical Soc |
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English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2470-0010 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000506592500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4248 |
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