Author
W-M. Yao
Other affiliations: University of California, Berkeley, Chonbuk National University
Bio: W-M. Yao is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Large Hadron Collider & Top quark. The author has an hindex of 112, co-authored 1113 publications receiving 101367 citations. Previous affiliations of W-M. Yao include University of California, Berkeley & Chonbuk National University.
Topics: Large Hadron Collider, Top quark, Higgs boson, Lepton, Tevatron
Papers published on a yearly basis
Papers
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TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.
12,798 citations
TL;DR: The review as discussed by the authors summarizes much of particle physics and cosmology using data from previous editions, plus 3,283 new measurements from 899 Japers, including the recently discovered Higgs boson, leptons, quarks, mesons and baryons.
Abstract: The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,283 new measurements from 899 Japers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as heavy neutrinos, supersymmetric and technicolor particles, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Supersymmetry, Extra Dimensions, Particle Detectors, Probability, and Statistics. Among the 112 reviews are many that are new or heavily revised including those on: Dark Energy, Higgs Boson Physics, Electroweak Model, Neutrino Cross Section Measurements, Monte Carlo Neutrino Generators, Top Quark, Dark Matter, Dynamical Electroweak Symmetry Breaking, Accelerator Physics of Colliders, High-Energy Collider Parameters, Big Bang Nucleosynthesis, Astrophysical Constants and Cosmological Parameters.
7,337 citations
TL;DR: The complete review as discussed by the authors is published online on the website of the Particle Data Group (http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as thePDG Book.
Abstract: The complete Review(both volumes) is published online on the website of the Particle Data Group(http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as thePDG Book. AParticle Physics Bookletwith the Summary Tables and essential tables, figures, and equations from selected review articles is also available.
6,464 citations
Tohoku University1, University of Zurich2, Lawrence Berkeley National Laboratory3, Stanford University4, College of William & Mary5, University of Urbino6, CERN7, Budker Institute of Nuclear Physics8, University of California, Irvine9, Cornell University10, Argonne National Laboratory11, ETH Zurich12, Tata Institute of Fundamental Research13, Hillsdale College14, Spanish National Research Council15, Ohio State University16, University of Notre Dame17, Kent State University18, University of California, San Diego19, University of California, Berkeley20, University of Minnesota21, University of Alabama22, University of Helsinki23, Los Alamos National Laboratory24, California Institute of Technology25, George Washington University26, Syracuse University27, Lawrence Livermore National Laboratory28, Oklahoma State University–Stillwater29, University of Washington30, Max Planck Society31, Boston University32, University of California, Los Angeles33, Royal Holloway, University of London34, Université Paris-Saclay35, University of Pennsylvania36, University of Illinois at Urbana–Champaign37, University of Bristol38, University of Tokyo39, University of Delaware40, Carnegie Mellon University41, University of California, Santa Cruz42, Karlsruhe Institute of Technology43, Heidelberg University44, Florida State University45, University of Mainz46, University of Edinburgh47, Brookhaven National Laboratory48, Durham University49, University of Lausanne50, Massachusetts Institute of Technology51, University of Southampton52, Nagoya University53, University of Oxford54, Northwestern University55, University of British Columbia56, Columbia University57, Lund University58, University of Sheffield59, University of California, Santa Barbara60, Iowa State University61, University of Alberta62, University of Cambridge63
TL;DR: This biennial Review summarizes much of Particle Physics using data from previous editions, plus 2205 new measurements from 667 papers, and features expanded coverage of CP violation in B mesons and of neutrino oscillations.
Abstract: This biennial Review summarizes much of Particle Physics. Using data from previous editions, plus 2205 new measurements from 667 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. This edition features expanded coverage of CP violation in B mesons and of neutrino oscillations. For the first time we cover searches for evidence of extra dimensions (both in the particle listings and in a new review). Another new review is on Grand Unified Theories. A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. All tables, listings, and reviews (and errata) are also available on the Particle Data Group website: http://pdg.lbl.gov.
5,143 citations
Tohoku University1, University of Zurich2, Lawrence Berkeley National Laboratory3, Stanford University4, College of William & Mary5, University of Genoa6, University of Urbino7, CERN8, Budker Institute of Nuclear Physics9, University of California, Irvine10, Cornell University11, Argonne National Laboratory12, ETH Zurich13, Tata Institute of Fundamental Research14, Hillsdale College15, Spanish National Research Council16, Ohio State University17, University of Notre Dame18, Kent State University19, University of California, San Diego20, University of California, Berkeley21, University of Minnesota22, University of Alabama23, University of Helsinki24, Los Alamos National Laboratory25, California Institute of Technology26, George Washington University27, Syracuse University28, Lawrence Livermore National Laboratory29, Oklahoma State University–Stillwater30, University of Washington31, Max Planck Society32, Boston University33, University of California, Los Angeles34, Royal Holloway, University of London35, Université Paris-Saclay36, Fermilab37, University of Pennsylvania38, University of Illinois at Urbana–Champaign39, University of Bristol40, University of Tokyo41, University of Delaware42, Carnegie Mellon University43, University of California, Santa Cruz44, Karlsruhe Institute of Technology45, Heidelberg University46, Florida State University47, Carleton University48, University of Mainz49, University of Edinburgh50, Brookhaven National Laboratory51, Durham University52, University of Lausanne53, Massachusetts Institute of Technology54, University of Southampton55, Nagoya University56, University of Oxford57, Northwestern University58, University of British Columbia59, Columbia University60, Lund University61, University of Sheffield62, University of California, Santa Barbara63, Iowa State University64, University of Alberta65, University of Cambridge66
TL;DR: The Particle Data Group's biennial review as mentioned in this paper summarizes much of particle physics, using data from previous editions, plus 2658 new measurements from 644 papers, and lists, evaluates, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons.
Abstract: This biennial Review summarizes much of particle physics. Using data from previous editions, plus 2658 new measurements from 644 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. Among the 112 reviews are many that are new or heavily revised including those on Heavy-Quark and Soft-Collinear Effective Theory, Neutrino Cross Section Measurements, Monte Carlo Event Generators, Lattice QCD, Heavy Quarkonium Spectroscopy, Top Quark, Dark Matter, V-cb & V-ub, Quantum Chromodynamics, High-Energy Collider Parameters, Astrophysical Constants, Cosmological Parameters, and Dark Matter. A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. All tables, listings, and reviews (and errata) are also available on the Particle Data Group website: http://pdg.lbl.gov.
4,465 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
[...]
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.
12,798 citations
TL;DR: In this article, the authors present a cosmological analysis based on full-mission Planck observations of temperature and polarization anisotropies of the cosmic microwave background (CMB) radiation.
Abstract: This paper presents cosmological results based on full-mission Planck observations of temperature and polarization anisotropies of the cosmic microwave background (CMB) radiation. Our results are in very good agreement with the 2013 analysis of the Planck nominal-mission temperature data, but with increased precision. The temperature and polarization power spectra are consistent with the standard spatially-flat 6-parameter ΛCDM cosmology with a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper). From the Planck temperature data combined with Planck lensing, for this cosmology we find a Hubble constant, H0 = (67.8 ± 0.9) km s-1Mpc-1, a matter density parameter Ωm = 0.308 ± 0.012, and a tilted scalar spectral index with ns = 0.968 ± 0.006, consistent with the 2013 analysis. Note that in this abstract we quote 68% confidence limits on measured parameters and 95% upper limits on other parameters. We present the first results of polarization measurements with the Low Frequency Instrument at large angular scales. Combined with the Planck temperature and lensing data, these measurements give a reionization optical depth of τ = 0.066 ± 0.016, corresponding to a reionization redshift of . These results are consistent with those from WMAP polarization measurements cleaned for dust emission using 353-GHz polarization maps from the High Frequency Instrument. We find no evidence for any departure from base ΛCDM in the neutrino sector of the theory; for example, combining Planck observations with other astrophysical data we find Neff = 3.15 ± 0.23 for the effective number of relativistic degrees of freedom, consistent with the value Neff = 3.046 of the Standard Model of particle physics. The sum of neutrino masses is constrained to ∑ mν < 0.23 eV. The spatial curvature of our Universe is found to be very close to zero, with | ΩK | < 0.005. Adding a tensor component as a single-parameter extension to base ΛCDM we find an upper limit on the tensor-to-scalar ratio of r0.002< 0.11, consistent with the Planck 2013 results and consistent with the B-mode polarization constraints from a joint analysis of BICEP2, Keck Array, and Planck (BKP) data. Adding the BKP B-mode data to our analysis leads to a tighter constraint of r0.002 < 0.09 and disfavours inflationarymodels with a V(φ) ∝ φ2 potential. The addition of Planck polarization data leads to strong constraints on deviations from a purely adiabatic spectrum of fluctuations. We find no evidence for any contribution from isocurvature perturbations or from cosmic defects. Combining Planck data with other astrophysical data, including Type Ia supernovae, the equation of state of dark energy is constrained to w = −1.006 ± 0.045, consistent with the expected value for a cosmological constant. The standard big bang nucleosynthesis predictions for the helium and deuterium abundances for the best-fit Planck base ΛCDM cosmology are in excellent agreement with observations. We also constraints on annihilating dark matter and on possible deviations from the standard recombination history. In neither case do we find no evidence for new physics. The Planck results for base ΛCDM are in good agreement with baryon acoustic oscillation data and with the JLA sample of Type Ia supernovae. However, as in the 2013 analysis, the amplitude of the fluctuation spectrum is found to be higher than inferred from some analyses of rich cluster counts and weak gravitational lensing. We show that these tensions cannot easily be resolved with simple modifications of the base ΛCDM cosmology. Apart from these tensions, the base ΛCDM cosmology provides an excellent description of the Planck CMB observations and many other astrophysical data sets.
10,728 citations
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TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These
9,929 citations