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Register a new userA machine learning global analysis approach to enhance the precision of the Higgs decay branching fractions in electron-positron colliders
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Several high energy $e^{+}e^{-}$ colliders are proposed as Higgs factories by the international high energy physics community. One of the most important goals of these projects is to study the Higgs properties, such as its couplings, mass, width, and production rate, with unprecedented precision. Precision studies of the Higgs boson should be the priority and drive the design and optimization of detectors. A global analysis approach based on the multinomial distribution and Machine Learning techniques is proposed to realize an ``end-to-end analysis and to enhance the precision of all accessible decay branching fractions of the Higgs significantly. A proof-of-principle Monte Carlo simulation study is performed to show the feasibility. This approach shows that the statistical uncertainties of all branching fractions are proportional to a single parameter, which can be used as a metric to optimize the detector design, reconstruction algorithms, and data analyses. In the Higgs factories, the global analysis approach is valuable both to the Higgs measurements and detector R & D, because it has the potential for superior precision and makes detector optimization single-purpose.
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Using 482 pb$^{-1}$ of data taken at $sqrt{s}=4.009$ GeV, we measure the branching fractions of the decays of $D^{*0}$ into $D^0pi^0$ and $D^0gamma$ to be $BR(D^{*0} to D^0pi^0)=(65.5pm 0.8pm 0.5)%$ and $BR(D^{*0} to D^0gamma)=(34.5pm 0.8pm 0.5)%$ respectively, by assuming that the $D^{*0}$ decays only into these two modes. The ratio of the two branching fractions is $BR(D^{*0} to D^0pi^0)/BR(D^{*0} to D^0gamma) =1.90pm 0.07pm 0.05$, which is independent of the assumption made above. The first uncertainties are statistical and the second ones systematic. The precision is improved by a factor of three compared to the present world average values.
An electron-positron linear collider in the energy range between 500 and 1000 GeV is of crucial importance to precisely test the Standard Model and to explore the physics beyond it. The physics program is complementary to that of the Large Hadron Collider. Some of the main physics goals and the expected accuracies of the anticipated measurements at such a linear collider are discussed. A short review of the different collider designs presently under study is given including possible upgrade paths to the multi-TeV region. Finally a framework is presented within which the realisation of such a project could be achieved as a global international project.
Based on a sample of $(1310.6 pm 7.0) times 10^6~J/psi$ events collected with the BESIII detector, we present measurements of $J/psi$ and $eta^prime$ absolute branching fractions using the process $J/psirightarrowgammaeta^prime$. By analyzing events where the radiative photon converts into an $e^+e^-$ pair, the branching fraction for $J/psi rightarrow gamma eta^prime$ is measured to be $(5.27pm0.03pm0.05)times 10^{-3}$. The absolute branching fractions of the five dominant decay channels of the $eta^prime$ are then measured independently for the first time and are determined to be $mathcal{B}(eta^prime rightarrow gamma pi^{+} pi^{-})$ = (29.90$pm$0.03$pm$0.55)%, $mathcal{B}(eta^prime rightarrow eta pi^{+} pi^{-})$ = (41.24$pm$0.08$pm$1.24)%, $mathcal{B}(eta^prime rightarrow eta pi^{0} pi^{0})$ = (21.36$pm$0.10$pm$0.92)%, $mathcal{B}(eta^prime rightarrow gamma omega)$ = (2.489$pm$0.018$pm$0.074)%, and $mathcal{B}(eta^prime rightarrow gamma gamma)$ = (2.331$pm$0.012$pm$0.035)%, where the first uncertainties are statistical and the second systematic.
Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. We demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any labels from the training data, only using appropriate questioning of the input neural network.
This paper reports the results of an experiment in high energy physics: using the power of the crowd to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document
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