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Register a new userLuminosity measurements for the R scan experiment at BESIII
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By analyzing the large-angle Bhabha scattering events $e^{+}e^{-}$ $to$ ($gamma$)$e^{+}e^{-}$ and diphoton events $e^{+}e^{-}$ $to$ $gammagamma$ for the data sets collected at center-of-mass (c.m.) energies between 2.2324 and 4.5900 GeV (131 energy points in total) with the upgraded Beijing Spectrometer (BESIII) at the Beijing Electron-Positron Collider (BEPCII), the integrated luminosities have been measured at the different c.m. energies, individually. The results are the important inputs for R value and $J/psi$ resonance parameter measurements.
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With the ability to run above 4~GeV, the BESIII experiment located in the Beijing Electron Positron Collider (BEPCII), has becoming a pioneer in searching and studying charmoniumlike states ($XYZ$ particles). In 2013, BESIII Collaboration discovered a charged charmoniumlike state $Z_c(3900)$, which is confirmed immediately experimentally, and provides the best candidate for a four quark state by now. Continuous studies by BESIII Collaboration show new decay behavior of $Z_c(3900)$, and there are possible partner particle $Z_c(4020)/Z_c(4025)$ existing. By scanning above 4~GeV, BESIII also reveals the potential connection between $Y(4260)$ and $X(3872)$ for the first time, which may help us understand $XYZ$ particles in a new sight.
A fast physics analysis framework has been developed based on SNiPER to process the increasingly large data sample collected by BESIII. In this framework, a reconstructed event data model with SmartRef is designed to improve the speed of Input/Output operations, and necessary physics analysis tools are migrated from BOSS to SNiPER. A real physics analysis $e^{+}e^{-} rightarrow pi^{+}pi^{-}J/psi$ is used to test the new framework, and achieves a factor of 10.3 improvement in Input/Output speed compared to BOSS. Further tests show that the improvement is mainly attributed to the new reconstructed event data model and the lazy-loading functionality provided by SmartRef.
We present the development and validation of a new multivariate $b$ jet identification algorithm ($b$ tagger) used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing $B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jets neural network output value in $Z+1$ jet and $tbar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.
This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of $6$ GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two gammas from 71% to 89% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with $langle E_ u rangle$ between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current $ u_e$ events arising from $ u_{mu} rightarrow u_{e}$ appearance.
These lectures describe several topics in statistical data analysis as used in High Energy Physics. They focus on areas most relevant to analyses at the LHC that search for new physical phenomena, including statistical tests for discovery and exclusion limits. Particular attention is payed to the treatment of systematic uncertainties through nuisance parameters.
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