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We report a measurement of the cross section for the process e+e−→π+π−J/ψ around the X(3872) mass in search for the direct formation of e+e−→X(3872) through the two-photon fusion process. No enhancement of the cross section is observed at the X(3872) peak and an upper limit on the product of electronic width and branching fraction of X(3872)→π+π−J/ψ is determined to be Γee×B(X(3872)→π+π−J/ψ)<7.5×10−3eV at 90% confidence level under an assumption of total width of 1.19±0.21 MeV. This is an improvement of a factor of about 17 compared to the previous limit. Furthermore, using the latest result of B(X(3872)→π+π−J/ψ), an upper limit on the electronic width Γee of X(3872) is obtained to be <0.32eV at the 90% confidence level.
A measurement of the 𝐶𝑃-even fraction of the decay 𝐷0→𝜋+𝜋−𝜋+𝜋− is performed with a quantum-correlated 𝜓(3770)→𝐷¯𝐷 data sample collected by the BESIII experiment, corresponding to an integrated luminosity of 2.93 fb−1. Using a combination of 𝐶𝑃 eigenstates, 𝐷→𝜋+𝜋−𝜋0 and 𝐷→𝐾0𝑆,𝐿𝜋+𝜋− as tagging modes, the 𝐶𝑃-even fraction is measured to be 𝐹4𝜋+=0.735±0.015±0.005, where the first uncertainty is statistical and the second is systematic. This is the most precise determination of this quantity to date. It provides valuable model-independent input for the measurement of the angle 𝛾 of the Cabibbo-Kobayashi-Maskawa matrix with 𝐵±→𝐷𝐾± decays, and for time-dependent studies of 𝐶𝑃 violation and mixing in the 𝐷0−¯𝐷0 system.
A measurement of the CP-even fraction of the decay D0→π+π−π+π− is performed with a quantum-correlated ψ(3770)→DD¯ data sample collected by the BESIII experiment, corresponding to an integrated luminosity of 2.93 fb−1. Using a combination of CP eigenstates, D→π+π−π0 and D→K0S,Lπ+π− as tagging modes, the CP-even fraction is measured to be F4π+=0.735±0.015±0.005, where the first uncertainty is statistical and the second is systematic. This is the most precise determination of this quantity to date. It provides valuable model-independent input for the measurement of the CKM angle γ with B±→DK± decays, and for time-dependent studies of CP violation and mixing in the D0-D¯0 system.
Luminosities and energies of e⁺e⁻ collision data taken between √s=4.61 GeV and 4.95 GeV at BESIII
(2022)
From December 2019 to June 2021, the BESIII experiment collected about 5.85 fb−1 of data at center-of-mass energies between 4.61 GeV and 4.95 GeV. This is the highest collision energy BEPCII has reached so far. The accumulated e+e− annihilation data samples are useful for studying charmonium(-like) states and charmed-hadron decays. By adopting a novel method of analyzing the production of Λ+cΛ¯−c pairs in e+e− annihilation, the center-of-mass energies are measured with a precision of ∼0.6 MeV. Integrated luminosities are measured with a precision of better than 1\% by analyzing the events of large-angle Bhabha scattering. These measurements provide important inputs to the analyses based on these data samples.
Using an 𝑒+𝑒− collision data sample with a total integrated luminosity of 3.19 fb−1 collected with the BESIII detector at a center-of-mass energy of 4.178 GeV, the branching fraction of the inclusive decay of the 𝐷+𝑠 meson to final states including at least three charged pions is measured for the first time to be ℬ(𝐷+𝑠→𝜋+𝜋+𝜋−𝑋)=(32.81±0.35stat±0.63syst)%. In this measurement the charged pions from 𝐾0𝑆 meson decays are excluded. The partial branching fractions of 𝐷+𝑠→𝜋+𝜋+𝜋−𝑋 are also measured as a function of the 𝜋+𝜋+𝜋− invariant mass.
Using a sample of 106 million 𝜓(3686) decays, 𝜓(3686)→𝛾𝜒𝑐𝐽(𝐽=0,1,2) and 𝜓(3686)→𝛾𝜒𝑐𝐽,𝜒𝑐𝐽→𝛾𝐽/𝜓(𝐽=1,2) events are utilized to study inclusive 𝜒𝑐𝐽→anything, 𝜒𝑐𝐽→hadrons, and 𝐽/𝜓→anything distributions, including distributions of the number of charged tracks, electromagnetic calorimeter showers, and 𝜋0s, and to compare them with distributions obtained from the BESIII Monte Carlo simulation. Information from each Monte Carlo simulated decay event is used to construct matrices connecting the detected distributions to the input predetection “produced” distributions. Assuming these matrices also apply to data, they are used to predict the analogous produced distributions of the decay events. Using these, the charged particle multiplicities are compared with results from MARK I. Further, comparison of the distributions of the number of photons in data with those in Monte Carlo simulation indicates that G-parity conservation should be taken into consideration in the simulation.
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.