A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯

Bacillus thuringiensis isolates entomopathogenic for Culex quinquefasciatus (Diptera: Culicidae) and Anticarsia gemmatalis (Lepidoptera: Noctuidae)

Samples of the Bacillus thuringiensis (Bt) were collected from soil and insects. Eight isolates were selected from rural soil, 15 from urban soil and 11 from insects. These were evaluated for entomopathogenicity against larvae of Anticarsia gemmatalis and Culex quinquefasciatus. The pathogenicity tests showed that a higher percentage of isolates were active against A. gemmatalis (60%) compared to C. quinquefasciatus (31%). Probit analysis (LC50) indicated that against A. gemmatalis four of the isolates presented values similar to the reference strain against A. gemmatalis, while against C. quinquefasciatus one isolate showed an LC50 similar to the reference strain (IPS-82). SDS-PAGE characterisation of two isolates showed a 27 kDa protein fraction related to the Bt subspecies israelensis cytolytic toxin (cyt) gene. One 130 kDa protein, possibly related to the Bt crystal inclusions (cry1) gene, was identified in the other two isolates, which were more toxic for lepidoptera; another isolate presented a protein of 100 kDa. Some new local Bt isolates had similar LC50 probit values to the reference strains

Measurement of b jet shapes in proton-proton collisions at s=5.02 TeV

We present the first study of charged-hadron production associated with jets originating from b quarks in proton-proton collisions at a center-of-mass energy of 5.02 TeV. The data sample used in this study was collected with the CMS detector at the CERN LHC and corresponds to an integrated luminosity of 27.4 pb(-1). To characterize the jet substructure, the differential jet shapes, defined as the normalized transverse momentum distribution of charged hadrons as a function of angular distance from the jet axis, are measured for b jets. In addition to the jet shapes, the per-jet yields of charged particles associated with b jets are also quantified, again as a function of the angular distance with respect to the jet axis. Extracted jet shape and particle yield distributions for b jets are compared with results for inclusive jets, as well as with the predictions from the pythia and herwig++ event generators

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯