Radiative Penguin Decays at BABAR

We report preliminary results based on a data sample of 20.7 fb^(-1) recorded at the Υ(4S) resonance by the BABAR detector at the PEP-II energy asymmetric collider at the Stanford Linear Accelerator Center. We have measured the branching fraction B(B^0→K^(*0)γ)=(4.39±0.41±0.27)×10^(–5) and measured a charge asymmetry in the B→K^*γ decays consistent with zero: A_(CP)=–0.035±0.076±0.012. We also searched for the decay B^0 →γγ and placed the 90% C.L. limit B(B^0 →γγ)<1.7×10^(-6). The search for the electroweak penguin decays B→K^(*)l^(+)l^(-) yielded the limits B(B→Kl^(+)l^(-))<0.6×10^(-6) and B(B→K*l^(+)l^(-))<2.5×10^(-6) at the 90% C.L

DIRC for a Higher Luminosity B Factory

The DIRC, a novel type of Cherenkov ring imaging device, is the primary hadronic particle identification system for the BaBar detector at the asymmetric B-factory Pep-II at SLAC. It is based on total internal reflection and uses long, rectangular bars made from synthetic fused silica as Cherenkov radiators and light guides. BaBar began taking data with colliding beams in late spring 1999. This paper describes the challenges for the DIRC in a higher luminosity environment and shows solutions to these challenges.Comment: 16 page

The DIRC Particle Identification System for the BABAR Experiment

A new type of ring-imaging Cherenkov detector is being used for hadronic particle identification in the BABAR experiment at the SLAC B Factory (PEP-II). This detector is called DIRC, an acronym for Detection of Internally Reflected Cherenkov (Light). This paper will discuss the construction, operation and performance of the BABAR DIRC in detail

Machine learning for particle identification in the LHCb detector

LHCb experiment is a specialised b-physics experiment at the Large Hadron Collider at CERN. It has a broad physics program with the primary objective being the search for CP violations that would explain the matter-antimatter asymmetry of the Universe. LHCb studies very rare phenomena, making it necessary to process millions of collision events per second to gather enough data in a reasonable time frame. Thus software and data analysis tools are essential for the success of the experiment. Particle identification (PID) is a crucial ingredient of most of the LHCb results. The quality of the particle identification depends a lot on the data processing algorithms. This dissertation aims to leverage the recent advances in machine learning field to improve the PID at LHCb. The thesis contribution consists of four essential parts related to LHCb internal projects. Muon identification aims to quickly separate muons from the other charged particles using only information from the Muon subsystem. The second contribution is a method that takes into account a priori information on label noise and improves the accuracy of a machine learning model for classification of this data. Such data are common in high-energy physics and, in particular, is used to develop the data-driven muon identification methods. Global PID combines information from different subdetectors into a single set of PID variables. Cherenkov detector fast simulation aims to improve the speed of the PID variables simulation in Monte-Carlo

SuperB: A High-Luminosity Heavy Flavour Factory. Conceptual Design Report.

479 páginas.-- INFN/AE - 07/2, SLAC-R-856, LAL 07-15.-- et al.Work supported in part by US department of Energy contract DE-AC02-7 6SF00515. SuperB project.Peer reviewe