19 research outputs found
Two-pion Bose-Einstein correlations in central Pb-Pb collisions at = 2.76 TeV
The first measurement of two-pion Bose-Einstein correlations in central Pb-Pb
collisions at TeV at the Large Hadron Collider is
presented. We observe a growing trend with energy now not only for the
longitudinal and the outward but also for the sideward pion source radius. The
pion homogeneity volume and the decoupling time are significantly larger than
those measured at RHIC.Comment: 17 pages, 5 captioned figures, 1 table, authors from page 12,
published version, figures at
http://aliceinfo.cern.ch/ArtSubmission/node/388
Suppression of charged particle production at large transverse momentum in central Pb-Pb collisions at TeV
Inclusive transverse momentum spectra of primary charged particles in Pb-Pb
collisions at = 2.76 TeV have been measured by the ALICE
Collaboration at the LHC. The data are presented for central and peripheral
collisions, corresponding to 0-5% and 70-80% of the hadronic Pb-Pb cross
section. The measured charged particle spectra in and GeV/ are compared to the expectation in pp collisions at the same
, scaled by the number of underlying nucleon-nucleon
collisions. The comparison is expressed in terms of the nuclear modification
factor . The result indicates only weak medium effects ( 0.7) in peripheral collisions. In central collisions,
reaches a minimum of about 0.14 at -7GeV/ and increases
significantly at larger . The measured suppression of high- particles is stronger than that observed at lower collision energies,
indicating that a very dense medium is formed in central Pb-Pb collisions at
the LHC.Comment: 15 pages, 5 captioned figures, 3 tables, authors from page 10,
published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/98
Probabilistic classifiers and automated cancer registration: An exploratory application.
AbstractA test of the performance of two probabilistic classifiers (random forests and multinomial logit models) in automatically defining cancer cases has been carried out on 5608 subjects, registered by the Venetian Tumour Registry (RTV) during the years 1987–1996 and manually checked for possible second cancers that occurred during the 1997–1999 period.An eightfold cross-validation was performed to estimate the classification error; 63 predictive variables were entered into the model fitting. The random forest allows to automatically classify 45% of subjects with a classification error lower than 5%, while the corresponding error is 31% for the multilogit model. The performance of the former classifier is appealing, indicating a potential drop of manually checked cases from 1750 to 960 per incidence year with a moderate error rate. This result suggests to refine the approach and extend it to other categories of manually treated cases