Particle identification in ALICE: a Bayesian approach
ALICE Collaboration. (2016). Particle identification in ALICE: a Bayesian approach. European Physical Journal Plus, 131(5), Article 168. https://doi.org/10.1140/epjp/i2016-16168-5
Julkaistu sarjassa
European Physical Journal PlusTekijät
Päivämäärä
2016Tekijänoikeudet
© CERN for the benefit of the ALICE Collaboration 2016. This article is published with open access at Springerlink.com and distributed under the terms of a the Creative Commons Attribution 4.0 License.
We present a Bayesian approach to particle identification (PID) within the ALICE experiment.
The aim is to more effectively combine the particle identification capabilities of its various detectors. After
a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach
for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed
via measurements of specific energy loss (dE/dx) and time of flight. PID efficiencies and misidentification
probabilities are extracted and compared with Monte Carlo simulations using high-purity samples
of identified particles in the decay channels K0
S → π−π+, φ → K−K+, and Λ → pπ− in p-Pb collisions
at √sNN = 5.02 TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology
was used to obtain corrected pT spectra of pions, kaons, protons, and D0 mesons in pp collisions
at √s = 7 TeV. In all cases, the results using Bayesian PID were found to be consistent with previous
measurements performed by ALICE using a standard PID approach. For the measurement of D0 → K−π+,
it was found that a Bayesian PID approach gave a higher signal-to-background ratio and a similar or larger
statistical significance when compared with standard PID selections, despite a reduced identification effi-
ciency. Finally, we present an exploratory study of the measurement of Λ+
c → pK−π+ in pp collisions at
√s = 7 TeV, using the Bayesian approach for the identification of its decay products.
...
Julkaisija
Springer Berlin HeidelbergISSN Hae Julkaisufoorumista
2190-5444Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/26111614
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Ellei muuten mainita, aineiston lisenssi on © CERN for the benefit of the ALICE Collaboration 2016. This article is published with open access at Springerlink.com and distributed under the terms of a the Creative Commons Attribution 4.0 License.
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Conditional particle filters with diffuse initial distributions
Karppinen, Santeri; Vihola, Matti (Springer, 2021)Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which ... -
Price Optimization Combining Conjoint Data and Purchase History : A Causal Modeling Approach
Valkonen, Lauri; Tikka, Santtu; Helske, Jouni; Karvanen, Juha (University of Pennsylvania Press, 2024)Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data ... -
A Fatty Acid Based Bayesian Approach for Inferring Diet in Aquatic Consumers
Galloway, Aaron W. E.; Brett, Michael T.; Holtgrieve, Gordon W.; Ward, Eric J.; Ballantyne, Ashley P.; Burns, Carolyn W.; Kainz, Martin J.; Müller-Navarra, Doerthe C.; Persson, Jonas; Ravet, Joseph L.; Strandberg, Ursula; Taipale, Sami; Alhgren, Gunnel (Public Library of Science, 2015)We modified the stable isotope mixing model MixSIR to infer primary producer contributions to consumer diets based on their fatty acid composition. To parameterize the algorithm, we generated a ‘consumer-resource library’ ... -
A linear approach for the nonlinear distributed parameter identification problem
Tai, Xue-Cheng; Neittaanmäki, Pekka (Birkhäuser, 1991)In identifying the nonlinear distributed parameters we propose an approach, which enables us to identify the nonlinear distributed parameters by just solving linear problems. In this approach we just need to identify linear ... -
Causal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach
Tikka, Santtu; Hyttinen, Antti; Karvanen, Juha (Foundation for Open Access Statistic, 2021)Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.