arXiv subjects

J. Bastos

Publications and source records attributed to J. Bastos.

4 recordsLinked to original sources

arXiv2014

A class of cubic Rauzy Fractals

In this paper, we study arithmetical and topological properties for a class of Rauzy fractals ${\mathcal R}_a$ given by the polynomial $x^3- ax^2+x-1$ where $a \geq 2$ is an integer. In particular, we prove the number of neighbors of ${\mathcal R}_a$ in the periodic tiling is equal to $8$. We also give explicitly an automaton that generates the boundary of ${\mathcal R}_a$. As a consequence, we prove that ${\mathcal R}_2$ is homeomorphic to a topological disk.

math.DS↗

arXiv2007

A multivariate approach to heavy flavour tagging with cascade training

This paper compares the performance of artificial neural networks and boosted decision trees, with and without cascade training, for tagging b-jets in a collider experiment. It is shown, using a Monte Carlo simulation of $WH \to lνq\bar{q}$ events, that for a b-tagging efficiency of 50%, the light jet rejection power given by boosted decision trees without cascade training is about 55% higher than that given by artificial neural networks. The cascade training technique can improve the performance of boosted decision trees and artificial neural networks at this b-tagging efficiency level by about 35% and 80% respectively. We conclude that the cascade trained boosted decision trees method is the most promising technique for tagging heavy flavours at collider experiments.

physics.data-an↗

arXiv2007

Tagging heavy flavours with boosted decision trees

This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of $WH \to lνq\bar{q}$ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 60% the light jet rejection given by boosted decision trees is about 35% higher than that given by neural networks.

physics.data-an↗

arXiv2006

A statistical method for luminosity monitoring in high energy collider experiments

A statistical method which uses a combination of two subdetectors to monitor the luminosity in high energy interactions is presented. To illustrate its performance, this method was applied to random triggered minimum bias data collected in the commissioning period of the HERA-B experiment in spring 2000. It is found that luminosity estimates with an intrinsic systematic error of 3% can be obtained.

physics.ins-det↗