Add to ORKGThis paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of $WH \to l\nu q\bar{q}$ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 90% the b-jet purity given by boosted decision trees is almost 20% higher than that given by neural networks
Many of the exotic particles expected at the LHC, such as SUSY, Higgs bosons and top quarks, will de...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset ...
The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging...
The ability to identify jets containing B hadrons is important for the high-pT physics program of a ...
The identification of b-quark initiated jets (b-tagging) is a fundamental tool for the physics of AT...
The ability to identify jets containing B-hadrons is important for the high-pT physics program of a ...
In this thesis, a new family of high-level jet flavour tagging algorithms called DL1 is presented. I...
Motivated by the application of data-driven solutions to the field of particle physics, in particula...
The existence of heavy particles, such as Higgs bosons and top quarks, which have short lifetime, ca...
The identification of jets containing a $b$-hadron, referred to as $b$-tagging, plays an important r...
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset ...
Machine learning algorithms have the capacity to discern intricate features directly from raw data. ...
The the identification of jets originating from b-quarks, called b-tagging, is an important part of ...
A short survey of the use of neural networks and statistical discriminants in high energy physics fo...
Many of the exotic particles expected at the LHC, such as SUSY, Higgs bosons and top quarks, will de...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset ...
The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging...
The ability to identify jets containing B hadrons is important for the high-pT physics program of a ...
The identification of b-quark initiated jets (b-tagging) is a fundamental tool for the physics of AT...
The ability to identify jets containing B-hadrons is important for the high-pT physics program of a ...
In this thesis, a new family of high-level jet flavour tagging algorithms called DL1 is presented. I...
Motivated by the application of data-driven solutions to the field of particle physics, in particula...
The existence of heavy particles, such as Higgs bosons and top quarks, which have short lifetime, ca...
The identification of jets containing a $b$-hadron, referred to as $b$-tagging, plays an important r...
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset ...
Machine learning algorithms have the capacity to discern intricate features directly from raw data. ...
The the identification of jets originating from b-quarks, called b-tagging, is an important part of ...
A short survey of the use of neural networks and statistical discriminants in high energy physics fo...
Many of the exotic particles expected at the LHC, such as SUSY, Higgs bosons and top quarks, will de...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset ...