BOOST 2017

Session

Poster Session

16 Jul 2017, 16:00

Embassy Suites Buffalo

Presentation materials

boostcamp101.pdf

boostcamp101.pptx

66. A new scale-invariant jet clustering algorithm for the substructure era

Prof. Joel Walker (Sam Houston State University)

We introduce a new scale-invariant jet clustering algorithm which does not impose a fixed cone size on the event. The proposed construction maintains excellent object discrimination for very collimated partonic systems. Nevertheless, it is able to asymptotically recover favorable behaviors of the standard anti-KT algorithm. Additionally, it is intrinsically suitable for the tagging of highly...

65. How Much Information is in a Jet?

Mr Kaustuv Datta (Reed College)

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics has typically employed image recognition, natural language processing, or other algorithms that have been extensively developed...

74. Identification of Hadronically-Decaying W Boson Top Quarks Using High-Level Features as Input to Boosted Decision Trees and Deep Neural Networks in ATLAS at #sqrt{s} = 13 TeV

Tatsumi Nitta (Waseda University (JP))

72. Identification of Ultra-Boosted Higgs-->bb Jets Using Subjet B-Tagging with ATLAS

Samuel Ross Meehan (University of Washington (US))

71. Improving jet substructure performance in ATLAS with unified tracking and calorimeter inputs

Steven Randolph Schramm (Universite de Geneve (CH))

77. Jet energy resolution

Charles Irving Harrington (State University of New York at buffalo (US))

75. Jet Mass Differential Cross Section

Anne Winifred Fortman (State University of New York at Buffalo (US))

73. Jet reclustering & close-by effects in ATLAS run II

Matt Leblanc (TRIUMF)

79. Pileup Offset Jet Energy Corrections at 13 TeV

Bahareh Hojatollah Roozbahani (State University of New York at buffalo (US))

70. Reducing the Top Quark Mass Uncertainty with Jet Grooming

Anders Andreassen (Harvard University)

The measurement of the top quark mass has large systematic uncertainties coming from the Monte Carlo simulations that are used to match theory and experiment. We explore how much that uncertainty can be reduced by using jet grooming procedures. We estimate the inherent ambiguity in what is meant by Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty can be reduced...

78. Search for light dijet resonances produced in association with a jet

Cristina Ana Mantilla Suarez (Johns Hopkins University (US))

80. Search for low-mass pair-produced dijet resonances using jet substructure techniques in proton-proton collisions at s=13 TeV

Alejandro Gomez Espinosa (Rutgers, State Univ. of New Jersey (US))

67. Theory Predictions for Pull

In this project, we apply the QT resummation on Pull Vector, which is which is a tool applied on the QCD color connection and this tool can help us to separate the background and signal for the process of Higgs and gluon decay to b\bar{b}.

And in the presentation, I will show some of our works like the result of resummation and try to compare it with the next-to-leading fix-order dipole...

69. Using Boosted Event Shapes for Heavy Object Classification

Justin Pilot (University of California Davis (US))

We present a novel approach to the problem of discriminating jets produced from the hadronic decays of highly-boosted heavy particles (top, W, Z, H) from light jets. By hypothesizing different particle origins for the jets and boosting all jet constituents into the corresponding rest frames, angular and kinematic distributions of reconstructed particles can be used to discriminate 2- or...

76. W Tag Scale Factor With Fully-Merged Tops

Ashley Marie Parker (State University of New York at buffalo (US))

68. Weakly Supervised Classifiers in High Energy Physics

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics...