Singularity variables for missing energy event kinematics

Abstract

We discuss singularity variables which are properly suited for analyzing the kinematics of events with missing transverse energy at the LHC. We consider six of the simplest event topologies encountered in studies of leptonic W -bosons and top quarks, as well as in SUSY-like searches for new physics with dark matter particles. In each case, we illustrate the general prescription for finding the relevant singularity variable, which in turn helps delineate the visible parameter subspace on which the singularities are located. Our results can be used in two different ways -first, as a guide for targeting the signal-rich regions of parameter space during the stage of discovery, and second, as a sensitive focus point method for measuring the particle mass spectrum after the initial discovery. 4 The only exception being an invisibly decaying Z-boson, or a leptonically decaying W -boson where the lepton is lost [26].5 For reviews of the large variety of mass measurement methods proposed for SUSY-like events with MET, see [27,28] and references therein.

Simulating the time projection chamber responses at the MPD detector using generative adversarial networks

“…We would like to finalise this section with a short note on the applicability of GAN usage for fast detector simulation. As it is raised in [38], for a physics analysis, the systematic uncertainties associated with a GAN-generated sample cannot be smaller compared to those from the sample that GAN is trained on. At the same time, applying GANs also means making use of the prior knowledge about the structure of the data [39].…”

Finding wombling boundaries in LHC data with Voronoi and Delaunay tessellations

“…and even singular on the boundary ∂P NP [18][19][20][21][22][23][24][25][26][27]. Since the background distribution f SM is a smooth function across ∂P NP , the presence of f NP creates a discontinuous "jump" in the combined event density (1.2), precisely at the location of the boundary ∂P NP [16,17].…”

Compressing PDF sets using generative adversarial networks

“…GANs belong to the class of unsupervised machine learning algorithms where a generative model is trained to produce new data which is indistinguishable under certain criteria from the training data. GANs are mainly used for image modeling [10], but in the last couple years, many applications have been found in High Energy Physics (HEP) [11][12][13][14][15][16][17][18][19][20]. Here, we propose to use the GANs to enhance the statistics of a given input Monte Carlo PDF by generating what we call synthetic replicas.…”