Cosmology and Dark Matter at the LHC

We examine the question of whether neutralinos produced at the LHC can be shown to be the particles making up the astronomically observed dark matter. If the WIMP alllowed region lies in the SUGRA coannihilation region, then a strong signal for this would be the unexpected near degeneracy of the stau and neutralino i.e., a mass difference \Delta M\simeq (5-15) GeV. For the mSUGRA model we show such a small mass difference can be measured at the LHC using the signal 3\tau+jet+E_T^{\rm miss}. Two observables, opposite sign minus like sign pairs and the peak of the \tau\tau mass distribution allows the simultaneous determination of \Delta M to 15% and the gluino mass M_{\tilde g} to be 6% at the benchmark point of M_{\tilde g}=850 GeV, A_0=0, \mu>0 with 30 fb^{-1}. With 10 fb^{-1}, \Delta M can be determined to 22% and one can probe the parameter space up to m_{1/2}=700 GeV with 100 fb^{-1}.Comment: 11 pages, 7 figures, Talk at IDM 2006, 11th September to 16th September, Greec

Detection of SUSY Signals in Stau Neutralino Co-annihilation Region at the LHC

We study the prospects of detecting the signal in the stau neutralino co-annihilation region at the LHC using tau leptons. The co-annihilation signal is characterized by the stau and neutralino mass difference (dM) to be 5-15 GeV to be consistent with the WMAP measurement of the cold dark matter relic density as well as all other experimental bounds within the minimal supergravity model. Focusing on tau's from neutralino_2 --> tau stau --> tau tau neutralino_1 decays in gluino and squark production, we consider inclusive MET+jet+3tau production, with two tau's above a high E_T threshold and a third tau above a lower threshold. Two observables, the number of opposite-signed tau pairs minus the number of like-signed tau pairs and the peak position of the di-tau invariant mass distribution, allow for the simultaneous determination of dM and M_gluino. For dM = 9 GeV and M_gluino = 850 GeV with 30 fb^-1 of data, we can measure dM to 15% and M_gluino to 6%.Comment: 4 pages LaTex, 3 figures. To appear in Proceedings of SUSY06, the 14th International Conference on Supersymmetry and the Unification of Fundamental Interactions, UC Irvine, California, 12-17 June 2006. A typo in a reference is correcte

The NOνA simulation chain

The NOνA experiment is a two-detector, long-baseline neutrino experiment operating in the recently upgraded NuMI muon neutrino beam. Simulating neutrino interactions and backgrounds requires many steps including: the simulation of the neutrino beam flux using FLUKA and the FLUGG interface; cosmic ray generation using CRY; neutrino interaction modeling using GENIE; and a simulation of the energy deposited in the detector using GEANT4. To shorten generation time, the modeling of detector-specific aspects, such as photon transport, detector and electronics noise, and readout electronics, employs custom, parameterized simulation applications. We will describe the NOνA simulation chain, and present details on the techniques used in modeling photon transport near the ends of cells, and in developing a novel data-driven noise simulation. Due to the high intensity of the NuMI beam, the Near Detector samples a high rate of muons originating in the surrounding rock. In addition, due to its location on the surface at Ash River, MN, the Far Detector collects a large rate (˜ 140 kHz) of cosmic muons. We will discuss the methods used in NOνA for overlaying rock muons and cosmic ray muons with simulated neutrino interactions and show how realistically the final simulation reproduces the preliminary NOνA data

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems