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A method and apparatus to authenticate a tamper resistant seal that includes a population of particles embedded in an adhesive, the population including at least one micromachine artifact of a predetermined physical shape.
MINERνA, or Main INjector ExpeRiment for ν-A, at Fermilab, is an experimentdedicated to the study of neutrino-nucleus interactions in the GeV regime. Itsgoal is to illustrate the interplay between hadronic and nuclear physics and measure intranuclear dynamics crucial for the present and future neutrino oscillationmeasurements. We first measure a set of variables sensitive to how Monte Carlo(MC) simulations of neutrino-nucleus interactions implement binding energy andthen move on to measure the antineutrino CCQE cross section on the hydrogentargets in MINERνA’s CH detector. We have developed a method to preferentially select events on the hydrogen by comparing outgoing neutrons’ directions totheoretical neutron directions assuming two-body interactions. We measured thecross section, extracted the axial form factor, and performed a Z-expansion fit. Weobserve larger values in the axial form factor at high Q2than current best fits. Finally, we show a preliminary selection of events with both protons and neutrons toinvestigate nuclear processes responsible for producing these final states.
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IEEE Lehigh Valley Section, Virtual, December 1, 2022
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The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low computational complexity that have weak inductive bias are feasible. To address this issue, we utilize knowledge distillation to leverage both the performance of large models and the reduced computational complexity of small ones. In this paper, we present an implementation of knowledge distillation, demonstrating an overall boost in the student models' performance for the task of classifying jets at the LHC. Furthermore, by using a teacher model with a strong inductive bias of Lorentz symmetry, we show that we can induce the same inductive bias in the student model which leads to better robustness against arbitrary Lorentz boost.
A lockout padlock controls access to a hazardous energy source control box. The control box has a box body and a door that closes the box body. The door has an aperture. A staple is connected to the box body and the staple extends through the aperture in the door of the control box. A first lockout hinge part has a first lockout hinge part padlock hole. A second lockout hinge part has a second lockout hinge part padlock hole. The first lockout hinge part and the second lockout hinge part are position together to form a padlock pocket. A first padlock is positioned and locked in the staple. The first padlock is positioned in said padlock pocket and covered by said first lockout hinge part and said second lockout hinge part. A second padlock is positioned in the first lockout hinge part padlock hole and in said second lockout hinge part padlock hole.
Hadronic physics studies the properties of the proton and neutron. The theory of Quantum Chromodynamics (QCD) governs the structure of hadrons. Non-perturbative structure functions are used to describe the hadrons? structure. Generalized Parton Distributions (GPDs) are a set of structure functions providing access to a 3D view of the nucleon. GPDs contain information about the longitudinal momentum and transverse position of quarks and their correlation. They are related to the spin structure and play a role in solving the proton spin puzzle. They provide access to the quarks? orbital angular momentum, a crucial puzzle piece. For this, measurements on the proton, already present, and neutron are necessary. This thesis aims to explore Compton scattering on the neutron with a deuterium target by measuring the spectator proton with the CLAS12 detector and the BONuS12 RTPC at Jefferson Lab. This work first describes the implementation, which I carried out, of a Kalman filter to reconstruct spectator protons in the BONuS12 detector. The second part of the manuscript is dedicated to the work on the analysis of DVCS on the neutron with the subtraction of background noises. Two different methods for the subtraction of ?0 will be detailed. Finally, the obtained results are presented and discussed
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughput environments, such as the CERN LHC. The quadratic complexity of transformer models demands substantial resources and increases latency during inference. In order to address these issues, we introduce the Spatially Aware Linear Transformer (SAL-T), a physics-inspired enhancement of the linformer architecture that maintains linear attention. Our method incorporates spatially aware partitioning of particles based on kinematic features, thereby computing attention between regions of physical significance. Additionally, we employ convolutional layers to capture local correlations, informed by insights from jet physics. In addition to outperforming the standard linformer in jet classification tasks, SAL-T also achieves classification results comparable to full-attention transformers, while using considerably fewer resources with lower latency during inference. Experiments on a generic point cloud classification dataset (ModelNet10) further confirm this trend. Our code is available at https://github.com/aaronw5/SAL-T4HEP.
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