arXiv subjects

Henry Diaz

Publications and source records attributed to Henry Diaz.

5 recordsLinked to original sources

arXiv2025

Tensorial charge assignments in unitary groups

We present an index-based tensorial formulation for computing eigenvalues of charge operators acting on arbitrary tensor representations of unitary gauge groups. The construction follows directly from the action of Cartan generators on tensor products and the additivity of weights, leading to a compact operator acting on general $(i_p,i_q)$ tensors. This framework provides a practical bookkeeping tool for assigning charges to arbitrary-dimensional multiplets appearing in model building. Explicit applications to $SU(2)$, $SU(3)$, and $SU(5)$ representations are discussed.

hep-ph↗

arXiv2022

Is the charged vecton boson seen by the CDF II the standard model one?

After the CDF II results, we can ask ourselves, have they observed the vector boson $W$ of the standard model? or have they discovered the effect of new physics? We show that, in electroweak models with $SU(2)_L\otimes SU(2)_R\otimes U(1)_{B-L}$ gauge symmetry, it is possible to accommodate, at tree level, the value of the mass of the charged vector boson obtained by the CDF-II. A shift in the mass of the lightest neutral vector boson is also predicted in some versions of the model.

hep-ph↗

arXiv2021

Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications

Multi-object tracking (MOT) is a crucial component of situational awareness in military defense applications. With the growing use of unmanned aerial systems (UASs), MOT methods for aerial surveillance is in high demand. Application of MOT in UAS presents specific challenges such as moving sensor, changing zoom levels, dynamic background, illumination changes, obscurations and small objects. In this work, we present a robust object tracking architecture aimed to accommodate for the noise in real-time situations. We propose a kinematic prediction model, called Deep Extended Kalman Filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space. DeepEKF utilizes a learned image embedding along with an attention mechanism trained to weight the importance of areas in an image to predict future states. For the visual scoring, we experiment with different similarity measures to calculate distance based on entity appearances, including a convolutional neural network (CNN) encoder, pre-trained using Siamese networks. In initial evaluation experiments, we show that our method, combining scoring structure of the kinematic and visual models within a MHT framework, has improved performance especially in edge cases where entity motion is unpredictable, or the data presents frames with significant gaps.

cs.CV↗

arXiv2021

Explicit parity violation in $SU(2)_L\otimes SU(2)_R\otimes U(1)_{B-L}$ models

Here we propose models with $SU(2)_L\otimes SU(2)_R\otimes U(1)_{B-L}$ electroweak gauge symmetry in which parity (or charge conjugation) is broken explicitly and the interactions of left- and right-handed fermions are completely different from each other at any energy scale. In these sort of models there is no restoration of parity at high energies. However, as in all left-right symmetric models, the electroweak charged interactions of quarks and leptons appear to be only left-handed at low energies, because the right-handed interactions are suppressed by the mass of the respective charged vector bosons and/or because these interactions may be much weaker than the left-handed ones.

hep-ph↗

arXiv2021

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence. Because of high risk and complexity, the adoption of AI for autonomous combat systems has been a long-standing difficulty. In order to address these issues, DARPA's AlphaDogfight Trials (ADT) program sought to vet the feasibility of and increase trust in AI for autonomously piloting an F-16 in simulated air-to-air combat. Our submission to ADT solves the high-dimensional, continuous control problem using a novel hierarchical deep reinforcement learning approach consisting of a high-level policy selector and a set of separately trained low-level policies specialized for excelling in specific regions of the state space. Both levels of the hierarchy are trained using off-policy, maximum entropy methods with expert knowledge integrated through reward shaping. Our approach outperformed human expert pilots and achieved a second-place rank in the ADT championship event.

cs.LG↗