arXiv subjects

Kinjal Patel

Publications and source records attributed to Kinjal Patel.

8 recordsLinked to original sources

arXiv2026

Static and dynamic properties of Triply Heavy Baryons

In this study, we investigate the ground-state masses, magnetic moments, transition magnetic moments, radiative decays, and heavy-to-heavy semileptonic decay rates, including their corresponding branching fractions of triply heavy baryons (THBs). The ground-state masses of the involved baryons are evaluated by numerically solving the six-dimensional hyperradial Schr\"{o}dinger equation within the hypercentral constituent quark model (hCQM), incorporating both hyper-Coulomb and linear confinement potentials along with spin-dependent interactions. The electromagnetic properties are calculated using the spin-flavour wave functions and the effective constituent quark masses of the baryon. The semileptonic $b \rightarrow c$ decay widths are computed using the Isgur--Wise function within the heavy-quark spin symmetry, from which the corresponding branching ratios and lepton flavour universality ratios are also determined.

hep-ph↗

arXiv2026

Semileptonic decay form factors of $\Xi_b^0 \rightarrow \Xi_c^+\ell\bar{\nu}_{\ell}$ in HQET

Heavy-to-heavy semileptonic decays, particularly the bottom-to-charm quark transitions, are essential for testing the Standard Model (SM) and extracting the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements. These decays have been extensively studied using various theoretical approaches. In this work, we investigate the semileptonic decay $\Xi_b^0 \rightarrow \Xi_c^+\ell\bar{\nu}_{\ell}$ (where $\ell = e$, $\tau$) using a phenomenological quark model. We compute the ground-state masses of the initial and final baryons to get the wave function, which is then used to calculate the form factors, including corrections up to order $1/m_Q$ within the framework of Heavy Quark Effective Theory (HQET). The obtained form factors are implemented in the helicity formalism to evaluate the differential decay rates, total decay width and branching ratio. We compare our results for the form factors at both the maximum and minimum recoil points with previous theoretical studies, finding good agreement. We observe that the form factors depend on the transferred momentum $q^2$ and their magnitude gradually increases with increasing $q^2$. The dominant form factors are $f_1$ and $g_1$, and they also exhibit similar $q^2$ dependencies. Additionally, we calculate the lepton flavour universality (LFU) ratio $R(\Xi_c) \approx 0.3$, which is in agreement with existing theoretical predictions.

hep-ph↗

arXiv2026

Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery

This technical report presents quantization-aware distillation (QAD) and our best practices for recovering accuracy of NVFP4-quantized large language models (LLMs) and vision-language models (VLMs). QAD distills a full-precision teacher model into a quantized student model using a KL divergence loss. While applying distillation to quantized models is not a new idea, we observe key advantages of QAD for today's LLMs: 1. It shows remarkable effectiveness and stability for models trained through multi-stage post-training pipelines, including supervised fine-tuning (SFT), reinforcement learning (RL), and model merging, where traditional quantization-aware training (QAT) suffers from engineering complexity and training instability; 2. It is robust to data quality and coverage, enabling accuracy recovery without full training data. We evaluate QAD across multiple post-trained models including AceReason Nemotron, Nemotron 3 Nano, Nemotron Nano V2, Nemotron Nano V2 VL (VLM), and Llama Nemotron Super v1, showing consistent recovery to near-BF16 accuracy.

cs.LG↗

arXiv2025

Semileptonic decay and form factors of $\Omega_b^- \rightarrow \Omega_c^0\,e\,\bar{\nu_e}$

We investigated the heavy-to-heavy semileptonic decay $\Omega_b^- \rightarrow \Omega_c^0 e \bar{\nu_e}$ within the framework of the Hypercentral Constituent Quark Model (HCQM). The ground-state masses of the involved baryons were evaluated by numerically solving the six-dimensional hyperradial Schr\"{o}dinger equation, incorporating both hyper-Coulomb and linear confinement potentials along with spin-dependent interactions. The Heavy Quark Effective Field Theory (HQET) form factors are computed up to the subleading order, incorporating $1/m_Q$ corrections that account for finite mass effects beyond the heavy-quark symmetry limit. These form factors were then employed to analyse the heavy-to-heavy semileptonic decay rate via helicity formalism. The decay width and branching ratio results were compared to those obtained using various theoretical approaches.

hep-ph↗

arXiv2025

Electromagnetic and weak decay of singly Heavy Baryons (Qqq)

The heavy-to-heavy exclusive semileptonic transitions of singly heavy baryons (SHBs) are investigated within the framework of the Hypercentral Constituent Quark Model (hCQM). The six-dimensional hyperradial Schr\"{o}dinger equation is solved in the variational approach to calculate the ground state masses of bottom and charmed baryons. The transition magnetic moments and radiative $M1$ decay widths are calculated using the spin-flavour wave function and the effective quark masses of constituent baryon. The Isgur-Wise function (IWF) is determined at zero recoil to compute the $b \rightarrow c$ semileptonic decay. Additionally, the branching ratios, as well as the slope and convexity parameters of IWF are evaluated and compared with results from other studies.

hep-ph↗

arXiv2024

Transition properties of Doubly Heavy Baryons

In this study, we have investigated the radiative and semileptonic decay of doubly heavy baryons. Our focus is to determine the static and dynamic properties such as ground state masses, magnetic moment, transition magnetic moment, radiative decay and heavy-to-heavy semileptonic decay rates including their corresponding branching fractions. The ground state masses are calculated by solving the six-dimensional hyperradial Schr\"{o}dinger equation. The magnetic moments and transition magnetic moments for $J^P=\frac{1}{2}^+$ and $J^P=\frac{3}{2}^+$ baryons are also calculated. In addition, radiative M1 decay widths are computed from the transition magnetic moment. We have employed the Isgur-Wise function(IWF) to analyse the semileptonic decay widths of the doubly heavy baryons. The obtained results are compared with other theoretical predictions.

hep-ph↗

arXiv2022

Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks

We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.

cs.LG↗

arXiv2021

A Spiking Neural Network for Image Segmentation

We seek to investigate the scalability of neuromorphic computing for computer vision, with the objective of replicating non-neuromorphic performance on computer vision tasks while reducing power consumption. We convert the deep Artificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network (SNN) architecture using the Nengo framework. Both rate-based and spike-based models are trained and optimized for benchmarking performance and power, using a modified version of the ISBI 2D EM Segmentation dataset consisting of microscope images of cells. We propose a partitioning method to optimize inter-chip communication to improve speed and energy efficiency when deploying multi-chip networks on the Loihi neuromorphic chip. We explore the advantages of regularizing firing rates of Loihi neurons for converting ANN to SNN with minimum accuracy loss and optimized energy consumption. We propose a percentile based regularization loss function to limit the spiking rate of the neuron between a desired range. The SNN is converted directly from the corresponding ANN, and demonstrates similar semantic segmentation as the ANN using the same number of neurons and weights. However, the neuromorphic implementation on the Intel Loihi neuromorphic chip is over 2x more energy-efficient than conventional hardware (CPU, GPU) when running online (one image at a time). These power improvements are achieved without sacrificing the task performance accuracy of the network, and when all weights (Loihi, CPU, and GPU networks) are quantized to 8 bits.

cs.NE↗