arXiv subjects

Ravi Gupta

Publications and source records attributed to Ravi Gupta.

6 recordsLinked to original sources

arXiv2026

Magnetohydrodynamic equilibrium and neutronics study on MAST-U using Jenga framework

Tokamak design is inherently challenging due to several cross-competing effects which require a careful and calibrated treatment to obtain an optimal operational envelope. Incorporating physics across varied fidelities is crucial in this exercise. Jenga is developed as a unified design and modeling framework for tokamaks, seamlessly coupling systems-level studies to high-fidelity models based on first principles. In this work, static Grad-Shafranov (GS) equilibrium for an entire pulse and the neutronics study of the Mega Ampere Spherical Tokamak Upgrade (MAST-U) tokamak are carried out in Jenga. Coil currents and plasma profiles from the EFIT++ reconstruction of MAST-U shots are used to reproduce the plasma poloidal flux and shape targets at different time slices. The results from Jenga are also in good agreement with FreeGSNKE and Fiesta codes. Neutronics analysis is performed for a hypothetical 50-50 mixture of deuterium-tritium (DT) fuel, using the same data structure as the systems and equilibrium studies. A distributed neutron source is initialized within the last closed flux surface (LCFS) of the plasma, with their strength being functions of the density and temperature of the ions. The distribution of the neutron flux across the energy spectrum is computed for the active coils and the first wall (limiter) independently over multiple scenarios. We demonstrate the capabilities of Jenga with a comprehensive analysis that takes inputs about the plasma geometry, tokamak design and plasma profiles and performs 0D, 2D and 3D numerics for the systems study, equilibrium and neutron transport respectively.

physics.plasm-ph↗

arXiv2026

Design and mechanical analysis of the PRAGYA tokamak vacuum vessel

PRAGYA is India's first privately developed low aspect ratio tokamak designed by Pranos Fusion Energy. The device is designed for a plasma major radius (R0) of about 0.4 m, a plasma minor radius (a) greater than 0.18 m, a plasma current (Ip) of up to 25 kA, and a toroidal magnetic field (B_T) of 0.1 T. The PRAGYA vacuum vessel incorporates several distinctive features, including a toroidal electrical break to minimize induced eddy currents and a double O-ring arrangement to reduce vacuum leakage. This paper presents the final design of the PRAGYA vacuum vessel and a comprehensive three-dimensional (3D) finite element model (FEM) assessment of its structural performance. The analysis evaluates the effects of self-weight, atmospheric pressure loading, and thermal stress arising from in-situ baking. The results confirm that the design satisfies the required safety margins under these combined loading conditions, providing a robust foundation for subsequent plasma operations in this compact tokamak.

physics.plasm-ph↗

arXiv2026

Union3.1: Self-consistent Measurements of Host Galaxy Properties for 2000 Type Ia Supernovae

The determination of distances using time-series photometry of Type Ia supernovae (SNe Ia) relies on a ~5% empirical correction related to the properties of their host galaxies, e.g., global stellar mass. It is therefore crucial for unbiased cosmology inference that host galaxy properties be self-consistently determined across the full range of redshifts probed, which we undertake in this study for approximately 2000 SNe in the Union3 compilation (now Union3.1). We use aperture-matched, homogeneously-reduced, optical-infrared photometry from the DESI Legacy Imaging Surveys to derive global galaxy properties using the stellar population synthesis and SED-fitting code Prospector. We find that the host masses of $z<0.10$ SNe in Union3 were, on average, overestimated relative to the rest of the sample, while the opposite was true for $z<0.15$ SNe in Pantheon+. After correction, the two studies' average distance modulus estimated for low-redshift SNe, previously $>0.03$ mag discrepant, come into 0.01 mag agreement. We then update the UNITY SN analysis and find that the uncertainties on all standardization parameters shrink to 0.6-0.9x their previous sizes. For flat-$\Lambda$CDM, we find $\Omega_m=0.344^{+0.026}_{-0.025}$, a -0.3$\sigma$ shift from Union3. We then combine with measurements of Baryon Acoustic Oscillations (BAO) and the Cosmic Microwave Background (CMB) exactly as done by DESI DR2 and find $w_0=-0.719\pm0.084$, $w_a=-0.95^{+0.29}_{-0.26}$, corresponding to 3.4$\sigma$ evidence against a cosmological constant (down from 3.8$\sigma$). We also update the DESI combined probe analysis using our correction to Pantheon+ and the recent DES-SN5YR Dovekie recalibration, finding $3.2\sigma$ (up from 2.8$\sigma$) and 3.4$\sigma$ (down from 4.2$\sigma$) evidence against a cosmological constant in the $w_0w_a$ plane, altogether marking a significantly improved consistency across SN analyses.

astro-ph.CO↗

arXiv2025

BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization

Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural language commands via Google Home/IFTTT integration and learns from implicit feedback through manual overrides. Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy. This work establishes a practical framework for deploying adaptive AI on resource-constrained IoT devices, enabling intelligent home automation without cloud dependencies.

cs.AI↗

arXiv2025

Agentic Educational Content Generation for African Languages on Edge Devices

Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demonstrates significant performance achievements. InkubaLM on Jetson Nano achieved a Time-To-First-Token (TTFT) of 129 ms, an average inter-token latency of 33 ms, and a throughput of 45.2 tokens per second while consuming 8.4 W. On Raspberry Pi 4B, InkubaLM also led with 326 ms TTFT and 15.9 tokens per second at 5.8 W power consumption. The framework consistently delivered high multilingual quality, averaging a BLEU score of 0.688, cultural relevance of 4.4/5, and fluency of 4.2/5 across tested African languages. Through potential partnerships with active community organizations including African Youth & Community Organization (AYCO) and Florida Africa Foundation, this research aims to establish a practical foundation for accessible, localized, and sustainable AI-driven education in resource-constrained environments. Keeping focus on long-term viability and cultural appropriateness, it contributes to United Nations SDGs 4, 9, and 10. Index Terms - Multi-Agent Systems, Edge AI Computing, Educational Technology, African Languages, Rural Education, Sustainable Development, UN SDG.

cs.AI↗

arXiv2021

The HST See Change Program: I. Survey Design, Pipeline, and Supernova Discoveries

The See Change survey was designed to make $z>1$ cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope spanning the redshift range $z=1.13$ to $1.75$, discovering 57 likely transients and 27 likely SNe Ia at $z\sim 0.8-2.3$. As in similar previous surveys (Dawson et al. 2009), this proved to be a highly efficient use of HST for SN observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star-formation rates. We find that the resulting number of SNe Ia per orbit is a factor of $\sim 8$ higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and SN discoveries. Novel features include fully blinded SN searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 magnitudes of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host-galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.

astro-ph.CO↗