arXiv subjects

Chinmay Bharathulwar

Publications and source records attributed to Chinmay Bharathulwar.

3 recordsLinked to original sources

arXiv2026

Mineral Detection of Neutrinos and Dark Matter 2026 Proceedings

The fourth "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'26) meeting was held April 14-17, 2026 in Karlsruhe, Germany, hosted by the Institute for Astroparticle Physics (IAP) at Karlsruhe Institute of Technology (KIT). These proceedings detail the contributions that were presented during MDvDM'26, illustrating the unprecedented progress in theoretical, computational and experimental studies towards the realization of the concept of mineral detectors. Mineral detectors represent an emerging particle detection concept that has risen in prominence in recent years due to the advent of modern computational and high-resolution microscopy techniques. Natural and synthetic crystals are capable of retaining microscopic damage features induced by nuclear recoils, which could be then read out with a variety of micrometer and nanometer resolution microscopy techniques. On laboratory time scales mineral detectors could be employed for reactor neutrino monitoring and dark matter detection, with the potential to measure the directions as well as the energies of the induced nuclear recoils. Uniquely, ancient natural crystals (so-called paleo-detectors) that have been recording nuclear recoils over geological timescales could be used for studying astrophysical neutrinos, cosmic rays, dark matter and heavy exotic particles, as well as the variation of their fluxes over our Galaxy's lifetime. In recent years the international MDvDM community has been successfully tackling the challenges associated with realizing the concept of mineral detectors, opening the pathway towards a fully fledged experimental program and potential future discoveries.

physics.ins-det↗

arXiv2026

Multi-scale reconstruction of single-ion damage tracks in diamond via nitrogen-vacancy centers

Understanding particle-induced damage tracks in solid-state materials underpins emerging applications in rare-event detection and quantum defect engineering. Resolving these tracks requires multi-scale readout, from event localization at the millimeter scale to track-morphology reconstruction at the nanoscale. Nitrogen-vacancy (NV) centers in diamond provide such a platform, combining optical localization with quantum sensing of track morphology. Here, we implant sub-MeV carbon ions into nitrogen-rich diamond and detect individual recoil events via spatially localized NV formation. We develop a simulation framework that explains the observed NV yield and predicts that directional information is retained in the NV distribution after annealing. Machine learning further recovers much of the information lost to defect diffusion and limited NV yield, improving head-tail classification to a level comparable to pre-annealed vacancy tracks. Measurements of NV spin coherence indicate compatibility with nanoscale track reconstruction via NV strain mapping and magnetic gradient-based techniques. These results identify promising pathways toward NV-diamond directional detectors for rare events, while the track-modeling framework has broader implications for paleodetection and quantum material synthesis.

physics.ins-det↗

arXiv2025

Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies

We present a novel method for diffusion-guided frameworks for view-consistent super-resolution (SR) in neural rendering. Our approach leverages existing 2D SR models in conjunction with advanced techniques such as Variational Score Distilling (VSD) and a LoRA fine-tuning helper, with spatial training to significantly boost the quality and consistency of upscaled 2D images compared to the previous methods in the literature, such as Renoised Score Distillation (RSD) proposed in DiSR-NeRF (1), or SDS proposed in DreamFusion. The VSD score facilitates precise fine-tuning of SR models, resulting in high-quality, view-consistent images. To address the common challenge of inconsistencies among independent SR 2D images, we integrate Iterative 3D Synchronization (I3DS) from the DiSR-NeRF framework. Our quantitative benchmarks and qualitative results on the LLFF dataset demonstrate the superior performance of our system compared to existing methods such as DiSR-NeRF.

cs.CV↗