Saksham Arora | Searcharxiv

arXiv2025

Imaging the LkCa 15 system in polarimetry and total intensity without self-subtraction artefacts

Studying young protoplanetary disks is essential for understanding planet formation, but traditional angular differential imaging can introduce self-subtraction artefacts that hinder interpretation of small-scale structures. We present high-resolution total- and polarized-intensity Ks-band images of the LkCa~15 system obtained with SPHERE using near-simultaneous reference-star differential imaging (star-hopping), yielding self-subtraction-free images beyond 0.1 arcsec. LkCa~15 hosts a ~160 au protoplanetary disk and has previously been reported to harbour candidate protoplanets at separations of 15--18 au. We analyse the disk morphology and dust properties and search for super-Jupiter planets beyond 20 au. We first model the near-infrared scattered-light images together with ALMA submillimetre continuum data using RADMC-3D and a two grain-size (micron and millimetre) compact olivine model. While this model broadly reproduces the disk geometry, it overpredicts the degree of forward scattering in the near-infrared. To investigate this discrepancy, we extract the scattering phase function S(theta) and polarized fraction P(theta) from the SPHERE data and compare them with aggregate-scattering models. The observed phase functions disfavour compact Mie spheres and are better matched by porous aggregates (CAHP). Recomputing the scattered-light models with porous CAHP grains in the disk surface layer significantly improves agreement with the observed Ks-band morphology and polarization, while retaining compact millimetre grains to reproduce the ALMA continuum. No new planetary companions are detected; we place upper mass limits of ~1.5 MJ beyond 200 au and ~3.6 MJ in the inner disk. Our results demonstrate that combining star-hopping imaging with phase-function diagnostics provides strong constraints on dust grain properties in protoplanetary disks.

astro-ph.EP↗

arXiv2025

Consensus Is All You Need: Gossip-Based Reasoning Among Large Language Models

Large language models have advanced rapidly, but no single model excels in every area -- each has its strengths and weaknesses. Instead of relying on one model alone, we take inspiration from gossip protocols in distributed systems, where information is exchanged with peers until they all come to an agreement. In this setup, models exchange answers and gradually work toward a shared solution. Each LLM acts as a node in a peer-to-peer network, sharing responses and thought processes to reach a collective decision. Our results show that this "gossip-based consensus" leads to robust, resilient, and accurate multi-agent AI reasoning. It helps overcome the weaknesses of individual models and brings out their collective strengths. This approach is similar to how humans build consensus, making AI seem more collaborative and trustworthy instead of just a black-box program.

cs.MA↗

arXiv2023

EXPLORE -- Explainable Song Recommendation

This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive user interface. Our methodology integrates Spotify data with user preference analytics to tailor music suggestions. Evaluation through RMSE and user studies underscores the efficacy and user satisfaction with our system. The paper concludes with potential directions for future enhancements in group recommendations and dynamic feedback integration.

cs.IR↗