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Ojas Patil

Publications and source records attributed to Ojas Patil.

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Impact on time delays due to milli-lensing by subhalos on lensed gravitational waves

Dark matter substructure properties such as their mass function and spatial distribution depend on the nature of dark matter and are strong tests of the cosmological model. Like luminous matter, these dark matter substructures cause gravitational lensing affecting observables such as time delays. Given the millisecond-level timing precision achievable with current and future gravitational-wave (GW) detectors, gravitationally lensed GWs provide a powerful probe of dark matter substructure, especially, at the lower end of the mass function. In contrast, the optical (or electromagnetic) observations can provide a precision of a few hours and are thus, not sensitive to deflections from the less massive subhalos. In this work, we investigate the impact of realistic population of DM subhalos ($10^6-10^9 M_\odot$) on the lensing time delays for two of the typical strong lens configurations, a fold and a cusp, seen in quadruply lensed sources for a galaxy-scale lens. We find that the subhalos with NFW density profiles cause perturbations of the order of few hours to the lensing time delays between the macro-lensed images produced by the main lensing galaxy. These time delay "anomalies", if not accounted for, may affect the results of strong lens searches conducted on the GW data by the LIGO--Virgo--Kagra collaboration. Lastly, for the 200 realisations analysed per fold and cusp lens systems, we find that the NFW subhalos produced no additional images of their own called milli-images. Statistical studies are needed to better determine the expected impact on the lensed GW time delays and (non-)detection of milli-images.

astro-ph.CO

Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments

Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth. However, developing and evaluating such systems is challenging due to the inherent complexity of enterprise environments, where data is fragmented across multiple sources and governed by sophisticated access controls. We present EnterpriseBench, a comprehensive benchmark that simulates enterprise settings, featuring 500 diverse tasks across software engineering, HR, finance, and administrative domains. Our benchmark uniquely captures key enterprise characteristics including data source fragmentation, access control hierarchies, and cross-functional workflows. Additionally, we provide a novel data generation pipeline that creates internally consistent enterprise tasks from organizational metadata. Experiments with state-of-the-art LLM agents demonstrate that even the most capable models achieve only 41.8% task completion, highlighting significant opportunities for improvement in enterprise-focused AI systems.

cs.LG