Mark Foreman | Searcharxiv

arXiv2026

Ephemeris Refinement for Qatar-4 b, HAT-P-18 b, and CoRoT-1 b with Small Telescope and TESS Observations

We present updated transit timing measurements for three hot Jupiters (Qatar-4 b, HAT-P-18 b, and CoRoT-1 b) by leveraging data collected from the MicroObservatory Telescope Network, a network of small, robotic ground-based telescopes, and the NASA Transiting Exoplanet Survey Satellite (TESS). By combining these data with archival published results, we present the most precise orbital solutions to date for all three systems, allowing for precise transit time predictions for future missions. We report an updated mid-transit time for Qatar-4 b of 2458919.5838 $\pm$ 0.000089 $\mathrm{BJD}_{\mathrm{TDB}}$ and an updated orbital period of 1.80536560 $\pm$ 0.00000021 days. For HAT-P-18 b, we find a mid-transit time of 2459743.85340 $\pm$ 0.000022 $\mathrm{BJD}_{\mathrm{TDB}}$ and an updated orbital period of 5.50802957 $\pm$ 0.00000012 days. For CoRoT-1 b, we report a mid-transit time of 2456268.99083 $\pm$ 0.000099 $\mathrm{BJD}_{\mathrm{TDB}}$ and an updated orbital period of 1.50896846 $\pm$ 0.000000071 days. Our results demonstrate improvements over recently published ephemerides, with reductions of 36.4%, 4.35%, and 17.5% in mid-transit time uncertainties and 65.0%, 77.4%, and 16.9% in orbital period uncertainties for Qatar-4 b, HAT-P-18 b, and CoRoT-1 b, respectively. The results of this study improve the precision of future transit predictions and demonstrate the value of coordinated small-telescope monitoring (and citizen science initiatives) when updating the orbital parameters of hot Jupiters.

astro-ph.EP↗

arXiv2026

Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight

Predicting human decision-making in high-stakes environments remains a central challenge for artificial intelligence. While large language models (LLMs) demonstrate strong general reasoning, they often struggle to generate consistent, individual-specific behavior, particularly when accurate prediction depends on complex interactions between psychological traits and situational constraints. Prompting-based approaches can be brittle in this setting, exhibiting identity drift and limited ability to leverage increasingly detailed persona descriptions. To address these limitations, we introduce the Large Behavioral Model (LBM), a behavioral foundation model fine-tuned to predict individual strategic choices with high fidelity. LBM shifts from transient persona prompting to behavioral embedding by conditioning on a structured, high-dimensional trait profile derived from a comprehensive psychometric battery. Trained on a proprietary dataset linking stable dispositions, motivational states, and situational constraints to observed choices, LBM learns to map rich psychological profiles to discrete actions across diverse strategic dilemmas. In a held-out scenario evaluation, LBM fine-tuning improves behavioral prediction relative to the unadapted Llama-3.1-8B-Instruct backbone and performs comparably to frontier baselines when conditioned on Big Five traits. Moreover, we find that while prompting-based baselines exhibit a complexity ceiling, LBM continues to benefit from increasingly dense trait profiles, with performance improving as additional trait dimensions are provided. Together, these results establish LBM as a scalable approach for high-fidelity behavioral simulation, enabling applications in strategic foresight, negotiation analysis, cognitive security, and decision support.

cs.AI↗