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Sonia Hernandez

Publications and source records attributed to Sonia Hernandez.

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Frequency Standard Contributions to Limitations on the Signal-to-Noise Ratio in Very Long Baseline Interferometric (VLBI) Observations

Since its observation in 2019, the first image of a super-massive black hole using Very Long Baseline Interferometry (VLBI) with an Earth-scale baseline has generated much scientific and public interest, including the possible extension of the baseline into space to obtain higher image resolution. Operating one or more VLBI nodes in space will require the use of frequency standards that are space qualified, greatly reducing the number of options available. The coherence function C(T) is the metric usually used to determine the viability of a frequency standard. Here we show that C(T) is a useful but not sufficient metric for gauging frequency standard performance in VLBI and instead derive an expression for the clock-limited VLBI visibility S/N. We evaluate this expression for real frequency standards and find only the Ultra-Stable Oscillator (USO) and hydrogen maser to be viable for upcoming high-frequency VLBI with the USO only useful for very limited integration times (30s at 90 GHz, 10s at 230 GHz, 5s at 345 GHz, and not viable at 630 GHz). The maser extends these, but may have prohivitive size for a space mission. We also evaluate emerging frequency standard technologies and find the Optical Local Oscillator portion of optical clocks to be very promising (conservatively >100s at 90 GHz, 60s at 230 GHz, 40s at 345 GHz, and 22s at 630 GHz) when accounting for both performance and potential operation in space.

astro-ph.IM

The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students

In this paper, we assess the efficacy of ChatGPT (version Feb 2023), a large-scale language model, in solving probability problems typically presented in introductory computer engineering exams. Our study comprised a set of 23 probability exercises administered to students at Rey Juan Carlos University (URJC) in Madrid. The responses produced by ChatGPT were evaluated by a group of five statistics professors, who assessed them qualitatively and assigned grades based on the same criteria used for students. Our results indicate that ChatGPT surpasses the average student in terms of phrasing, organization, and logical reasoning. The model's performance remained consistent for both the Spanish and English versions of the exercises. However, ChatGPT encountered difficulties in executing basic numerical operations. Our experiments demonstrate that requesting ChatGPT to provide the solution in the form of an R script proved to be an effective approach for overcoming these limitations. In summary, our results indicate that ChatGPT surpasses the average student in solving probability problems commonly presented in introductory computer engineering exams. Nonetheless, the model exhibits limitations in reasoning around certain probability concepts. The model's ability to deliver high-quality explanations and illustrate solutions in any programming language, coupled with its performance in solving probability exercises, suggests that large language models have the potential to serve as learning assistants.

cs.CL