arXiv subjects

Jiayue Zhang

Publications and source records attributed to Jiayue Zhang.

9 recordsLinked to original sources

arXiv2026

A Bayesian Search for Planet Engulfment Signatures in Solar Analogs

We present a systematic Bayesian search for chemical fingerprints of planet engulfment in 113 solar twins and analogs with high-precision abundance measurements, 45 of which host known or candidate planets or brown-dwarf companions. We constructed a Bayesian framework with three sets of abundance models: random scatter, Galactic chemical evolution, and planet engulfment with bulk Earth or CM chondrite compositions. Through model comparisons, we identified three candidates whose abundance patterns strongly favor planet engulfment over the alternatives, with inferred engulfed masses of about 7.5-33 Earth masses. Our findings correspond to a nominal detection rate of 1-3% for planet-engulfment signatures among solar analogs. This work extends abundance-based engulfment searches beyond the binary-star context and provides a framework for probing star-planet co-evolution with solar analogs, which goes beyond the commonly used abundance-condensation-temperature correlation (Tc slope).

astro-ph.EP↗

arXiv2026

RV and TTV Measurements of Two Transiting Long-Period Giants around TOI-4600

TOI-4600b and c, originally identified by the Transiting Exoplanet Survey Satellite (TESS) and reported by I. Mireles et al. (2023), are a rare pair of transiting long-period giant planets ($\rm P_b=82.7$ days, $\rm P_c=482.8$ days) orbiting an early K dwarf. In this work, we refine the orbital parameters of the TOI-4600 system by combining new TESS photometry, ground-based transit follow-up, and radial velocity (RV) observations from MAROON-X. We obtain improved constraints on planetary masses and eccentricities, and update other parameters, such as the stellar age. For TOI-4600b, we measure a mass of $M_p = 74.7^{+4.7}_{-4.4}\,M_{\oplus}$ and an eccentricity of $e=0.153^{+0.020}_{-0.018}$, and $M_p = 212.53^{+13.26}_{-13.03}\,M_{\oplus}$ and $e=0.219^{+0.015}_{-0.018}$ for TOI-4600c. We find significant transit timing variations (TTV) in both planets, with semi-amplitudes of approximately $1$\,hr. We derive Transit Spectroscopy Metric values of 16.87 for TOI-4600b and 10.09 for TOI-4600c, indicating that both planets are promising JWST targets for studying the atmospheres of temperate and cold Jupiters, a relatively poorly characterized sample thus far. These updated parameters and TTV ephemerides are important for planning and interpreting future photometric, spectroscopic, and dynamical studies of the TOI-4600 system.

astro-ph.EP↗

arXiv2025

Navigating Uncertainty in ESG Investing

The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning (RL) model, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing the Double-Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-adjusted Capital Asset Pricing Models (CAPMs) to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings, facilitating more informed investment decisions.

q-fin.PM↗

arXiv2025

Joint Liability Model with Adaptation to Climate Change

This paper extends the application of ESG score assessment methodologies from large corporations to individual farmers' production, within the context of climate change. Our proposal involves the integration of crucial agricultural sustainability variables into conventional personal credit evaluation frameworks, culminating in the formulation of a holistic sustainable credit rating referred to as the Environmental, Social, Economics (ESE) score. This ESE score is integrated into theoretical joint liability models, to gain valuable insights into optimal group sizes and individual-ESE score relationships. Additionally, we adopt a mean-variance utility function for farmers to effectively capture the risk associated with anticipated profits. Through a set of simulation exercises, the paper investigates the implications of incorporating ESE scores into credit evaluation systems, offering a nuanced comprehension of the repercussions under various climatic conditions.

q-fin.GN↗

arXiv2025

Strategic Investment to Mitigate Transition Risks

This paper investigates strategic investments needed to mitigate transition risks, particularly focusing on sectors significantly impacted by the shift to a low-carbon economy. It emphasizes the importance of tailored sector-specific strategies and the role of government interventions, such as carbon taxes and subsidies, in shaping corporate behavior. In providing a multi-period framework, this paper evaluates the economic and operational trade-offs companies face under four various decarbonization scenarios: immediate, quick, slow, and no transitions. The analysis provides practical insights for both policymakers and business leaders, demonstrating how regulatory frameworks and strategic investments can be aligned to manage transition risks while optimizing long-term sustainability effectively. The findings contribute to a deeper understanding of the economic impacts of regulatory policies and offer a comprehensive framework to navigate the complexities of transitioning to a low-carbon economy.

econ.GN↗

arXiv2024

Planets Around Solar Twins/Analogs (PASTA) I.: High precision stellar chemical abundance for 17 planet-hosting stars and the condensation temperature trend

The Sun is depleted in refractory elements compared to nearby solar twins, which may be linked to the formation of giant or terrestrial planets. Here we present high-resolution, high signal-to-noise spectroscopic data for 17 solar-like stars hosting planets, obtained with Magellan II/MIKE, to investigate whether this depletion is related to planet formation. We derive stellar parameters, including stellar atmosphere, age, radius, mass, and chemical abundances for 22 elements from carbon to europium through line-by-line differential analysis. Our uncertainties range from 0.01 dex for Fe and Si to 0.08 dex for Sr, Y, and Eu. By comparing the solar abundances to those of the 17 stars, we investigate the differential abundance ([X/Fe]$_{\rm solar}$ - [X/Fe]$_{\rm star}$) versus condensation temperature ($T_c$) trend. In particular, we apply Galactic chemical evolution corrections to five solar twins within the full sample. Our results conform to previous studies that the Sun is relatively depleted in refractory compared to volatile elements. For both five solar twins and the rest of solar-like stars, we find that all stars hosting known gas giant planets exhibit negative $T_c$ trend slopes, suggesting that the Sun is relatively depleted in refractory elements compared to similar giant-planet-host stars. Additionally, we find no correlation between $T_c$ trend slopes and the total mass of detected terrestrial planets in each system, suggesting that terrestrial planet formation may not be the cause of refractory element depletion in the Sun.

astro-ph.SR↗

arXiv2024

Investigation of the effectiveness of applying ChatGPT in Dialogic Teaching Using Electroencephalography

In recent years, the rapid development of artificial intelligence technology, especially the emergence of large language models (LLMs) such as ChatGPT, has presented significant prospects for application in the field of education. LLMs possess the capability to interpret knowledge, answer questions, and consider context, thus providing support for dialogic teaching to students. Therefore, an examination of the capacity of LLMs to effectively fulfill instructional roles, thereby facilitating student learning akin to human educators within dialogic teaching scenarios, is an exceptionally valuable research topic. This research recruited 34 undergraduate students as participants, who were randomly divided into two groups. The experimental group engaged in dialogic teaching using ChatGPT, while the control group interacted with human teachers. Both groups learned the histogram equalization unit in the information-related course "Digital Image Processing". The research findings show comparable scores between the two groups on the retention test. However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test. Electroencephalography data revealed that students who interacted with ChatGPT exhibited higher levels of cognitive activity, suggesting that ChatGPT could help students establish a knowledge foundation and stimulate cognitive activity. However, its strengths on promoting students. knowledge application and creativity were insignificant. Based upon the research findings, it is evident that ChatGPT cannot fully excel in fulfilling teaching tasks in the dialogue teaching in information related courses. Combining ChatGPT with traditional human teachers might be a more ideal approach. The synergistic use of both can provide students with more comprehensive learning support, thus contributing to enhancing the quality of teaching.

cs.CY↗

arXiv2023

Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT-related research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.

cs.CL↗

arXiv2019

Finding Salient Context based on Semantic Matching for Relevance Ranking

In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval. We first propose a new notion of salient context and then define how to measure it. Then we show how the most salient context can be located with a sliding window technique. Finally, we use the semantic similarity between a query term and the most salient context terms in a corpus of documents to rank those documents. Experiments on various collections from TREC show the effectiveness of our model compared to the state-of-the-art methods.

cs.IR↗