arXiv subjects

Wang Xiaofeng

Publications and source records attributed to Wang Xiaofeng.

3 recordsLinked to original sources

arXiv2025

SN 2022acko: a low-luminosity SNe IIP with signs of early circumstellar interaction

We present optical-ultraviolet photometry and optical spectra for the type II supernova (SN) 2022acko. The spectroscopic observations span phases from $\sim$ 1.5 to $\sim$ 60 days after the explosion, while the light curve was observed up to $\sim$ 300 days. The V-band peak is $-15.5 \pm 0.3$ mag, suggesting that SN 2022acko is a low-luminosity SN II (LLSN). The overall observed properties of SN 2022acko are consistent with those produced by a lower mass progenitor ($\rm M_{ZAMS} \sim $9-10M$_{\odot}$). The spectra at $t=1.5$d and $t=2.5$d exhibit a broad emission feature peaking near 4600 Å(the ``ledge'' feature), which we interpret as blueshifted He II 4686 Ålines arising from the ionized ejecta. Moreover, a possible flash-ionized (FI) emission line of H$α$ (FWHM $\sim 1100\ \rm km \ s^{-1}$) was superposed on the broad emission component of H$α$ P-Cgyni profile in the $t=1.5$d spectrum. Assuming an ejecta velocity of $\rm 12000\ km\ s^{-1}$, the rapid disappearance of this narrow H$α$ emission line within two days suggests highly confined CSM within $\sim \rm 2\times10^{14}\, cm$. Assuming a spherically symmetric CSM, the mass loss rate within this radius is estimated to be $\rm \sim 5 \times 10^{-4} M_{\odot} \ year^{-1}$ based on our hybrid light curve model. The early ``ledge'' feature observed in SN 2022acko have also been observed in other SNe II, suggesting that early-phase circumstellar interaction (CSI) is more common than previously thought.

astro-ph.HE↗

arXiv2025

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts

cs.SE↗

arXiv2023

Autonomous Agents in Software Development: A Vision Paper

Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many software engineering tasks, including automated code generation, debugging, maintenance, etc. However, only a limited number of existing works have thoroughly explored the potential of GPT agents in SE. This vision paper inquires about the role of GPT-based agents in SE. Our vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work. We argue that multiple GPT agents can perform creative and demanding tasks far beyond coding and debugging. GPT agents can also do project planning, requirements engineering, and software design. These can be done through high-level descriptions given by the human developer. We have shown in our initial experimental analysis for simple software (e.g., Snake Game, Tic-Tac-Toe, Notepad) that multiple GPT agents can produce high-quality code and document it carefully. We argue that it shows a promise of unforeseen efficiency and will dramatically reduce lead-times. To this end, we intend to expand our efforts to understand how we can scale these autonomous capabilities further.

cs.SE↗