Chengye Li | Searcharxiv

arXiv2026

System noise temperature measurements of a 19-element phased array feed

We present multi-epoch system noise temperature measurements of a 19 element hexagonal room-temperature phased array feed (PAF) using a modified Y-factor method. A Python-based pipeline with iterative channel calibration and maxSNR beam forming is developed to do data reduction, and a covariance-based decomposition scheme is proposed to extract mutual coupling noise temperature Tmc. Long-term observations from March to June 2026 capture temporal receiver noise drift mainly induced by LNA temperature variations. Within the 1.37-1.47 GHz operating band, the measured beam equivalent system noise temperature of the PAF is about 73-89 K. The measured Tmc baseline ranges from about 2 K to 8 K, while under thermal loading, the resonant peak of Tmc can increase to more than 20 K. The baseline of Tmc is consistent with the S-parameter mea surements, which show the Tmc of the full array ranges less than 10.7 K. The baseline fluctuation of Tmc most likely arises from the superposition of thermal and non-thermal components of the spatial electromagnetic fields. A detachable stainless steel shield is deployed to quantify sidelobe spillover noise, and comparative tests show unshielded en vironmental interference increases receiver noise temperature by over 60 K. The proposed measurements and data processing framework provides a practical reference for noise per formance characterization of ground-based radio telescope PAF prototypes.

astro-ph.IM↗

arXiv2025

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning

In-context learning (ICL) allows large language models (LLMs) to solve novel tasks without weight updates. Despite its empirical success, the mechanism behind ICL remains poorly understood, limiting our ability to interpret, improve, and reliably apply it. In this paper, we propose a new theoretical perspective that interprets ICL as an implicit form of knowledge distillation (KD), where prompt demonstrations guide the model to form a task-specific reference model during inference. Under this view, we derive a Rademacher complexity-based generalization bound and prove that the bias of the distilled weights grows linearly with the Maximum Mean Discrepancy (MMD) between the prompt and target distributions. This theoretical framework explains several empirical phenomena and unifies prior gradient-based and distributional analyses. To the best of our knowledge, this is the first to formalize inference-time attention as a distillation process, which provides theoretical insights for future prompt engineering and automated demonstration selection.

cs.LG↗