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Zijin Su

Publications and source records attributed to Zijin Su.

2 recordsLinked to original sources

LATED: Ly$α$-anchored photometric selection of candidate metal-free and extremely metal-poor star formation from the end of reionisation to cosmic noon

Cosmological simulations allow a low-level tail of Population III (Pop III) star formation to persist to $z=2-6$. Spectroscopic confirmation is expensive, so an efficient photometric pre-selection is needed. We present LATED (Lyman-Alpha Tomography of Extremely Metal-poor Domains), which selects metal-free and extremely metal-poor candidates from a Ly$α$-emitter parent sample using strong-line diagnostics. The method requires three bands and two colours, $x=m_{\rm OIII}-m_{{\rm H}α}$ and $y=m_{{\rm H}α}-m_{\rm cont}$, which trace oxygen abundance and the H$α$ equivalent width, respectively. Requiring that the filters simultaneously contain [O III]+H$β$ and H$α$, together with a Ly$α$ parent selection, defines five windows spanning $z=1.92-6.60$ (four JWST/NIRCam, one Roman/WFI). The criteria, $x\geq x_{\rm min}(z)$ and $y\leq y_{\rm max}(z)$, are set by the per-redshift extrema of a forward-modelled Pop III template locus. Ordinary metal-enriched star-forming and AGN templates fall outside the selection region, and possible contaminants such as little red dots are flagged by their multi-band colours. To check contamination empirically, we apply LATED to 1126 JADES spectroscopic galaxies, and no source is selected in the four NIRCam windows. We release a Python package which converts the same photometry into R3=[O III]/H$β$ as a measurement or upper limit, reproducing JADES spectroscopy with small 0.12 dex scatter. Applied to 85 archival MUSE Ly$α$ emitters in Abell 2744, LATED recovers the confirmed extremely metal-poor galaxy AMORE6 and reveals three new candidates at $z=3-5$. Photometry alone cannot establish a metal-free nature. LATED delivers prioritised candidates for spectroscopic follow-up, providing a scalable route toward a systematic census of late-time Pop III star formation.

astro-ph.GA↗

Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement

Multi-label sentiment classification plays a vital role in natural language processing by detecting multiple emotions within a single text. However, existing datasets like GoEmotions often suffer from severe class imbalance, which hampers model performance, especially for underrepresented emotions. To address this, we constructed a balanced multi-label sentiment dataset by integrating the original GoEmotions data, emotion-labeled samples from Sentiment140 using a RoBERTa-base-GoEmotions model, and manually annotated texts generated by GPT-4 mini. Our data balancing strategy ensured an even distribution across 28 emotion categories. Based on this dataset, we developed an enhanced multi-label classification model that combines pre-trained FastText embeddings, convolutional layers for local feature extraction, bidirectional LSTM for contextual learning, and an attention mechanism to highlight sentiment-relevant words. A sigmoid-activated output layer enables multi-label prediction, and mixed precision training improves computational efficiency. Experimental results demonstrate significant improvements in accuracy, precision, recall, F1-score, and AUC compared to models trained on imbalanced data, highlighting the effectiveness of our approach.

cs.CL↗