Donghui Quan

Identifying fast radio burst (FRB) sources likely to repeat can guide targeted observations and advance our understanding of their populations. We investigate repeater-candidate selection using 18 observational parameters from a Blinkverse-based sample of 3990 sources, including 104 confirmed repeaters. Eight model configurations are compared across four positive-training sizes and 30 random seeds. Positive–unlabelled (PU) Bagging-RF achieves competitive performance, recovering an average of 92.27% of 44 held-out repeaters within the highest-ranked 30% of test sources when trained on 60 confirmed repeaters. We further combine this model with cross-fitted calibration to estimate conditional repeater probabilities for 3886 unlabelled sources. Analysis across assumed repeater fractions quantifies prior sensitivity, while repeated fits characterize ranking stability. The resulting catalogue integrates these estimates with source-specific observational information to support follow-up target selection.

We present a uniform IRAM-30 m survey analysis of four oxygen-bearing complex organic molecules (COMs) – methanol (CH ₃ OH), acetaldehyde (CH ₃ CHO), methyl formate (CH ₃ OCHO), and dimethyl ether (CH ₃ OCH ₃ ) – toward 50 high-mass star-forming regions (HMSFRs) associated with 6.7 GHz methanol masers. Column densities were derived through a homogeneous rotation-diagram approach, with CH ₃ CN used as a proxy excitation-temperature reference when needed. In the CH ₃ OH-normalized abundance-ratio space, CH ₃ OCHO/CH ₃ OH and CH ₃ OCH ₃ /CH ₃ OH show the strongest pairwise correlation, whereas the correlations involving CH ₃ CHO are weaker. No clear monotonic trends are found with galactocentric distance or beam-averaged H ₂ column density. Comparison with previous observations places the CH ₃ OCHO–CH ₃ OCH ₃ behavior within the range of earlier abundance-ratio measurements, while CH ₃ CHO shows larger inter-study variation. A representative warm-up chemical model was used only for qualitative comparison with the observed abundance ranges, which are most closely matched during the decline from the post-desorption abundance peaks in the model. These results provide homogeneous beam-averaged abundance-ratio constraints for common O-bearing COMs in HMS-FRs and show that their source-to-source behavior is molecule-dependent rather than fully described by a single common abundance pattern.

For over a century, the origin of low-energy cosmic rays (LECRs), the dominant heaters and ionizers of dense interstellar gas, has remained elusive owing to solar modulation and uncertain transport processes. In this study, we introduce a new astrophysical approach based on H i narrow self-absorption (HINSA) to obtain spatially resolved measurements of LECR ionization rates using high-fidelity H i observations toward the Orion region from the FAST telescope. The LECR ionization rate is found to scale with local star formation rate as log 10 ζ = ( 1.4 ± 0.70 ) log 10 SFR + ( − 10.5 ± 2.9 ) . Moreover, it increases with visual extinction, and is found to exceed, toward active star-forming regions, the value predicted for diffuse regions based on Voyager measurements and an external propagation model. These findings demonstrate that LECRs are generated in situ by star-forming activities rather than penetrating from the broader Galactic cosmic-ray population. This is further supported by Fermi-LAT gamma-ray observations toward the Orion region. Together, these results resolve a key uncertainty in cosmic-ray origin and establish a new avenue for quantifying the energetic feedback that regulates the interstellar medium.

Molecular inventories are essential for exploring chemical networks and physical conditions, yet their systematic analysis is often hindered by severe line confusion and heterogeneous excitation conditions in spectral surveys. In this paper, we propose a graph-based framework for molecular inventory analysis in Sgr B2(N) that integrates six complementary descriptors into a heterogeneous molecular graph, including source size, rotational temperature, line width, velocity offset, vibrational state, and a Self-Referencing Embedded Strings-based molecular structure representation. Molecular embeddings are learned via multiscale, metapath-guided random walks that jointly encode chemical structure and astrophysical co-occurrence, enabling relationally consistent inference of molecular column densities across 20 subregions of Sgr B2(N) with R ² values ranging from 0.64–0.91. Furthermore, we introduce an interpretability framework combining attribution analysis, prototype retrieval, contrastive molecular analysis, and an embedding-based abundance-informed prioritization metric, showing that the learned embedding preserves molecular structural similarity while capturing environmental drivers of abundance variation. By unifying prediction, interpretation, and abundance-informed prioritization within a single pipeline, this work provides a scalable, data-driven approach to astrochemical inventory analysis in spectral surveys.

Understanding whether prebiotic molecules can endure and reform through the energetic stages of star formation is essential for tracing the continuity of interstellar chemistry toward life. Glycolamide, an isomer of glycine, was recently detected in the molecular cloud G+0.693-0.027. However, establishing its presence in warm, high-density environments is crucial to evaluate the chemical continuity of amides. Here we report the tentative detection of glycolamide in a hot molecular core, G358.93-0.03 MM1, using ALMA 1 mm observations. Seven unblended or only mildly blended emission lines were identified, yielding an abundance of (1.7±0.2)×10 ⁻¹⁰ relative to H ₂ . The comparable formamide/glycolamide and acetamide/glycolamide abundance ratios in both sources suggest a chemically connected amide network across different environments. These results demonstrate that amides can persist and chemically evolve during massive star formation, tracing the chemical continuity from interstellar to protostellar environments.

Context . Linear C ₄ H and cyclic c -C ₃ H ₂ , as small unsaturated hydrocarbons, are the key precursors to complex organic molecules and are critical components of the interstellar medium. However, observational constraints on the evolution of these molecules in late-stage massive star-forming regions remain scarce. Aims . We present on-the-fly mapping observations of C ₄ H 9–8 lines, c -C ₃ H ₂ 2–1, H ¹³ CO ⁺ 1–0, and H42 α toward a sample of 22 massive star-forming regions using the IRAM 30 m telescope. Our aim is to further explore the evolution of these carbon-chain molecules by combining observational results obtained in cold cores. Methods . We employed H ¹³ CO ⁺ 1–0 and H42 α as tracers to probe the positions of molecular cloud cores and ionised hydrogen regions (H II regions), respectively. One chemical model in particular, which includes gas, dust grain surface, and icy mantle phases for C ₄ H and c -C ₃ H ₂ molecules, was used to make comparisons with observed abundances. Results . From mapping observations targeting 31 regions across 22 sources, C ₄ H 9–8 ( J = 19/2–17/2) and C ₄ H 9–8 ( J = 17/2–15/2) were detected in only 17 regions, while H ¹³ CO ⁺ 1–0 and c -C ₃ H ₂ 2–1 were successfully detected in all 31 regions. We find that the emission of C ₄ H 9–8 and c -C ₃ H ₂ 2–1 is concentrated at the edges of H42 α emission regions. The C ₄ H/H ¹³ CO ⁺ and c -C ₃ H ₂ /H ¹³ CO ⁺ relative abundance ratios range from 0.17 to 1.77 (median ~0.57) and 1.42 to 6.69 (median ~4.19), respectively, with a median C ₄ H/ c -C ₃ H ₂ ratio of 0.13. By combining the observational results of cold cores, we find that C ₄ H/H ¹³ CO ⁺ and c -C ₃ H ₂ /H ¹³ CO ⁺ ratios show a strong decreasing trend as molecular cores evolve. Conclusions . The decreasing trends in C ₄ H/H ¹³ CO ⁺ and c -C ₃ H ₂ /H ¹³ CO ⁺ ratios imply that small unsaturated hydrocarbons can be consumed and converted into other organic molecules during the evolution of molecular cores. The spatial concentration of C ₄ H and c -C ₃ H ₂ emission at the edges of H42 α regions further supports their role as precursors in the chemical pathways that lead to complex organic molecules in the interstellar medium.

In this study, we present a transformer-based multitask model for fast radio burst (FRB) detection, signal segmentation, and parameter estimation directly from time–frequency data, without requiring computationally expensive dedispersion preprocessing. To overcome the scarcity of labeled observational data, we develop an FRB simulator and a rule-based automatic annotation pipeline, enabling training exclusively on simulated data. Evaluations on the FAST-FREX data set show that our model achieves an F1 score of 97.8%, recall of 95.7%, and precision of 100%, outperforming both conventional tools (e.g., PRESTO, Heimdall) and recent AI-based baselines (e.g., RaSPDAM, DRAFTS) in both accuracy and inference speed. The model supports pixel-level signal segmentation and yields reliable estimates for dispersion measure and time of arrival. Large-scale blind searches on CRAFTS data further demonstrate robustness, with an average false-positive rate of 0.28% and minimal human verification required. This search has already led to the identification of two pulsar candidates, both confirmed as known pulsars. Processing benchmarks indicate that the model enables real-time searches on a single consumer-grade GPU, making petabyte-scale blind searches feasible. The code is publicly available on GitHub, and the model can be easily integrated with existing tools to automate and streamline radio data analysis beyond FRB or pulsar searches.

Spectral lines from interstellar molecules provide crucial insights into the physical and chemical conditions of the interstellar medium. Traditional spectral line analysis relies heavily on manual intervention, which becomes impractical when handling the massive datasets produced by modern facilities such as the Atacama Large Millimeter/submillimeter Array (ALMA). To address this challenge, we introduce a novel deep reinforcement learning framework to automate spectral line fitting. Using observational data from ALMA, we train a neural network that maps both molecular spectroscopic data and observed spectra to physical parameters such as excitation temperature and column density. The neural network predictions can serve as initial estimates and be further refined using a local optimizer. Our method achieves fitting results consistent with global optimization with multiple runs, while reducing the number of forward modeling runs by an order of magnitude. We apply our method to pixel-level fitting for an observation of the G327.3-0.6 hot core and validate our results using xclass . We perform the fitting for typical complex organic molecules of hot cores, including CH ₃ OH, CH ₃ OCHO, CH ₃ OCH ₃ , C ₂ H ₅ CN, and C ₂ H ₃ CN. For a region of 100 × 100 pixels covering 5 GHz bandwidth, the fitting process requires 4.9–41.9 minutes using a desktop with 16 cores and one consumer-grade GPU card.

To address the high computational cost associated with molecular identification in spectral lines, we propose a graph-based feature learning framework to determine the molecular composition of spectral lines that incorporates rule-driven and node-level constraints to enhance identification accuracy. The approach begins with a sliding-window technique to extract localized spectral features that are then represented as nodes within a heterogeneous graph. To maintain semantic consistency across chemically diverse molecular species, rule-based constraints are introduced. Additionally, node-level constraints are employed to prevent the random walk process from becoming trapped in local cycles. The random walk mechanism is further refined through the integration of 2 parameters that guide node selection. Specifically, we design a frequency-aware sampling strategy in which transition probabilities are dynamically updated according to the number of times each node is used, combined with a stochastic modulation factor to enhance exploration and reduce sampling bias. Furthermore, a dynamic rule selection mechanism is employed to adaptively guide the random walk process in response to the evolving topological structure of the graph. Finally, the effectiveness of the proposed algorithm for molecular identification is validated using simulated data, achieving a recall rate of 94.63%. The method is then applied to the real G327 region, where molecular spectral lines are first identified and then used as inputs for XCLASS fitting to further verify the performance of the proposed algorithm. Moreover, the spatial map of molecular counts within spectral cubes is generated, demonstrating the capability for detailed chemical structure analysis.

Amino acids, the building blocks of life, have long been sought its origin in space (1). Glycine, the simplest amino acid, has been detected on asteroids and comets within the solar system (2, 3). Experimental evidence suggests that glycine could be synthesized in the interstellar medium (ISM) before the formation of stars and planets (4, 5). However, despite numerous efforts, all previous attempts to identify glycine in the ISM have been unsuccessful (6–11). Here we report the first conclusive detection of glycine (NH2CH2COOH) beyond the solar system, identified in the hot corino surrounding the low-mass protostar IRAS 16293–2422B. A total of 19 spectral lines of glycine and its three isomers have been identified. Moreover, physical and chemical models successfully predict the presence of glycine under hot corino conditions, while its absence is noted in a shock-affected molecular cloud. Together, these results provide strong evidence that amino acids form and exist within star-forming regions.