Green Pea and Blueberry galaxies are well known for their compact size, low mass, strong emission lines, and analogs to high-z Lyα-emitting galaxies. In this study, 1547 strong [O iii] λ5007 emission-line compact galaxies with 1694 spectra are selected from LAMOST DR9 at the redshift range from 0.0 to 0.59. According to the redshift distribution, these samples can be separated into three groups: Blueberries, Green Peas, and Purple Grapes. Optical [Mg ii] λ2800 line feature, BPT diagram, multiwavelength spectral energy distribution (SED) fitting, mid-IR (MIR) color, and MIR variability are deployed to identify 23 active galactic nucleus candidates from these samples, which are excluded for the following star formation rate (SFR) discussions. We perform the multiwavelength SED fitting with GALEX UV and WISE MIR data. Color excess from the Balmer decrement shows that these strong [O iii] λ5007 emission-line compact galaxies are not highly reddened. The stellar mass of the galaxies is obtained by fitting LAMOST calibrated spectra with the emission lines masked. We find that the SFR is increasing with the increase of redshift, while for the sources within the same redshift bin the SFR increases with mass with a similar slope to the star-forming main sequence. These samples have a median metallicity of 12 + log(O/H) of 8.10. The metallicity increases with mass, and all the sources are below the mass–metallicity relation. The direct-derived T _e -based metallicity from the [O iii] λ4363 line agrees with the empirical N2-based empirical gas-phase metallicity. Moreover, these compact strong [O iii] λ5007 lines are mostly in a less dense environment.

Green Pea galaxies are compact galaxies with high star formation rates. However, limited samples of Green Pea galaxies have H i 21 cm measurements. Whether the H i gas fraction (f _{H I} ≡ M _{H I}/M _⋆) of Green Pea galaxies follows the existing scaling relations between the f _{H I} and NUV-r color or linear combinations of color and other physical quantities needs checking. Using archival data of H i 21 cm observations, we investigate the scaling relation of the NUV-r color with the M _{H I}/M _⋆ of 38 Green Pea galaxies, including 17 detections and 21 non-detections. The H i to stellar mass ratios (f _{H I}) of Green Pea galaxies deviate from the polynomial form, where a higher H i gas fraction is predicted given the current NUV-r color, even with the emission lines removed. The blue sources (NUV-r < 1) from the comparison sample (ALFALFA-SDSS) follow a similar trend. The H i gas fraction scaling relations with linear combination forms of − 0.34 ( NUV − r ) − 0.64 log ( μ ⋆ , z ) +5.9 and − 0.77 log μ ⋆ , i + 0.26 logSFR / M ⋆ + 8.53 , better predict the H i gas fraction of the Green Pea galaxies. In order to obtain accurate linear combined forms, higher-resolution photometry from space-based telescopes is needed.

We measure the significance of thermally pulsing asymptotic giant branch (TP-AGB) stars via the spectral energy distributions (SEDs) of a sample of post-starburst (PSB) galaxies at z = 0.2–0.7. Using ground- and space-based photometry from the 3D-HST catalog, as well as associated near-infrared (NIR) Hubble Space Telescope (HST) slitless grism spectroscopy, we evaluate the importance of TP-AGB stars in the SEDs of 177 PSB galaxies by fitting simple stellar populations with different levels of TP-AGB contributions. The grism spectra, despite their low resolution of R ∼ 100, enable the detection of molecular features specific to TP-AGB stars and thus improve constraints on their contribution. A majority (∼70%) of galaxies in the PSB sample show features indicative of TP-AGB stars, while the remainder does not and they are well fit by Bruzual & Charlot TP-AGB light models. Stacked spectra of sources classified to be the best fit by TP-AGB heavy/mild models reveal strong detections of NIR molecular features associated with TP-AGB stars. Additionally, we observe a tentative trend with redshift where more TP-AGB heavy galaxies are observed in the higher redshift PSB galaxy population. Finally, neglecting the contribution of TP-AGB stars can yield an over-prediction of stellar masses measured in the K-band ranging from 0.13–0.23 dex.

Modern time-domain surveys discover transients faster than dedicated spectroscopic facilities can classify them. We test whether archival large-scale multiobject spectroscopy can partly relieve this bottleneck using 108 LAMOST low-resolution spectra of 102 cataloged transients. The sample includes 53 primary supernova (SN) candidates for classifier comparison and 19 externally spectroscopically confirmed SNe for reference. We apply a host-continuum suppression workflow in which direct host-spectrum subtraction is adopted when a clean independent host spectrum provides a stable residual, and STARLIGHT stellar-continuum modeling is adopted when such a spectrum is unavailable or ambiguous. We benchmark SNID, GELATO, and DASH on spectra affected by host light in the 3 . ″ 3 LAMOST fiber. For the 53 SN candidates, GELATO returns candidate classifications for all objects, compared with 24 (45%) for DASH and 12 (23%) for SNID v5.0/templates-2.0; these fractions measure returned yield, not accuracy. In the 12 SNID-pass cases, SNID and GELATO agree at the broad-family level for seven objects (58%); the lower SNID yield is consistent with low-contrast broad SN features in host-contaminated residual spectra, although tool-specific templates, preprocessing, and thresholds also contribute. In this sample, DASH assigns stripped-envelope labels to several spectra for which GELATO returns Type II-family candidate labels. Sixteen candidates (30%) precede maximum light and 18 (34%) are at or before +5 days, reflecting temporal selection and retrospective mining of the decade-scale LAMOST archive rather than dedicated transient monitoring. These results define a practical procedure for serendipitous transient spectroscopy with multifiber survey data.

This article reports a sample of 1830 strong [O III] λ5007 emission-line compact galaxies discovered with the LAMOST spectroscopic survey and the photometric catalog of SDSS. We newly identify 402 spectra of 346 strong [O III]λ5007 emission-line compact galaxies by finding compact isolated point sources. Combined with the samples in our previous work (Liu et al. 2022), this returns a sample of 1830 unique strong [O III]λ5007 emission-line compact galaxies with 2033 spectra of z <= 0.53. For the sources with 2σ [OIII]λ4363 detections, we calculate the gas-phase metallicity with the direct-Te method, and verify that the strong-line metallicity diagnostics calibrated with the direct-Te method also applies to this sample. The strong [O III]λ5007 emission-line compact galaxies fall below several Te-calibrated mass-metallicity relations. The N/O measurements of the strong [O iii]λ5007 emission-line compact galaxies mainly locate at a plateau at low metallicity, indicating the product of primary nucleosynthesis. The Ne3O2 and O32 relation follows a tight linear relation with no redshift evolution. The Ne3O2 anti-correlates with the stellar mass, and at fixed stellar mass the Ne3O2 increase with the redshift. Eight sources with asymmetric [O III]λ5007 emission-line profiles have been identified, however with no [O III]λ4363 detection, which proves the rich metal content and complex ionized gas kinematics within the galaxies. Higher-resolution spectroscopy will be necessary to identify the ionized gas components in detail.

The processing of two-dimensional (2D) spectral images constitutes a critical and multifaceted discipline in contemporary astronomical data analysis. As spectroscopic instruments evolve towards higher multiplexing, resolution, and sensitivity, the raw 2D data captured by detectors present increasingly complex challenges that transcend simple one-dimensional extraction. This review provides a systematic and comprehensive examination of the methodological evolution in this field over the past two decades. It gathered relevant studies by searching mainstream academic repositories and general search engines with the core keyword ‘2D Spectral Image’, and selected qualified references according to accessibility and research relevance. We categorize the landscape into three major paradigms: (1) physics-based modeling and algorithmic correction techniques for geometric distortion, scattered light, and sky background; (2) data-driven machine learning and deep learning approaches for image correction, spectral classification, and faint signal detection; and (3) the development of open-source software pipelines that democratize advanced processing. A central contribution of this review is a detailed comparative analysis of the performance metrics, underlying assumptions, and practical limitations of prominent algorithms. We highlight the transformative impact of convolutional neural networks (CNNs) and vision transformers (ViTs) on tasks such as celestial object classification and exoplanet detection, while also acknowledging the enduring importance of robust physical models for calibration and uncertainty quantification. The discussion culminates in an assessment of persistent challenges—including computational scalability, model generalizability, and interpretability—and outlines promising future directions at the intersection of AI, statistical inference, and large-scale survey science.