adsabs/AstroCLIMB huggingface dataset:
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: The fraction of red galaxies as a function of stellar mass is displayed for centrals in the left panel and for satellites in the right panel. Our model predictions from ModA (represented by the red region) and ModB (represented by the green region) are compared with observed fractions from SDSS DR7 (G. De Lucia et al. 2024; represented by magenta and blue diamonds), SDSS-DR8 (M. Hirschmann et al. 2014; represented by orange stars), and the GAMA survey by L. J. M. Davies et al. (2019; represented by purple triangles). Red galaxies are selected based on different cuts in log sSFR, specifically −10.5 for GAMA data and −10.6 and −11.0 for SDSS data. The regions represented by red and green in our models encompass the smallest and largest sSFR cuts, respectively. The plots highlight an important distinction between the two models. While ModA aligns more closely with the observed data in both cases, both models capture the general trend of increasing fraction with increasing mass. However, their predictions are higher than expected for low-mass galaxies and lower than expected for high-mass galaxies. This discrepancy is more pronounced for satellite galaxies, particularly for ModB.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: In the left panel, we compare the stellar mass vs. halo mass relation predicted by our models, ModA and ModB, with those from the models of B. P. Moster et al. (2010; represented by black lines), B. P. Moster et al. (2018; represented by purple lines), and P. Behroozi et al. (2019; represented by cyan lines). The right panel compares the stellar-to-halo-mass ratio vs. halo mass. An apparent difference between ModA and ModB is evident in these plots. While ModA closely aligns with the relation predicted by Moster+10, ModB diverges, showing an increase relative to ModA in low-mass halos (﹩\mathrm{log}{M}_{\mathrm{halo}}\lt 12.7﹩). This divergence exceeds the 2σ scatter represented by the dashed–dotted black lines. Additionally, ModB exhibits a narrower scatter, particularly in low-mass halos. A key takeaway from this figure is that to reproduce the predicted relation on cluster scales, the high-mass end of the SMF at z = 0 must closely match that observed by M. Bernardi et al. (2018). Further discussion on this topic can be found in Section 5.1.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: Fraction of galaxies with different morphologies, classified based on the bulge-to-total ratio (B/T), plotted as a function of stellar mass. Galaxies are categorized as bulge-dominated if they have B/T > 0.7 (represented by red), disk-dominated if B/T < 0.3 (represented by blue), and spheroidals for values in between (represented by green). Our predictions from ModA are shown as solid lines, while those from ModB are represented by dashed lines. These predictions are compared with observed data from A. F. L. Bluck et al. (2019), represented by diamonds. Remarkably, both models demonstrate a high level of accuracy in reproducing the observed trends, which is unprecedented in SAMs.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: The SFR–stellar mass relation at z = 0 predicted by ModA is shown in the left panel, while that predicted by ModB is shown in the right panel. These are compared with the observed main-sequence data by D. Elbaz et al. (2007; represented by blue lines). The contours delineate different percentages of the data, as indicated by the colors in the accompanying bar. The last contour encloses 90% of the full sample, with the remaining 10% represented by individual black dots. Overall, ModA aligns well with the observed main sequence, whereas ModB appears to be slightly biased toward higher values but still falls within the observed scatter. Notably, ModA predicts a broader red sequence that extends toward massive galaxies, while the red sequence predicted by ModB is more concentrated toward less massive galaxies.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: The sSFR distributions across different ranges of stellar mass are displayed in various panels, as indicated in the legend, for both ModA and ModB. These distributions are compared with the equivalent distribution derived from SDSS DR7, represented by black lines. The two models predict distinct distributions, particularly in the range of ﹩10.5\lt \mathrm{log}{M}_{* }\lt 11.0﹩, where ModA appears to align more closely with the observed distributions. Overall, there are no drastic differences observed across either range of stellar mass.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: The metallicity–stellar mass relation predicted by ModA is represented by red lines and dots, while that predicted by ModB is shown with green lines and dots. These predictions are compared with the observed relation from A. Gallazzi et al. (2005), depicted by black lines. While both models capture the general trend of increasing metallicity with stellar mass, neither accurately reproduces the local slopes at masses lower than ﹩\mathrm{log}{M}_{* }\sim 10﹩. Nevertheless, the majority of data points from both models fall within the observed scatter.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: The structure of residual block and SE Block: FC denotes fully connected layer, w 1 and w 2 indicate the number of two FCs.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: Comparison of the stellar parameters predicted by Se-ResNet in this work with target values calculated by Culpan et al. (2022). The dotted lines above are one-to-one lines.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: LAMOST hot subdwarf spectrum (top panel), and its normalized spectrum (bottom panel).
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: The structure of Se-ResNet for whole spectrum input: feature propagation from left to right.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: Example spectrum of a hot subdwarf: Wavelength range from ﹩4000﹩ to ﹩8000\,\mathring{{\rm{A}}}﹩. Some important H and He lines are marked by red dotted lines.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: Example spectra selected from LAMOST data for WD-type, BHB-type, A-type, and hot subdwarf stars.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption: The flowchart of the method.
Paper: Se-ResNet+SVM Model: An Effective Method of Searching for Hot Subdwarfs from LAMOST
Authors: Zhongding Cheng, Xiaoming Kong, Tianmin Wu, Aina Zhang, Bowen Liu, Yude Bu, Zhenxin Lei, Yatao Zhang, Zhenping Yi, Meng Liu
Figure caption:  T eff-log g diagram for hot subdwarf stars. The gray circles represent the hot subdwarf sample from Culpan et al. (2022), and the red diamonds are the 125 hot subdwarf stars with precise parameters identified in this study. The zero-age horizontal branch (ZAHB) and terminal-age horizontal branch (TAHB) from Latour et al. (2018) are depicted as dashed lines with Y = 0.24 and solid lines with Y = 0.40, respectively. The He-MS represented by the solid line is from Paczynski (1971).
Paper: A Comparative Study of Ultraluminous Infrared Galaxies in the IRAS and SDSS Surveys
Authors: Shaohua Zhang, 少华 张, Zhijian Luo, 智坚 罗, Xiheng Shi, 习珩 史, Chenggang Shu, 成钢 束, Hubing Xiao, 胡兵 肖, Hongyan Zhou, 宏岩 周
Figure caption: Top panel: distribution of the angular separation between the second-brightest WISE sources in the W4 band and the SDSS counterpart for each primary ULIRG candidate. The size of each bin of the histogram is 2″. Bottom panel: flux ratio distribution of the second-brightest WISE sources within 2″ of the SDSS counterparts.
Paper: A Comparative Study of Ultraluminous Infrared Galaxies in the IRAS and SDSS Surveys
Authors: Shaohua Zhang, 少华 张, Zhijian Luo, 智坚 罗, Xiheng Shi, 习珩 史, Chenggang Shu, 成钢 束, Hubing Xiao, 胡兵 肖, Hongyan Zhou, 宏岩 周
Figure caption: The 30 × 30 arcsec2 optical images of ULIRGs with obvious interacting features, which are centered on their SDSS counterparts’ positions. For five cases (F09320+6134, F10565+2448, F13428+5608, F14394+5332, and F15327+2340), the scales of the images are 1 × 1 arcmin2.
Paper: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Authors: C. Storfer, X. Huang, A. Gu, W. Sheu, S. Banka, A. Dey, J. Inchausti Reyes, A. Jain, K. J. Kwon, D. Lang, V. Lee, A. Meisner, J. Moustakas, A. D. Myers, S. Tabares-Tarquinio, E. F. Schlafly, D. J. Schlegel
Figure caption: Comparison of overlapping candidates discovered in S22 and our work. The number of candidates from S22 for each of the two grades are shown in red. Also in red are the overlapping systems by grade as a percentage of the number for the respective grade in S22. Conversely, the number of candidates from our search are shown in blue. Also in blue are the overlapping systems by grade as a percentage of the number in the respective grade in our work. For example, 44 are assigned Grade A by this work and S22, which is 10.9% of the total number of Grade As (404) in S22 and 38.3% of the Grade As reported in this work.
Paper: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Authors: C. Storfer, X. Huang, A. Gu, W. Sheu, S. Banka, A. Dey, J. Inchausti Reyes, A. Jain, K. J. Kwon, D. Lang, V. Lee, A. Meisner, J. Moustakas, A. D. Myers, S. Tabares-Tarquinio, E. F. Schlafly, D. J. Schlegel
Figure caption: A comparison of a sample of systems reported in both S22 and this work. For all images, north is up, and east to the left. The scale bar shown in the first image applies to all images in this figure. We show an example system for all permutations of grade assignment given Grades A and B from S22 and Grades A, B, C, and D in this work. The name for each system from this work is shown in the top left, with the name from S22 in parentheses below that. The bottom left shows the grade from this work and below that, the S22 grade in parentheses.
Paper: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Authors: C. Storfer, X. Huang, A. Gu, W. Sheu, S. Banka, A. Dey, J. Inchausti Reyes, A. Jain, K. J. Kwon, D. Lang, V. Lee, A. Meisner, J. Moustakas, A. D. Myers, S. Tabares-Tarquinio, E. F. Schlafly, D. J. Schlegel
Figure caption: Left: redshift distributions of the candidates found in Legacy Surveys DR9 from SDSS Data Release 17 (spectroscopic; dark red) and Zhou et al. (2021, photometric; light red). Right: redshift distribution of the candidates and the lenses included in the training sample. Dashed vertical lines indicate the average redshift of the training sample and candidates.
Paper: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Authors: C. Storfer, X. Huang, A. Gu, W. Sheu, S. Banka, A. Dey, J. Inchausti Reyes, A. Jain, K. J. Kwon, D. Lang, V. Lee, A. Meisner, J. Moustakas, A. D. Myers, S. Tabares-Tarquinio, E. F. Schlafly, D. J. Schlegel
Figure caption: The stacked histogram of the distribution of S22 grades shows a comparison with our grades for the overlapping candidates. Note that grades for the lens candidates reported in this work were assigned prior to S22 being posted on arXiv.
Paper: A Comprehensive Timing Analysis of Individual Pulses in X-Ray Bursts from SGR J0501+4516
Authors: Jiao-Jiao Yang, Shuo Xiao, Zheng-Huo Jiang, Tong-Lei Liao, Mei-Xin Hong, Xi-Hong Luo
Figure caption: The scatter plot illustrates the relationship between the MVT and XRB duration of 27 XRBs from SGR J0501+4516. The horizontal histogram displays the distribution of XRB duration, while the vertical histogram shows the distribution of MVTs. The red curves represent the log-Gaussian function fitting lines for the distribution of the MVT and XRB duration, respectively, and the fitted means are﹩{7.22}_{-2.11}^{+2.93}﹩ and ﹩{185}_{-48}^{+56}﹩ ms, with standard deviations of ﹩{1.48}_{-0.20}^{+0.15}﹩ and ﹩{1.43}_{-0.28}^{+0.33}﹩, respectively.
Paper: A Comprehensive Timing Analysis of Individual Pulses in X-Ray Bursts from SGR J0501+4516
Authors: Jiao-Jiao Yang, Shuo Xiao, Zheng-Huo Jiang, Tong-Lei Liao, Mei-Xin Hong, Xi-Hong Luo
Figure caption: Left: an XRB light curve from SGR J0501+4516 (ObsID 080903421) observed by GBM; the red solid and orange dashed lines are obtained by fitting the FRED function with v free and fixed at 1, respectively. Right: Another XRB from SGR J0501+4516 (ObsID 080822647); the gray line represents the XRB light curve. The dashed lines indicate different pulses identified by the pulse-finding algorithm at different time resolutions, and the red line is obtained by fitting the pulses using multiple FRED functions.
Paper: A Comprehensive Timing Analysis of Individual Pulses in X-Ray Bursts from SGR J0501+4516
Authors: Jiao-Jiao Yang, Shuo Xiao, Zheng-Huo Jiang, Tong-Lei Liao, Mei-Xin Hong, Xi-Hong Luo
Figure caption: Schematic diagram of the pulse-finding algorithm. See Section 2.2 for a detailed description.
Paper: A Comprehensive Timing Analysis of Individual Pulses in X-Ray Bursts from SGR J0501+4516
Authors: Jiao-Jiao Yang, Shuo Xiao, Zheng-Huo Jiang, Tong-Lei Liao, Mei-Xin Hong, Xi-Hong Luo
Figure caption: The fitting results of the light curve in the left panel of Figure 2 using the FRED model based on the MCMC method.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Comparing and examining the correspondence between the heatmaps and the convex hull of PILs (left column: the convex hull of PILs; middle column: ResNet18's heatmaps; right column: CausalNet’s heatmaps). Heatmaps are generated by the GradCAM, where the dark red represents that the neural network is more attentive to the regions it covers, indicating that these regions contribute more than other regions to the flare forecast. The mask overlaid on the heatmap filters out a pixel set with values greater than a certain threshold.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Comparing and examining the correspondence between the heatmaps and the convex hull of PILs (left column: the convex hull of PILs; middle column: ResNet18's heatmaps; right column: CausalNet’s heatmaps). Heatmaps are generated by the GradCAM, where the dark red represents that the neural network is more attentive to the regions it covers, indicating that these regions contribute more than other regions to the flare forecast. The mask overlaid on heatmap filters out a pixel set with values greater than a certain threshold.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Causal intervention (X is input, Y is label, M is a mediator that helps Y, and S is a confounder that hinders Y). (b) The ideal causal intervention that cuts off the confounder S and keeps the mediator M.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Flowchart of model training and data set split. With a data set split, multiple classifiers are trained independently. They are fused together to form the final classifier for flare forecasting. Another classifier is trained for updating the data set split, which outputs the probability of each sample belonging to each partition.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Schematic of a causal attention module (multiple CaaMs are stacked, each of them is composed of a D-block and an M-block).
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: ROC curves of CausalNet and ResNet18 on the test set.
Paper: Causal Attention Deep-learning Model for Solar Flare Forecasting
Authors: Xinze Zhang, Long Xu, Zihan Li, Xin Huang
Figure caption: Chronological data distribution of our established solar flare data set.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: In the top panels, we present the percentage of stellar mass contributed by the positive mode of the AGN feedback as a function of halo mass for central galaxies, while the bottom panels show the same for satellite galaxies. The predictions are made by ModA (red lines) and ModB (green lines) at two redshifts: z = 0 (solid red/green lines) and z = 3 (dashed–dotted black lines). The plots account for all stellar mass attributable to the positive AGN feedback across all branches of the merger tree for each galaxy. Both models indicate minor differences between central and satellite galaxies and predict a decreasing percentage of stellar mass due to positive AGN feedback with increasing stellar mass, scatter included. This figure, combined with Figure 11, suggests that while galaxies can grow significantly when the positive AGN feedback is activated, this growth is largely the result of cumulative growth throughout their assembly histories. When considered individually, growth due to positive AGN feedback never exceeds an average of 7%–8%, even in dwarf galaxies. Interestingly, the trends between the models are reversed at z = 3, with the positive mode in ModA being stronger on average than in ModB. This difference can be attributed to variations in SN feedback and reincorporation times applied in the models, which causes a stronger positive mode at lower redshifts in ModB. For further details, please refer to the text.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: In the top panels, we compare the relations between the stellar mass of central and satellite galaxies from ModA when both modes of the AGN feedback are active (x-axis), against that in which the positive mode is switched off (y-axis). The bottom panels show the same relations for ModB. A notable difference between the two models emerges from this comparison. In ModB, the change in stellar mass for central/satellite galaxies is relatively modest, with approximately 90% of the data points falling within a 2–1 relation represented by the green line. In contrast, ModA exhibits more substantial variations in stellar mass, with several galaxies reaching and even surpassing a 10–1 relation. Excluding the most extreme cases reveals that the distributions of both models are nearly mirror images of the 1–1 relation. Nevertheless, only some central and satellite galaxies are actually more massive in the models where the positive AGN feedback is deactivated. For more details, refer to the text.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: BH vs. stellar mass relations predicted by ModA (with similar trends shown by ModB) presented for galaxies of different morphologies in three panels. The top panel displays the relation for disk galaxies with B/T < 0.3, the middle panel shows the relation for lenticulars with 0.3 < B/T < 0.7, and the bottom panel depicts the relation for ellipticals with B/T > 0.7. These model predictions are compared with the same observed data as shown in the bottom panels of Figure 9. The plots reveal distinct behaviors for each morphology type. Disk galaxies appear slightly biased high compared to observations, following a similar relation but with a higher intercept. Lenticular galaxies closely follow the observed relation. Elliptical galaxies, however, exhibit two distinct populations: one that adheres to a tight correlation different from the observed one, and another that aligns with the observed data cloud from A. E. Reines & M. Volonteri (2015). This underscores the importance of galaxy morphology in understanding the BH vs. stellar mass relation.
Paper: The Impact of Positive AGN Feedback on the Properties of Galaxies in a Semianalytic Model of Galaxy Formation
Authors: Emanuele Contini, Sukyoung K. Yi, Seyoung Jeon, Jinsu Rhee
Figure caption: In the top panels, we compare the BH vs. bulge mass relations predicted by ModA (left) and ModB (right) with observed data and relations from M. C. Bentz & E. Manne-Nicholas (2018), represented by blue lines and red diamonds. In the bottom panels, the BH vs. stellar mass relations are compared with observed data from A. E. Reines & M. Volonteri (2015), depicted by green triangles. Contours in the plots indicate the percentages of data points they enclose, color-coded as shown in the accompanying bar up to 90%. Data points not included within the contours are represented by individual black dots, accounting for the remaining 10%. Both models are fairly capable of describing both relations in a similar manner. However, the second relation is overpredicted at low stellar masses compared to the observations. It should be noted that these observed relations are fitted to data points, the majority of which lie above the low-mass range.
Paper: TESS Light Curves of Cataclysmic Variables. IV. A Synoptic View of Eclipsing Old Novae and Novalike Variables
Authors: Albert Bruch
Figure caption:  O − C diagrams for the stars listed in Table 5 under the assumption of a constant orbital period (except panels (c), (p), and (s)). The red curves are least-squares fits of a second-order polynomial or, in some cases, of a sinusoid, to the data. For details, see the text.