adsabs/AstroCLIMB huggingface dataset:
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIV. Long-duration High-cadence Reverberation Mapping Results for 11 PG Quasars
Authors: Chen Hu, Zhu-Heng Yao, Yong-Jie Chen, Yu-Yang Songsheng, Yi-Lin Wang, Sen Yang, Hao Zhang, Wei-Jian Guo, Pu Du, Yan-Rong Li, Ming Xiao, Jun-Rong Liu, Hua-Rui Bai, Feng-Na Fang, Yi-Xin Fu, Yue-Chang Peng, Shuo Zhai, Jin-Ming Bai, Luis C. Ho, Michael S. Brotherton, Jesús Aceituno, Hartmut Winkler, Jian-Min Wang
Figure caption: Light curves, interpolation cross-correlation function analysis, and spectral decomposition of the mean and rms spectra for PG 1411+442, plotted in the same manner as in Figure 4.
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIV. Long-duration High-cadence Reverberation Mapping Results for 11 PG Quasars
Authors: Chen Hu, Zhu-Heng Yao, Yong-Jie Chen, Yu-Yang Songsheng, Yi-Lin Wang, Sen Yang, Hao Zhang, Wei-Jian Guo, Pu Du, Yan-Rong Li, Ming Xiao, Jun-Rong Liu, Hua-Rui Bai, Feng-Na Fang, Yi-Xin Fu, Yue-Chang Peng, Shuo Zhai, Jin-Ming Bai, Luis C. Ho, Michael S. Brotherton, Jesús Aceituno, Hartmut Winkler, Jian-Min Wang
Figure caption: Light curves, interpolation cross-correlation function analysis, and spectral decomposition of the mean and rms spectra for PG 1352+183, plotted in the same manner as in Figure 4.
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIV. Long-duration High-cadence Reverberation Mapping Results for 11 PG Quasars
Authors: Chen Hu, Zhu-Heng Yao, Yong-Jie Chen, Yu-Yang Songsheng, Yi-Lin Wang, Sen Yang, Hao Zhang, Wei-Jian Guo, Pu Du, Yan-Rong Li, Ming Xiao, Jun-Rong Liu, Hua-Rui Bai, Feng-Na Fang, Yi-Xin Fu, Yue-Chang Peng, Shuo Zhai, Jin-Ming Bai, Luis C. Ho, Michael S. Brotherton, Jesús Aceituno, Hartmut Winkler, Jian-Min Wang
Figure caption: Light curves, interpolation cross-correlation function analysis, and spectral decomposition of the mean and rms spectra for PG 1229+204, plotted in the same manner as in Figure 4.
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIV. Long-duration High-cadence Reverberation Mapping Results for 11 PG Quasars
Authors: Chen Hu, Zhu-Heng Yao, Yong-Jie Chen, Yu-Yang Songsheng, Yi-Lin Wang, Sen Yang, Hao Zhang, Wei-Jian Guo, Pu Du, Yan-Rong Li, Ming Xiao, Jun-Rong Liu, Hua-Rui Bai, Feng-Na Fang, Yi-Xin Fu, Yue-Chang Peng, Shuo Zhai, Jin-Ming Bai, Luis C. Ho, Michael S. Brotherton, Jesús Aceituno, Hartmut Winkler, Jian-Min Wang
Figure caption: Light curves, interpolation cross-correlation function analysis, and spectral decomposition of the mean and rms spectra for PG 0844+349, plotted in the same manner as in Figure 4.
Paper: Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIV. Long-duration High-cadence Reverberation Mapping Results for 11 PG Quasars
Authors: Chen Hu, Zhu-Heng Yao, Yong-Jie Chen, Yu-Yang Songsheng, Yi-Lin Wang, Sen Yang, Hao Zhang, Wei-Jian Guo, Pu Du, Yan-Rong Li, Ming Xiao, Jun-Rong Liu, Hua-Rui Bai, Feng-Na Fang, Yi-Xin Fu, Yue-Chang Peng, Shuo Zhai, Jin-Ming Bai, Luis C. Ho, Michael S. Brotherton, Jesús Aceituno, Hartmut Winkler, Jian-Min Wang
Figure caption: Light curves, interpolation cross-correlation function analysis, and spectral decomposition of the mean and rms spectra for PG 1341+258, plotted in the same manner as in Figure 4.
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for C2.7 flare on 2014 November 23 at 18:08:40.36 UTC with GOES15 XRS-B with AIA-131 Å. ALEXIS needs the emission from two regions to recreate the XRS signal; the emission from cluster 2 (green) peaking at 18:10:04 UTC marks the highest data point for the time range under analysis. HARP 4817 and NOAA AR 12209 are responsible for the emission of region 2 (green). Both SolarSoft and the SWPC have reported a C2.7 flare at 18:10:00 UTC occurring in region 1 (orange). They are both accurate and specific in location but they do not report the correct time of the flare. ALEXIS fails to detect the flare occurring in region 1 (orange) at 18:04:00 UTC, which maps to NOAA 12216 and HARP 4851. Note that the magnitude of the only flare detected by ALEXIS is contaminated by the previous emission, another example of how a new labeling scheme independent of the X-ray magnitude of a single pixel is needed to correctly identify the strength of a flare. How does the emission from cluster 1 (orange) influence or increase the probability of magnetic reconnection at the other regions?
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for flare C1.9 on 2014 July 6 at 20:52:40.60 UTC recreated with GOES15 XRS-B with AIA-131 Å. Sympathetic flares could be evidenced by multiple emission, from different spatial locations independent of each other, which occurs at the same moment. Adding the emission from region 0 (blue, NOAA AR 12108 and HARP 4315) and region 3 (green, NOAA AR 12108 and HARP 4296) will account for the X-rays in this time range. This is an example where the same NOAA AR will map to different HARPs. Both the SWPC and SolarSoft are accurate but not specific reporting the flare as happening at NOAA AR 2107 mapping to HARP 4296 (candidate 3). Can we identify how to assign what the true X-ray class of each of the regions should be constrained by the maximum X-ray emission?
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for flare C1.1 on 2011 November 29 at 01:16:00.16 UTC recreated with GOES15 XRS-B with AIA-131 Å. Three flares and three ARs that span across the face of the solar surface. SolarSoft and the SWPC both have the same metadata for location and timing: an event at candidate 1 (yellow) related to NOAA AR 11356 and to HARP 1093. The decay phase of the flare duration in the canonical metadata fails to catch another flare happening at candidate region 2 (green) related to NOAA AR 11361 and to HARP 1119. Previous to both emissions, candidate 0 (blue) also undergoes energy release related to AR 11362 and HARP 1120.
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for flare C1.2 on 2014 March 20 at 16:06:00.21 UTC recreated with GOES13 XRS-A with AIA-131 Å: another example of sympathetic flares. SolarSoft and the SWPC are specific and accurate for a C1.2-class flare that happens at 16:06:00. ALEXIS reports this flare as C2.72. Also, the pipeline is able to identify two more events. These are synchronous flares occurring at 16:18:28 between candidate 0 (blue) and candidate 2 (green).
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for flare C1.9 on 2014 February 6 at 08:01:16.19 UTC recreated with GOES15 XRS-A with AIA-131 Å. ALEXIS reports three flares. The SWPC identifies this flare as having happened at NOAA AR 11968, which maps to HARP 3688. This is the same mapping ALEXIS has for region 0; this SWPC entry is accurate but not specific. The metadata for SolarSoft, on the other hand, identifies their flare to have happened in NOAA AR 11967, which maps to HARP 3686; this SolarSoft entry is accurate but not specific for the ALEXIS flare that occurred in region 2 (orange). Candidate 2 (green) has the strongest emission and dominates the flare, followed by candidate 2 (orange) and candidate 0 (blue).
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for C2.3 flare on 2015 October 27 at 15:49:40.55 UTC recreated with GOES15 XRS-B with AIA-94 Å: example where ALEXIS picks up the background emission before and after the flare. SolarSoft and the SWPC report a flare that occurred within the ALEXIS integration region for cluster 1 (orange) for a C2.9-class flare on 15:49:00 UTC at the exact same coordinate. Both teams are specific; if HARP association for these teams were based on exact coordinates, they both would report HARP 6026. Nevertheless, because in practice mapping would be done from the NOAA AR to HARP, their mapping would identify the flare as occurring at NOAA AR 12435 and HARP 6026. We were not able to associate the background emission from region 0 (blue) to any NOAA AR or HARP. An interesting question for events likes these is can we track candidate regions after the flare into a new time range for analysis?
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for C1.9 flare on 2011 September 15 at 03:08:20.37 UTC recreated with GOES15 XRS-B with AIA-131 Å: example where ALEXIS picks up the background emission before the flare. SolarSoft reports a flare that occurred within the ALEXIS integration region for cluster 1 (orange) for a C1.9-class flare on 03:08:00 UTC; SolarSoft is accurate and specific with HARP 843. Note that a NOAA AR was not reported by SolarSoft. The SWPC reports the same flare peak time but has no specific location data and no NOAA AR; for this example, the SWPC is not accurate or specific. The background emission found by ALEXIS can identify where the X-rays are being produced before the actual flare at NOAA AR 11297 associated with HARP 869 and assigned to candidate 2 (green). Can this be a region that can influence global properties of other ARs and increase their probability to undergo energy release?
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for C1.8 flare on 2013 April 28 at 15:44:00.10 UTC recreated with GOES15 XRS-B with AIA-131 Å: example that demonstrates the benefits of using the full resolution and cadence of AIA. Two flares within the same AR separated by 10 minutes. SolarSoft identifies the two flares and correctly assigns them to a HARP 2693 and NOAA AR 11731, but SolarSoft’s entry at 15:53:00 is not specific. On the other hand, both flares are specific and accurate for the SWPC. Specifying what pixels are representative of flaring regions will help in creating new geometric features.
Paper: ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Authors: Jorge R. Padial Doble, Kelly Holley-Bockelmann
Figure caption: Search for C2.8 flare on 2014 October 20 at 02:01:40.10 UTC recreated with GOES15 XRS-B with AIA-131 Å: example that demonstrates the benefits of using the full resolution and cadence of AIA. Two flares within the same AR separated by 13 minutes where one region (blue) can produce the background and also a new flare that was previously uncataloged. SolarSoft and the SWPC identify a flare accurately and specifically at cluster 1 (orange), correctly assigning NOAA AR 12192 and HARP 4698 to the event.
Paper: Quantified Estimation of Molecular Detections across Different Classes of Neptunian Atmospheres Using Cross-correlation Spectroscopy: Prospects for Future Extremely Large Telescopes with High-resolution Spectrographs
Authors: Dwaipayan Dubey, Liton Majumdar, Charles Beichman, Geoffrey A. Blake, Gautam Vasisht, Thomas Henning
Figure caption: Self-consistent PT structures for different Neptune-class planets. It is constructed using the 1D radiative-convective equilibrium model, petitCODE. This simulation utilized the system parameters listed in Table 1 and elemental abundances specified in Section 2.1. In the legend, the planets are ordered sequentially from highest (HAT-P-11 b) to lowest (TOI-270 d) planetary Teq. All the planets exhibit noninverted PT structures.
Paper: Quantified Estimation of Molecular Detections across Different Classes of Neptunian Atmospheres Using Cross-correlation Spectroscopy: Prospects for Future Extremely Large Telescopes with High-resolution Spectrographs
Authors: Dwaipayan Dubey, Liton Majumdar, Charles Beichman, Geoffrey A. Blake, Gautam Vasisht, Thomas Henning
Figure caption: Planet radius as a function of semimajor axis/host-star radius. The “yellow” scatter points indicate the population of exoplanets detected to date, with data sourced from the NASA Exoplanet Archive (https://exoplanetarchive.ipac.caltech.edu/) on 2024 June 23. The “cyan” points highlight planets with radii between 2 and 3 R⊕, generally categorized as Neptune-class planets. For reference, the radii of Earth and Jupiter are included to illustrate the location of the selected planets within the entire exoplanet population.
Paper: Quantified Estimation of Molecular Detections across Different Classes of Neptunian Atmospheres Using Cross-correlation Spectroscopy: Prospects for Future Extremely Large Telescopes with High-resolution Spectrographs
Authors: Dwaipayan Dubey, Liton Majumdar, Charles Beichman, Geoffrey A. Blake, Gautam Vasisht, Thomas Henning
Figure caption: The specific routine followed to calculate high-resolution line-by-line (R = 106) opacity for certain molecules, as listed in Table 2. ExoMol or HITRAN line lists are used to generate the molecular opacity in petitRADTRANS format for 130 PT grid points. Molecule-specific broadening parameters are also being employed.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: The red gradient colors describe the theoretical H I power spectra in units of mK2, the blue dashed line is the theoretical CMB lensing power spectrum κ–κ, and the purple gradient colors correspond to the theoretical H I–κ cross correlations in units of mK. The gradient evolution changes from darker colors for the lower redshifts to whiter colors for the higher redshifts. In this work, we consider two representative scenarios for the H I IM: LOW-Z  corresponds to H I observations from 980 to 1260 MHz (0.13 < z < 0.45), and HIGH-Z  from 350–1050 MHz (0.39 < z < 3.05).
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: Cross correlations between H I and κ with the H I simulated directly from input theory (blue solid) and the reconstructed H I from FastICA (red solid). We assume that κ is correlated with all H I channels. The left plot shows the cross-power spectrum of a specific frequency channel, i.e., the 20th channel, for the LOW-Z scenario. The right plot shows the cross-power spectrum of the 48th channel for the HIGH-Z scenario. The colored dashed lines are calculated from Equation (19) with variations in the effective number of frequency channels. The results are obtained with ns = 4.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: A window function of CMB lensing κ described in Equation (2) is shown as a purple line, and the H I window functions described in Equation (4) are shown as red and blue steps for the two scenarios considered in this work.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: The mixing matrix WFG due to the foreground removal is obtained via the FastICA method. The subpanels are the matrices with different numbers of independent components (ns). The x- and y-axes correspond to the matrix dimension related to the H I channel indices, and the color bar reflects the relative values of the mixing matrices.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: Validations of the correlated simulations as described in Equations (7) and (8). The simulated band powers are consistent with the theoretical calculations, and the band-power errors are estimated from 100 realizations. Here, we define Dℓ = ℓ(ℓ + 1)Cℓ/2π.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: Different components of the H I autopower spectra for LOW-Z  (left) at z = 0.22 and HIGH-Z  (right) at z = 0.72.
Paper: Investigating Cross Correlations between Cosmic Microwave Background Lensing and 21 cm Intensity Mapping
Authors: Alessandro Marins, Chang Feng, Filipe B. Abdalla
Figure caption: Validations of the cross correlations between CMB lensing and the H I. The correlated simulations are defined in Equations (7) and (8), and the error bars are estimated from 100 realizations. The error bars are multiplied by a factor of 0.2 for visualization purposes.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: The model’s overall structure comprises the backbone, the encoder–decoder structure, and CHAT. The transformer encoder uses multiscale attention for encoding, while the decoder uses SSA for decoding. Collaborative auxiliary training updates the decoder parameters only during training. It does not participate in the forward propagation.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Structure of Deformable DETR. Embedded patches are derived from feature segmentation obtained from the backbone network. These features are processed through N layers (N = 6) of transformer encoder layers to produce intermediate features referred to as memory. The memory undergoes M rounds (M = 6) of cross attention with object queries to generate the model’s predictions.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Illustrations of the five types of SRBs (where Type IIIs represents Type III SRBs appearing in group form).
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: CHAT. The input to the multiscale adapter consists of the restored multiscale features. These multiscale features are processed through auxiliary heads, namely, Faster R-CNN and ATSS, to obtain the selected object queries. The object queries are then fed into the decoder for further updates and training.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Dynamic convolution module. The tensor X represents the features output by the previous layer of the model. After branching, it is processed separately through attention and convolution. The resulting outputs are then combined and passed through an activation function to obtain tensor Y. At this stage, the shape of the vector remains unchanged.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Grad-CAM heatmap comparison of SRB spectrogram images, where red indicates high-attention areas and blue represents low-attention areas.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Faster R-CNN auxiliary head, where cross-entropy loss is used as the classification loss and GIoU is used as the regression loss. Predictions with a GIoU less than 0.5 are considered negative samples, while those with a GIoU greater than or equal to 0.5 are considered positive samples.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Illustrations of various data augmentations on SRB Types II, III, IIIs, IV, and V.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Illustration of multiscale deformable attention. The reference point represents a token that, after obtaining multiscale features, no longer interacts with all other tokens. Instead, it dynamically engages in computations with specific other tokens.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: SSA module. Memory refers to the intermediate image features output by the model. After being restored to multiscale features, these are fed into different dynamic convolutions.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Performance of image augmentation. Note: random flipping is included by default in each experiment. The position of the diagonal indicates that only a single augmentation is applied.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Example of GIoU.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Detection results of SRBs under strong noise interference. The first row displays the ground truth boxes (which are manually annotated), while the second row shows the predicted boxes output by the DETR4SBRs model. It can be observed that, despite the presence of various noise patterns resembling the shape of the eruptions in the spectrogram background, the model is still able to accurately differentiate between the background and the eruption targets. The third row presents the detection results obtained from the gray-scale images.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Model performance in cases of mislabeled and missed annotations. The first row displays the ground truth boxes (which are manually annotated), while the second row shows the predicted boxes output by the DETR4SBRs model. The ground truth is manually annotated. It is noteworthy that the model continues to perform effectively even in areas with annotation errors and missed labels. In particular in the second column, where eruptions are not clearly marked by human annotators, the DETR4SBRs model is still capable of recognizing these instances.
Paper: Solar Radio Burst Detection Based on Deformable DETR
Authors: Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu
Figure caption: Detection results of SRB Types II, III, IIIs, IV, and V. It can be observed that the DETR4SBRs model surpasses the original method in recognizing multiple categories. The DETR4SBRs model demonstrates greater sensitivity to the continuity of eruptions and exhibits more confidence in its bounding box predictions.