adsabs/AstroCLIMB huggingface dataset:
Paper: Atomic Transition Probabilities for Ultraviolet and Optical Lines of Tm II 
Authors: E. A. Den Hartog, G. T. Voith, I. U. Roederer
Figure caption: Logarithmic differences of BFs measured in this study to those in the literature. The top panel shows a comparison to BFs calculated from the theoretical gA-values of Quinet et al. (1999) downloaded from the D.R.E.A.M. database (blue circles; https://agif.umons.ac.be/databases/dream.html) and to the experimental BFs of UW97 (black stars). The lower panel shows the same comparison with UW97 on an expanded scale. Error bars in the lower panel indicate the uncertainties of UW97 and this study combined in quadrature. The solid horizontal lines at 0.0 in each panel indicate perfect agreement, while the dashed lines in the top panel indicate a factor-of-2 difference.
Paper: Atomic Transition Probabilities for Ultraviolet and Optical Lines of Tm II 
Authors: E. A. Den Hartog, G. T. Voith, I. U. Roederer
Figure caption: Comparison of synthetic (lines) and observed (points) spectra around the Tm II line at 2624.34 Å in HD 222925. The red line shows the best-fit abundance, and the shaded regions indicate variations in this best-fit abundance by factors of ±2 (i.e., 0.30 dex). The black line shows a spectrum with no Tm.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Coverage of the GW simulated map (event 927563) taken from Singer et al. (2016) with an integrated 3D strategy from two sites, CTAO North and CTAO South (see Table 2). The probability map (NSIDE = 512) is shown in the global and inset view. The color bar on the left represents the localization probability in the map. The color bar on the right represents the galaxy probability after correlation with the probability map. The galaxies having a probability of more than 1% the maximum galactic probability are represented in the inset view by dots. The circles in the inset view represent the telescope pointings. The start of the observation campaign is set at 2023 March 15 10:30:10 UTC.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Observability and visibility considerations for GW170817 on the night of 2017 August 17–18 for seven observation sites: H.E.S.S., CTAO South, CTAO North, Paranal, Maunakea, MeerKAT, and ASKAP. The localization region is an update computed with the LALInference pipeline (Veitch et al. 2015). The dotted lines show the altitudes of the Sun and Moon as a function of time in each site respectively. The light blue areas represent the times when observation is possible for each observatory (computed with 15 minute time bins). The green lines show the time of the distribution of the alert. From the bottom, radio telescopes are considered to operate during day, dark, and Moon time. Optical telescopes are considered to operate during dark and Moon time, while IACTs are here considered to operate only during darktime. Darktime is defined by the Sun (Moon) being at least 18° (0.°5) below the horizon. The violet line shows the fraction of the probability map that is accessible to the site at a given time.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Coverage of GRB 231012A trigger with an integrated 2D strategy from four telescopes on the same site, LS1, LST2, LST3, and LST4 in La Palma (LP, see Table 1). The probability map (NSIDE = 128) is shown in the globe and inset view. The color bar on the left represents the localization probability in the map. The circles in the inset view represent the telescope pointings. The start of the observation campaign is set at 2023 October 12 17:42:18 UTC.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Comparison of the cumulative probability covered in the GRB 231012A trigger follow-up campaign by the telescopes independently and combined.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Overview of the main workflows within the tilepy Python package.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Coverage of IPN localization map of GRB 20120612 (NSIDE = 2048) with a radio observatory at the Paul Wild Observatory in Australia. The probability map is shown in the globe and inset view. The color bar on the left represents the localization probability in the map. The circles in the inset view represent the telescope pointings. The start of the observation campaign is set at 2017 August 17 10:30:10 UTC. Note that this time has been selected for illustrative purposes, as it corresponds to a period for which observation windows are found at the Paul Wild Observatory.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Comparison of the cumulative galaxy probability covered in the GW simulated event 927563 follow-up campaign by the telescopes independently and combined.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Like Figure 5, but showing coverage of GW190814bv (NSIDE = 1024) with an integrated and targeted 3D strategy from seven telescopes on five different sites for which observations could be scheduled (see Table 3). The start of the observation campaign is set at 2023 September 15 01:30:10 UTC.
Paper: Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events
Authors: Monica Seglar-Arroyo, Halim Ashkar, Mathieu de Bony de Lavergne, Fabian Schüssler
Figure caption: Comparison of the cumulative galaxy probability covered in the GW190814bv trigger follow-up campaign by the telescopes independently and combined.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Swift X-ray maps (N is up, and E to the left) in the 0.3–10 keV band for the 17 sources comprising our sample. All maps, centered at the positions of the SMS4 radio sources, are binned by 4 × 4 pixels and are 8′ × 8′ except for B0344−345, shown at 10′ × 10′. Radio flux density contours from SUMSS, carried out at 843 MHz, overlay the X-ray maps and have been selected for each source to best display the shape of the radio emission. Following Paper I, blue contours distinguish sources detected in the X-rays by Swift in our campaign from nondetected sources, whose contours are marked in red. B1754−597, not detected in our analysis due to the short exposure, was detected by eROSITA in the 1eRASS scan.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Radial profiles in the 0.5–4 keV band of extended SMS4 sources in our sample. For each source, the ER value is given. For all sources, the redshift z is given with the corresponding scale factor. The redshift is spectroscopic for B0344−345, and is photometric for the other two sources. The dotted horizontal line represents the mean background level computed over the whole image, while the dashed line corresponds to the fit of data with the analytic model of the PSF. As is apparent, the radial profiles confirm the extent of these three sources.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption:                         Infrared and optical maps, (matched in scale for each source; N is up and E is to the left), of the 11 SMS4 sources detected by Swift-XRT in our sample and also of B1754−597, detected by eROSITA. Infrared maps (left side of each panel) in the W1 filter (3.4 μm) are from AllWISE, while optical maps (right side; 1 pixel = 1″) in the r filter are from DSS2. Blue ellipses mark the positional uncertainty of G4Jy sources; ellipses are magenta for SUMSS components, also shown for the FR II radio galaxies B0103−453 and B0344−345, with letters corresponding to those assigned in Figure 1. Dashed circles mark the positional uncertainty of the X-ray sources; the circle is black for our Swift-XRT sources, and red for the 1eRASS source corresponding to B1754−597. Positional uncertainties are given at a 90% confidence level for both the radio and the X-ray sources. Crosses (x) and plus signs (+) mark infrared sources from AllWISE and optical sources from GSC 2.4.2. Black or white is equally used for AllWISE and GSC 2.4.2 sources to improve visibility with respect to the map in the background. Green is used for AllWISE counterparts also associated by W20, and for the optical counterpart of B1754−597 suggested by BH06.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: The extended X-ray source PKS 2148−555 (first presented in Paper I). Optical map from DSS2 (left), 0.5–4 keV X-ray map, binned by 8 × 8 pixels, from Swift-XRT (center), and the corresponding 0.5–4 keV radial profile (right). In both the optical and the X-ray maps, N is up and E is to the left. The radial profile is computed up to 5′ from the X-ray centroid, corresponding to the dashed circles (note 1′ = 44.9 kpc). The dotted horizontal line represents the mean background level computed over the whole image, while the dashed line corresponds to the fit of data with the analytic model of the PSF.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Images of B0344−345 (matched in scale; N is up and E to the left), ((a) top left) the 11 ks Swift image (1–4 keV); ((b) top right) WISE 3.4 μm image; ((c) bottom left) GMRT survey field (153 MHz) observation; ((d) bottom right) NVSS (1.4 GHz) image. The Swift data show emission from a compact source, presumably an AGN, as well as diffuse emission, extending about 2′. In the IR image, the three galaxies with measured redshifts (and their redshift values) are indicated. The X-ray source ID is indicated with a black circle and is included in all four images.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Radial profile in the 0.5–4 keV band of a pointlike source in our sample, compared with MRK 876. For each source, the ER value is given, together with the spectroscopic redshift z and the corresponding scale factor. The dotted horizontal line represents the mean background level computed over the whole image, while the dashed line corresponds to the fit of data with the analytic model of the PSF. As discussed in the text, the radial profiles for both sources are consistent with the Swift PSF model.
Paper: A Swift X-Ray View of the SMS4 Sample. II. X-Ray Properties of 17 Bright Radio Sources
Authors: Alessandro Maselli, William R. Forman, Christine Jones, Ralph P. Kraft, Matteo Perri
Figure caption: Swift X-ray map (N is up, and E to the left) in the 0.3–10 keV band of MRK 876, a calibration point source with an exposure of 48795 s. The radius of the white circle C is 5 pixels (1 pixel = ≃2.″36), while the inner and the outer radii of the black dashed annulus A are 10 and 20 pixels, respectively. The map is binned by 4x4 pixels.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Performance analysis of model classification across classes in confusion matrix. Each cell displays the number of candidates and the corresponding fraction (in brackets). The x-axis represents the labels predicted by the model, while the y-axis corresponds to the ground-truth labels. The diagonal cells show correct predictions, and off-diagonal cells highlight misclassifications.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Examples of each class in the data set. From the first row to the sixth row, the classes are cigar, edge on, in-between, round, spiral, mergers. The images in the last row are saturated or error imaging and sampled from the Legacy Surveys.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Examples excluded from the galaxy merger catalog proposed by Darg et al. (2010) for the dependent test.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: The high-level architecture of the proposed method.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Evolution of average accuracy for the seven classes and loss over successive training epochs for both the training and validation data sets.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: The detailed structure of the encoder. The input images undergo Visformer and ResNet-12 in parallel. The respective outputs of these two blocks are then concatenated into feature matrices with dimensions of 640 × 5 × 5.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Visualization of t-SNE embedding with outliers. The insets labeled A to E are samples of outliers.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Examples of mergers identified from the Legacy Surveys.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Samples of identified galaxies with obvious merging structure in the GZ DECaLS project. These galaxies have relatively low voting scores as merging galaxies.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: Precision–recall curve on the independent test data set. It illustrates the variation in precision as the model’s recall increases.
Paper: Identifying Mergers in the Legacy Surveys with Few-shot Learning
Authors: Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu, Junxi Tao
Figure caption: A partial region of a coadded image from the Legacy Surveys, where red boxes indicate the positions of some candidate galaxies.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Comparison of residual contamination levels for DC1 B maps obtained with GLS (solid) and cILC (dashed) for DC1 data, where the residuals are sourced by several components, including the noise bias (green), the diffuse foreground (red), the point sources (blue), and the E-to-B filtering leakage (cyan). Among them, the noise and the filtering leakage are properly debiased by ancillary noise simulations and by the leakage template, respectively, while other contaminations are blind for the data. The GLS (and cILC with a slight shift on the x-axis for a clear display) debiased power spectrum is plotted with x error bars denoting the ℓ-bin width, and y error bars denoting the uncertainty of the noise bias. Note that the residual levels are very comparable between the GLS and cILC methods, with the cILC yielding slightly lower biases for all residuals.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Reconstructed BB band powers of the foreground at 95 × 95 (left), 150 × 150 (middle), and 95 × 150 (right) GHz, for the “p3” (top), “p10” (middle), and “p16” (bottom) models, derived from the TF pipeline using the DC1 data. The solid black line is the mean of the ensemble of trajectories, whereas the dark-gray and light-gray shaded regions indicate the 68.3% (dark gray) and 95.4% (light gray) CLs. Orange squares are the band powers of the DC1 input foreground, and the purple dots show the spectra with extra contribution from residual noise fluctuations. The reconstructed foreground band powers agree well with the DC1 input ones at ℓ ≲ 120 for 95 × 95 and 95 × 150, and at ℓ ≲ 200 for 150 × 150. The disagreement of the 95 × 95 and 95 × 150 band powers at ℓ ≳ 120 is due to the contribution from point sources that are not modeled in the TF pipeline. If we ignore the degeneracy between foreground and CMB, which is very subdominant here, the TF pipeline typically fits the foreground models to the combination of DC1 foreground and residual noise fluctuations, shown as purple dots.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Left: the GLS weights for the CMB components in all seven bands of DC1, with the values close to zero zoomed in on in the subfigure. Right: the final power spectrum recovered for DC1 with the GLS method (orange bars), which has been corrected for noise bias (green dashed) and filtering leakage bias (red dashed). The blue points represent the GLS power spectrum before debiasing. We also show the actual input B-modes at r true = 0.023 used in DC1 (black solid). As a comparison, the ABS-recovered BB spectrum can be seen in Figure 4 of Ghosh et al. (2022).
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: The marginalized r–β  d  (left panel) and β  s –β  d  (right panel) constraints from the TF pipeline with the “p3,” “p10,” and “p16” foreground models. Inner and outer contours indicate the 68.3% confidence level (CL) and 95.4% CL, respectively.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Top: the observed intensity (Stokes I) sky at 353 GHz from the simulation for “region A” (left) and “region B” (right), separated by splitting the fiducial TF mask along R.A. ≃170°, both of which have almost equal sky coverage and geometry, ensuring a fair comparison between the two. Bottom: the corresponding BB band powers and associated 1σ uncertainties for “region A” (left) and “region B” (right) derived from the best-fit models of “p3” (dashed blue), “p10” (solid red), and “p16” (solid green) in 50 ≤ ℓ ≤ 200 at 353 × 353 GHz.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Left: the GLS solution for CMB for DC1 multiplied by the UNP mask. Right: the cILC-cleaned CMB map for DC1 multiplied by the UNP mask.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: Same as Figure 2, but for the cILC weights and corresponding B-mode recovery.
Paper: Forecast of Foreground Cleaning Strategies for AliCPT-1
Authors: Junzhou Zhang, Shamik Ghosh, Jiazheng Dou, Yang Liu, Siyu Li, Jiming Chen, Jiaxin Wang, Zhaoxuan Zhang, Jacques Delabrouille, Mathieu Remazeilles, Chang Feng, Bin Hu, Hao Liu, Larissa Santos, Pengjie Zhang, Wen Zhao, Le Zhang, Zhi-Qi Huang, Hong Li, Xinmin Zhang
Figure caption: CMB TT (top left) and EE (top right) power spectra for ABS (orange) and NILC (blue) methods; the TE (bottom) power spectrum for the NILC (blue) method. The multipole range is 20 ≤ ℓ ≤ 1024 with bin width Δℓ = 30. The Planck-based theoretical spectra as the simulation inputs are plotted in solid black in each panel. The error bars show 2σ statistical uncertainties including cosmic variance, noise sampling variance, and filtering effects as well as foreground residuals after cleaning, which are estimated from 50 realizations of “observed” multifrequency maps with passing through the ABS and NILC pipelines. The ABS- and NILC-derived band power spectra and associated error bars are in good agreement, and all estimated spectra are consistent with the theoretical inputs within the 2σ level. Note that the measured power spectrum matrix (﹩{\tilde{{ \mathcal D }}}_{{ij}}^{\mathrm{obs}}﹩ in Equation (24)) must be symmetric during the ABS calculation to ensure real eigenvalues. However, this condition is not satisfied by the measured TE data, making the ABS currently applicable only to auto power spectra. For the applicability to cross-power spectra, a further extension is required.