adsabs/AstroCLIMB huggingface dataset:
Paper: Constraining the Neutron-star Equation of State via Short Gamma-Ray Burst X-Ray Afterglows
Authors: R. Moradi, Y. Wang, F. Rastegarnia, E. S. Yorgancioglu, Shu-Xu Yi, B. Eslam Panah, S. N. Zhang
Figure caption: Schematic (not from simulation): the merger of two neutron stars (NSs) is a leading model for SGRBs. If the remnant’s mass is low enough, it may become an SMNS through secular spin-down. The X-ray plateau phase in SGRBs suggests magnetic spin-down from a rapidly rotating, highly magnetized SMNS. The collapse of this SMNS into a BH likely causes the steep decline in X-ray flux observed at the plateau’s end.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Analytical solutions of models, FM model tori. The black line is for Single, green for Schw, blue and red for Contra and Corot models. See Table 1 for their details.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Poloidal slices of the MRI ﹩{Q}_{\theta }^{\mathrm{MRI}}﹩ quality factor for the Corot model (top panels) and the high-magnetization CorotM model (bottom panels). Left and right panels show the distributions at times t = 10,000 M and t = 80,000 M, respectively. The color scale indicates the magnitude of ﹩{Q}_{\theta }^{\mathrm{MRI}}﹩.
Paper: First Direct Carbon Abundance Measured at z > 10 in the Lensed Galaxy MACS0647-JD
Authors: Tiger Yu-Yang Hsiao, Michael W. Topping, Dan Coe, John Chisholm, Danielle A. Berg, Abdurro'uf, Javier Álvarez-Márquez, Roberto Maiolino, Pratika Dayal, Lukas J. Furtak
Figure caption: Abundance ratios [C/O] (relative to solar) and 12+log(O/H) for MACS0647−JD compared to other galaxies. We include low-z galaxies (the gray crosses; D. A. Berg et al. 2016; P. Senchyna et al. 2017; D. A. Berg et al. 2019), Milky Way Stars (D. C. Nicholls et al. 2017), intermediate-z (z ∼ 1–4) galaxies (the black circles; e.g., M. Pettini et al. 2000; R. A. E. Fosbury et al. 2003; D. K. Erb et al. 2010; J. R. Rigby et al. 2011; L. Christensen et al. 2012; M. B. Bayliss et al. 2014; B. L. James et al. 2014; D. P. Stark et al. 2014; C. C. Steidel et al. 2016; E. Vanzella et al. 2016; R. Amorín et al. 2017; J. R. Rigby et al. 2018; D. A. Berg et al. 2018; A. Citro et al. 2024), as well as high-z galaxies recently studied spectroscopically with JWST: GS-z12 (z = 12.5; the green hexagon; F. D’Eugenio et al. 2024), GN-z11 (z = 10.6; the magenta pentagon; A. J. Cameron et al. 2023), GHZ2 (z = 12.34; the olive square; M. Castellano et al. 2024), RXCJ2248−ID (z = 6.11; the orange circle; M. W. Topping et al. 2024), GLASS−150008 (z = 6.23; the orange diamonds; T. Jones et al. 2023; Y. Isobe et al. 2023), MACS1149−JD1 (z = 9.11; the orange hexagon; M. Stiavelli et al. 2023), GS−z9−0 (z = 9.43; the orange octagon; M. Curti et al. 2025), CEERS−1019 (z = 8.68; the orange triangle; R. Marques-Chaves et al. 2024), A1703−zd6 (z = 7.04; the (fat) orange diamond; M. W. Topping et al. 2025), and s04590 (z = 8.495; the orange cross; K. Z. Arellano-Córdova et al. 2022). Note that galaxies at z > 10 in this plot, except MACS0647−JD, are derived either assuming electron temperature (A. J. Cameron et al. 2023; M. Castellano et al. 2024) or through SED modeling (F. D’Eugenio et al. 2024), displayed with partial transparency. For GS-z12, F. D’Eugenio et al. (2024) presented DLA metallicity for 12 + log(O/H); we also show their 12 + log(O/H) from their BEAGLE SED fitting of 7.59 ± 0.25. We also compare the theoretical predictions of the relation, including metal-poor core-collapse supernovae (CCSN; N. Tominaga et al. 2007; M. Curti 2026), shown in the blue region, and faint supernovae from pure Pop III (I. Vanni et al. 2023; M. Curti 2026), shown in the magenta region.
Paper: First Direct Carbon Abundance Measured at z > 10 in the Lensed Galaxy MACS0647-JD
Authors: Tiger Yu-Yang Hsiao, Michael W. Topping, Dan Coe, John Chisholm, Danielle A. Berg, Abdurro'uf, Javier Álvarez-Márquez, Roberto Maiolino, Pratika Dayal, Lukas J. Furtak
Figure caption: Top: the NIRSpec and MIRI spectrum of MACS0647−JD. The inset shows how we normalize the NIRSpec data to NIRCam photometry with a JD1 magnification of μ = 8. Bottom: emission lines used in this paper, including C III] λλ1907,1909 (top panel), [O III] λ4363 (middle panel), and [O III] λ5008 (bottom panel). C III] λλ1907,1909 and [O III] λ4363 shown are stacked NIRSpec spectra with total magnification μ = 26.6, while [O III] λ5008 is the MIRI observation of JD1 with μ = 8. The black lines show the observed spectrum and the red lines indicate the fit to the spectra and emission lines.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: The first metric as symbolized by a Gaussian curve of possible column densities (CD), here in log value for convenience. The range of column densities is illustrated as a Gaussian distribution to indicate the origin of the sampled column densities, without implying that individual values carry explicit probabilistic meaning. Both spectra represent simulations of the same molecule under all the same conditions, except for column density. The red line in both spectra indicates the threshold of three lines over 5σ. The two simulations here have the SNR applied as described in 2.3. Notably, in this example, the minimum threshold CD (11.31) is lower than the machine learning–predicted column density of 12.01. In total, 81.35% of the distribution of CD would have at least three lines over 5σ, as demonstrated by the pink area under the Gaussian curve. We could make an educated guess, then, that based on this metric alone, this molecule would be an excellent candidate to pursue.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: The process of taking noise from the GOTHAM observational data and applying it to each individual peak of interest in the simulation. Each peak of interest in a spectrum is first identified, followed by choosing a window (defined in the code as a chosen number of full-width-half-maxes around each side) around each peak. The identical matching region is then chosen in the observational GOTHAM data to obtain noise and calculate the rms noise. The rms value is used as the noise for that specific peak’s window in the SNR calculation to be applied to the simulated spectra. Each step is repeated for every peak in the simulated spectrum. This is what allows simulated molecular spectra to be made more specific to what it might look like in TMC–1 with observations made with the GBT.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: Figure reproduced from H. Toru Shay et al. (2025). The detected inventory of TMC–1 molecular species and their column densities is converted into numerical vectors with the VICGAE model. The vectorized strings and the corresponding column densities are then used to train a machine learning regressor. Through learning the relationship between vectorized molecular structures and their column densities, the model is optimized to find the best fit. Once trained, the best-fit model can predict column densities for new molecules and be used to identify potential detection targets in TMC–1.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: Visual aid for the stacking metric. The peaks over a specific threshold are counted and then used as the value n when calculating a new rms for the spectrum. The rest of the process is as previously described.
Paper: The Aromatic Infrared Bands around the Wolf–Rayet Binary WR 140 Revealed by JWST
Authors: Kotomi Taniguchi, Ryan M. Lau, Takashi Onaka, Macarena Garcia Marin, Hideo Matsuhara, Anthony Moffat, Theodore R. Gull, Thomas I. Madura, Gerd Weigelt, Riko Senoo, Alan T. Tokunaga, Walter Duley, Peredur M. Williams, Noel D. Richardson, Joel Sanchez-Bermudez
Figure caption: Relationship between the peak wavelength and FWHM for the 6 and 7.7 μm features. (Left) The plot for the 6 μm feature. Red data points are our results around WR 140. The other shadow regions are taken from D. A. García-Hernández et al. (2013). (Right) The plot for the 7.7 μm feature. The red circles indicate our results around WR 140, and the other cross points are taken from Tables 2 and 3 of D. A. García-Hernández et al. (2013), respectively.
Paper: The Aromatic Infrared Bands around the Wolf–Rayet Binary WR 140 Revealed by JWST
Authors: Kotomi Taniguchi, Ryan M. Lau, Takashi Onaka, Macarena Garcia Marin, Hideo Matsuhara, Anthony Moffat, Theodore R. Gull, Thomas I. Madura, Gerd Weigelt, Riko Senoo, Alan T. Tokunaga, Walter Duley, Peredur M. Williams, Noel D. Richardson, Joel Sanchez-Bermudez
Figure caption: Gaussian fitting results toward positions where the AIBs have been detected. Different filled colors mean that they are treated independently.
Paper: The Aromatic Infrared Bands around the Wolf–Rayet Binary WR 140 Revealed by JWST
Authors: Kotomi Taniguchi, Ryan M. Lau, Takashi Onaka, Macarena Garcia Marin, Hideo Matsuhara, Anthony Moffat, Theodore R. Gull, Thomas I. Madura, Gerd Weigelt, Riko Senoo, Alan T. Tokunaga, Walter Duley, Peredur M. Williams, Noel D. Richardson, Joel Sanchez-Bermudez
Figure caption: Continuum fitting for the observed spectra. Black and red lines indicate the observed spectra and continuum fitting, respectively. The fitting results for the wavelength range of 8.0–12.0 μm are used for analysis only of the 11.3 μm feature.
Paper: The Aromatic Infrared Bands around the Wolf–Rayet Binary WR 140 Revealed by JWST
Authors: Kotomi Taniguchi, Ryan M. Lau, Takashi Onaka, Macarena Garcia Marin, Hideo Matsuhara, Anthony Moffat, Theodore R. Gull, Thomas I. Madura, Gerd Weigelt, Riko Senoo, Alan T. Tokunaga, Walter Duley, Peredur M. Williams, Noel D. Richardson, Joel Sanchez-Bermudez
Figure caption: Gaussian fitting results toward positions at the star WR 140 and close to it (N1, E1, and I1). The AIBs have not been identified at these positions.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: The disk emission of EX Lupi observed at near-infrared and millimeter wavelengths. Left: the color map represents the near-infrared polarimetric QΦ image, capturing scattered light from micron-sized particles, observed with VLT/SPHERE in the H band (λ = 1.6 μm; E. Rigliaco et al. 2020). The red circle marks the inner working angle of the coronagraph with a radius of ﹩0\mathop{.}\limits{^{\prime\prime} }15﹩. Overlaid in white contours is the dust continuum emission, tracing millimeter-sized particles, obtained from ALMA Band 4 observations (λ = 2.0 mm; this work). Contour levels match those in Figure 1. The yellow star symbol denotes the stellar position. Right: the best-fit ellipse to the disk edge is displayed as the white ellipse, while the light white curves indicate the distribution of solutions within the estimated 1σ errors. The blue contours represent the scattered-light image convolved with a Gaussian with a FWHM of ﹩0\mathop{.}\limits{^{\prime\prime} }2﹩. Contour levels are set at [60, 70, 80, 90] × σsca, where σsca is the 1σ uncertainty derived from an emission-free region of the convolved scattered-light image. The white circles along the disk edge indicate the radially averaged positions, measured at azimuthal intervals of 10∘ from the convolved scattered-light image. The blue cross symbol marks the center of the ellipse model, determined from the outer ring in the ALMA continuum image (see Figure 2), and is set as the center position of the EX Lupi system.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Same as Figure 1, but with the CLEAN procedure (Section 2.2). Left: CLEAN model generated by multiscale approach with scale parameters of [0, 1, 3] × θcl, where θcl denotes the CLEAN beam size. The image initially has a unit of janskys per pixel (Jy pixel–1), while it is converted to millijanskys per square arcsecond (mJy arcsec–2). Middle: restored CLEAN image after convolution with an elliptical Gaussian representing the main lobe of the synthesized beam (i.e., the CLEAN beam), along with adding the dirty map of residuals (right). Each white contour corresponds to [3, 4, 5] × σnoise, where σnoise is the rms noise value calculated from the emission-free area (36 μJy beam−1 for Band 3 and 46 μJy beam−1 for Band 4). Right: the residual map. The unit is expressed in rms noise σ. The black contours represent the same as the white ones in the restored CLEAN image.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Schematic view of the EX Lupi disk structure, based on dust continuum and scattered-light imaging observations. The system consists of an inner arc and an outer ring at radius rring on the midplane. The disk has a height Hs(rridge) of the photospheric surface at radius rridge. Due to the inclination (inc) view, the observed disk geometry appears elliptical, with the inclination (inc) angle determined by fitting an ellipse to the projected outer ring on the midplane. The system center is defined to coincide with the center of this ellipse. When viewed at the inclination, the ellipse tracing the edge of the scattering surface appears offset along its minor axis by a measurable displacement Oc from the system center, offering the height of the scattering surface. The inner arc plays a critical role in shaping the illumination pattern: it casts a shadow on the near side (southeast) while allowing direct stellar irradiation to reach the far side (northwest) disk surface, influencing the observed brightness distribution.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Azimuthally averaged radial visibility profiles. The left and right sides correspond to Band 3 and 4 data, respectively. The panels show real parts of the visibilities for the PRIISM image (purple) and the CLEAN model image (orange), together with the observational data (gray). Each profile is binned to 100 data points in total and deprojected to compensate for the disk orientation. The reduced chi-squared values ﹩{\tilde{\chi }}^{2}﹩ calculated from the observed data and the models are shown in the labels of the top panels. The panels display, from top to bottom, the amplitude of the real part of the visibility, the residuals obtained by subtracting the model from the observations, and the SNR of the visibility within each bin in uv space (i.e., the ratio of visibility amplitude to the error obtained from the error propagation of the visibility weight). The more detailed calculation of each panel is described in M. Yamaguchi et al. (2024).
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Panels (a) and (b): deprojected PA profiles for Band 3 and Band 4 model images. The profiles show the radial location of the peak (circle points) and its 50% of the intensity distribution of the inner arc overlaid by the model images. The origin of the orbital radius is the position of the central star. Panel (c): the radial FWHM sizes of the intensity distribution. Panel (d): the peak intensity (circle points). In all panels, the circle symbols denote PA-binned means computed over 10∘-wide bins; the shaded regions indicate the corresponding standard deviations. The vertical bars indicate the geometric mean of the effective spatial resolutions.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Close-up views of the inner arc of EX Lupi in ALMA Band 3 (left) and Band 4 (right) PRIISM model images. The same linear color scale is used. The white stellar symbol indicates the position of the star and the red one the position of the peak intensity of the inner arc. Contour lines at the (40, 50, 60, 70, 80, 90)% value of the peak emission are overplotted. A white bar of ﹩0\mathop{.}\limits{^{\prime\prime} }05﹩ (= 8 au) is provided as a reference to the angular scales. The filled white ellipse indicates the effective spatial resolution θeff.
Paper: Peculiar Disk Substructures Associated with the Young Eruptive Star EX Lupi
Authors: Masayuki Yamaguchi, Hauyu Baobab Liu, Michihiro Takami, Pin-Gao Gu
Figure caption: Posterior distributions of the best-fit ellipse model parameters, which trace the edge of the scattered EX Lupi disk surface as viewed along the line of sight. All parameters are expressed in units of arcseconds. The purple vertical dashed lines indicate the 16th and 84th percentiles, while the orange solid lines mark the most probable values (50th percentile). Contours represent the 68% and 95% confidence regions. The axis limits of each subplot correspond to the parameter range defined by the uniform prior.
Paper: A Comprehensive Characterization of Galaxy-cool CGM Connections at z < 0.4 with DESI Year 1 Data
Authors: Yu Voon Ng, Ting-Wen Lan, J. Xavier Prochaska, Amélie Saintonge, Yu-Ling Chang, Małgorzata Siudek, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, A. Dey, P. Doel, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, M. Ishak, S. Juneau, T. Kisner, A. Kremin, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, J. Moustakas, A. D. Myers, S. Nadathur, C. Poppett, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, J. Silber, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou
Figure caption: SFR–mass distribution of the foreground galaxies. The solid line divides the samples into star-forming and quiescent galaxies. The contours mark the levels of numbers at 10, 100, 500, 1000, 5000, and 8000.
Paper: Improving Solar Flare Nowcasting with the Hot Onset Precursor Event Technique
Authors: Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
Figure caption: Example of the onset detection algorithm using GOES-XRS data, as applied to an X5.8 flare on 2024 May 11 during the 2024 May Gannon storm. The first column shows plots of XRS-A and XRS-B flux, running difference flux (using 3 minute intervals), and rate of change of the running difference flux in the top, middle, and bottom panels, respectively. The second column depicts the isothermal plasma temperature in the first panel, temperature determined from the running difference flux in the second panel, and rate of change of the running difference temperatures in the third panel. Lastly, the third column shows isothermal plasma EM, EM determined from the running difference flux, and the rate of change of this running difference EM in the first, second, and third panels, respectively. The thin red vertical lines indicate the triggers obtained from HOPE nowcasting algorithm, and the thick vertical red line indicates the first trigger. The dashed vertical purple lines indicate the local maximum for the ΔEM rate of change, and the vertical blue lines indicate the flare peak time.
Paper: Improving Solar Flare Nowcasting with the Hot Onset Precursor Event Technique
Authors: Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
Figure caption: Statistical variation of plasma properties during the HOPE phase of 25 flares (ranging from C2.6 to M1.0 class) measured by the DAXSS instrument. Histograms of plasma temperature, EM, and the time difference between the first flare onset trigger and the flare peak are shown in the left, middle, and right panels of the first row. The second row shows the variation of SXR flux, temperature, and EM with the peak flux (in Wm−2) during the onset (blue) and flare peak (red). Gray dotted lines are drawn to guide the eye between the onset and peak parameters of the same flare. Similarly, the third row depicts the AF variation of Mg, Si, and Fe with the peak flux (in Wm−2) in the left, middle, and right panels, respectively. An AF of 1 indicates FSEC abundances.
Paper: Improving Solar Flare Nowcasting with the Hot Onset Precursor Event Technique
Authors: Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
Figure caption: Plasma parameter signatures during the HOPE phase of an M1.0 flare on 2022 August 15. The upper left panel shows the SXR flux measured by XRS-A, XRS-B, and DAXSS. The upper middle and upper right panels show the plasma temperatures and EM, respectively. The middle left panel shows the flux derivative of XRS-A, XRS-B, and DAXSS. The middle and right panels of the middle row show the characteristic temperature vs. EM loop from GOES and DAXSS, respectively. Lastly, the bottom panels show the Mg, Si, and Fe AF variations.
Paper: Improving Solar Flare Nowcasting with the Hot Onset Precursor Event Technique
Authors: Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
Figure caption: DAXSS spectra during times of an M1.0 flare on 2022 August 15. The preflare spectrum is shown in gray, onset start in blue, onset end in red, and impulsive end in green. The line complexes of five low-FIP elements (Mg, Si, S, Ca, and Fe) are also marked with red arrows. Note: sulfur has a FIP of 10.36 eV and is at the border between low-FIP and high-FIP elements. The Mg feature has significant line blending with Fe and Al above 10 MK. Additional details of various spectral features in the DAXSS SXR range are described in Appendix A of T. N. Woods et al. (2023).
Paper: Improving Solar Flare Nowcasting with the Hot Onset Precursor Event Technique
Authors: Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab
Figure caption: Statistical variation of plasma parameters during the HOPE phase of 137 flares measured by the GOES-XRS instrument. The first row shows the results for 44 category (a) flares (C5.0–M1.0). The second row shows results for 58 category (b) flares (M1.0–X1.0), and the third row shows 35 category (c) flares (X1.0 and above). The first column is a histogram of the running difference temperature (ΔT) at the first trigger. The second column is a histogram of the running difference EM (ΔEM) at the local maximum of the (Δ2EM). The third column shows a histogram of the time difference between the first HOPE trigger and the flare peak.