adsabs/AstroCLIMB huggingface dataset:
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: Distribution of mean [Fe/H] (upper panel) and dispersion of [Fe/H] in the cluster sample of all three simulations (lower panel). m12i is indicated by the dashed lines colored in indigo, m12f is indicated by the dashed–dotted lines colored in red, and m12m is indicated by the solid blue lines. m12f has a larger range of mean and scatter in [Fe/H].
Paper: Observational Signatures of AGN Feedback in the Morphology and the Ionization States of Milky Way-like Galaxies
Authors: Nadia Qutob, Razieh Emami, Kung-Yi Su, Randall Smith, Lars Hernquist, Dian P. Triani, Cameron Hummels, Drummond Fielding, Philip F. Hopkins, Rachel S. Somerville, David R. Ballantyne, Mark Vogelsberger, Grant Tremblay, James F. Steiner, Douglas Finkbeiner, Ramesh Narayan, Minjung Park, Josh Grindlay, Priyamvada Natarajan, Christopher C. Hayward, Dušan Kereš, Sam B. Ponnada, Sirio Belli, Rebecca Davies, Gabriel Maheson, Letizia Bugiani, Yijia Li
Figure caption: The depiction of the mass ratio for the cosmic-ray jet with high (top panel), low (intermediate panel) energy flux, and the default no-jet case (bottom panel) for a few different times, including t = (200, 550, 1000, 1350) Myr. From left to right, we present Mg II, O VI, and O VIII, respectively. All of these plots depict the median values calculated by taking the weighted mass.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: Histograms of the chemical difference metric for pairs of stars within OCs indicated by blue histograms (intracluster pairs) and between OCs indicated by the dashed red histograms (intercluster pairs) for each simulation m12i (top), m12f (middle), and m12m (bottom) in each panel. These clusters were selected to fall within ±1σ from the overall mean distribution of [Fe/H] in each simulation. The bin with the smallest chemical difference metric obtained for both inter- and intracluster pairs is abundantly populated, indicating that such a difference is pretty small to uniquely identify the chemistry of individual clusters.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: Histograms of the chemical difference metric for pairs of stars within OCs, indicated by blue histograms (intracluster pairs), and between OCs, indicated by the dashed red histograms (intercluster pairs), for each simulation: m12i (top), m12f (middle), and m12m (bottom). The chemical difference distributions shown in each column are computed using the method presented Section 2.3, but leverage different elements to compute the chemical difference metric. The left panel is a reproduction of Figure 8, which was generated using all relevant elements in our simulations (C, N, O, Mg, Si, S, Ca, and Ne). The middle column shows the chemical difference distribution calculated using three elements (Mg, C, and N). The right column shows the chemical difference distribution calculated using a different subset of three elements (Mg, C, and O). We note that decreasing the number of elements increases both the number of intracluster pairs and intercluster pairs that populate the smallest-chemical-difference-metric bin. There is slightly more contamination from intercluster pairs when fewer elements are used to calculate the chemical difference metric. While broadly all of the distributions presented here look very similar, the maximum differences between the inter- and intracluster global distributions are found when all elements are used to calculate the metric.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: From left to right, top to bottom: [X/H] vs. [Fe/H] for C, N, O, Mg, Si, and Ca for stars from OCs presented in this paper in m12i. Elements are highly correlated with [Fe/H], as indicated by the Pearson correlation coefficient, which is labeled on each plot (>0.99 for all elements considered). The dotted diagonal red line is the one-to-one line. Note that the slope of the [X/H] vs. [Fe/H] is similar to the one-to-one line and there is limited scatter in [X/H] at any value of [Fe/H]. This indicates that all the elements considered carry similar discriminating power as [Fe/H].
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: For each of the simulations in each panel: blue—the intracluster dispersion indicated as the mean dispersion per element; and indigo—the dispersion around the average dispersion. The indigo splines indicate the ±1σ around it. The gray shaded region highlights the typical measurements of such dispersion from observations, with the gray horizontal line indicating the upper limit. Dashed red—intercluster dispersion. m12f has larger dispersion in both inter- and intracluster abundances, most probably due to an LMC-like merger resulting in a more mixed disk compared to other simulations that do not incorporate such a merger.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: Correlation between [X/H] vs. [Y/H] for C, N, O, Mg, and Ca, color-coded by [Fe/H] (shown in the color bar). Elements are highly correlated with one another, as indicated by the Pearson correlation coefficient (≥0.989 for all elements considered). The dotted diagonal red line is the one-to-one line. Note that the slope of the [X/H] vs. [Y/H] is similar to the one-to-one line and there is limited scatter in [X/H] at any value of [Y/H]. This indicates that all the elements considered carry similar discriminating power.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: Histograms of the chemical difference metric for pairs of stars with mock APOGEE errors in OCs (blue) and between OCs (dashed red) for each simulation. These clusters were selected to fall within the mean ±1σ of the typical intracluster dispersion from the overall mean distribution of [Fe/H] in each simulation. Most of the bins are abundantly populated with the chemical difference metrics obtained from both inter- and intracluster pairs, indicating that such a difference is pretty small to uniquely identify the chemistry of individual clusters.
Paper: Strong Chemical Tagging in FIRE: Intra- and Intercluster Chemical Homogeneity in Open Clusters in Milky Way–like Galaxy Simulations
Authors: Binod Bhattarai, Sarah R. Loebman, Melissa K. Ness, Andrew Wetzel, Emily C. Cunningham, Hanna Parul, Alessa Ibrahim Wiggins
Figure caption: From left to right, top to bottom: [X/Fe] vs. [Fe/H] for C, N, O, Mg, Si, and Ca for stars from OCs presented in this paper in m12i. Elements have a very tight scatter for a given value of [Fe/H], as indicated by the small range in [X/Fe] shown on the y-axes. The scatter is more substantial at lower values of [Fe/H]; however, the scatter decreases substantially at [Fe/H]≥0.19 dex.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: An example of misaligned S0 galaxy. Panel (a) displays the SDSS g-, r-, and i-band false-color images, with the purple hexagon marking the MaNGA bundle. Panels (b) and (c) show the velocity fields for the stellar and gaseous (traced by Hα) components, respectively. The red side moves away from us, while the blue side moves toward us. The black solid lines mark the PA of kinematic major axes, with the two blue dashed lines being their ±1σ errors.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Panel (a) shows the B/T vs. Sérsic index n distribution. Gray dots represent the entire S0 sample, while the blue contours represent the spirals and red contours represent the ellipticals. The green triangles and blue stars are misaligned S0s and misaligned spirals, respectively. The purple arrow indicates the direction of a continuous distribution for misaligned spirals and misaligned S0s. Panel (b) shows the r-band bulge absolute magnitude M  r  vs. bulge effective radius R  e . Gray dots represent the entire S0 sample, while green triangles, magenta squares, and blue stars are misaligned S0s, merger remnant S0s, and misaligned spirals, respectively. The aligned S0s are displayed as gray solid contours, while spiral galaxies and elliptical galaxies are shown as blue dashed and red dashed contours, respectively.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Panels (a) and (c) show the misalignment fraction and merger remnant fraction in the entire MaNGA sample, respectively. Panel (b) shows the misalignment fraction in the emission-line galaxies sample. Blue circles are for spiral galaxies, green squares for S0 galaxies, and red triangles for elliptical galaxies. Each morphological type is divided into young and old population. Gray triangles represent the non-S0 control samples closely matched in M * and global D n 4000 for S0 galaxies. Each point in the same population (young, old, and young+old) is slightly offset in the x-axis to avoid overlap.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: The D n 4000 radial gradients for young (left) and old (right) misaligned S0s (red dashed), aligned S0s (cyan dashed–dotted), and merger remnant S0s (purple dotted). Thick lines represent the median of D n 4000 in each R/R  e  bin, while shaded regions with same color are the ±1σ error of D n 4000 measurement.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Panels (a) and (b) show the distribution of group halo mass M halo and number of group members for galaxies within their galaxy group, respectively. Misaligned S0s, aligned S0s, and merger remnant S0s are represented by red solid, blue dashed–dotted, and purple dashed histograms, respectively. Each distribution is normalized to their sample size. The vertical lines with the same color as histograms are the median of the distributions.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Global D n 4000 vs. ﹩\mathrm{log}({M}_{* }/{M}_{\odot })﹩ diagram for S0 galaxies and their non-S0 control sample, with the gray circles being MPL-10 galaxies. Red squares represent S0 galaxies, and blue open squares are their corresponding controls. The horizontal lines correspond to D n 4000 = 1.6, which split galaxies into young and old population. The top and right panels show the distribution of stellar masses and global D n 4000 for the S0 galaxies (red) and their control samples (blue), respectively, with each distribution normalized with respect to their sample size.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Panel (a) shows the B/T distribution and panel (b) shows the ﹩{\lambda }_{{R}_{e}}﹩ distribution. Misaligned S0s, aligned S0s, misaligned spirals, and aligned spirals are represented by red solid, blue dashed–dotted, cyan shaded, and gray shaded, respectively. Each distribution is normalized to their sample size. The vertical lines with the same color as histograms are the median of the distributions.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Fraction of galaxies with spiral arm features in misaligned and aligned control galaxies with different stellar populations. Red histograms with hatched lines are misaligned galaxies, while the cyan histograms are aligned controls.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: Classification of galaxies with interacting features: (a) ongoing merger, (b) a galaxy with tidal features, (c) a galaxy with an extended asymmetric stellar halo, and (d) a galaxy with a shell.
Paper: Misaligned Gas Acquisition as a Formation Pathway of S0 Galaxies
Authors: Yuren Zhou, Yanmei Chen, Yong Shi, Qiusheng Gu, Junfeng Wang, Dmitry Bizyaev
Figure caption: The distribution of stellar mass M * for misaligned S0s (red solid), aligned S0s (blue dashed–dotted), and merger remnant S0s (purple dashed). Each distribution is normalized to their sample size. The vertical lines with the same color as histograms are the median of the distributions.
Paper: PMN J1310–5552: A Gamma-Ray-emitting Blazar Candidate Harboring a Cosmic Monster
Authors: Vaidehi S. Paliya
Figure caption: Left: the data and the folded absorbed power-law model for the joint Swift-XRT and NuSTAR spectral fitting carried out in the 0.3−79 keV energy range. The spectra were binned to have at least 3σ significance in each bin. Right: the broadband SED of PMN J1310−5552. Various radiative components are shown with different lines as labeled, and the black solid line is the sum of all of them. Gray data points refer to archival observations adopted from the SSDC server.
Paper: PMN J1310–5552: A Gamma-Ray-emitting Blazar Candidate Harboring a Cosmic Monster
Authors: Vaidehi S. Paliya
Figure caption: Left: optical SED of PMN J1310−5552 fitted with the standard accretion disk model. The red line refers to the X-Shooter spectrum, which is shown only for visualization purposes. The fitted model (blue dashed line) corresponds to the optimized L disk and M BH values (“+” mark on the right panel). The green shaded area refers to the 1σ uncertainty in the fitted model. Right: the χ 2 grid of L disk and M BH. The black solid, blue dotted, and red dashed lines represent the confidence contours at 1σ, 2σ, and 3σ levels, respectively. The side plots refer to χ 2 variations as a function of the considered parameters. The gray shaded region shows the 1σ uncertainty in the best-fit parameters, which are highlighted with dashed lines.
Paper: PMN J1310–5552: A Gamma-Ray-emitting Blazar Candidate Harboring a Cosmic Monster
Authors: Vaidehi S. Paliya
Figure caption: The optical spectrum of PMN J1310−5552 taken with X-Shooter (red) and Goodman (black) spectrographs. The inset shows the multi-Gaussian fitting done on the continuum-subtracted Mg II emission line using PyQSOFit. The inset axes units are the same as the main panel.
Paper: PMN J1310–5552: A Gamma-Ray-emitting Blazar Candidate Harboring a Cosmic Monster
Authors: Vaidehi S. Paliya
Figure caption: The M BH (left) and L disk (right) histograms for blazars detected with the Fermi-LAT and Swift-BAT (black empty) and blazars detected only with the Fermi-LAT (gray filled). The location of PMN J1310−5552 is shown with the blue dashed line.
Paper: PMN J1310–5552: A Gamma-Ray-emitting Blazar Candidate Harboring a Cosmic Monster
Authors: Vaidehi S. Paliya
Figure caption: The plot of the accretion disk luminosity vs. jet power. The right plot shows the same quantities normalized for the mass of the black hole. The black dashed line represents the one-to-one correlation.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Example of a composite far-side (FS) phase-shift map on 2011 March 26 made by combining four component maps, each integrated over 24 hr, the consecutive midpoints of which are separated by six hours. Red locus in each frame shows the boundary of the far hemisphere at 24:00 UT. Blue boundaries show the same at times ﹩t^{\prime} ﹩=18:00  UT, 12:00  UT, 06:00  UT, and 00:00  UT. (b) The averaged phase-shift map with the boundaries of the averaged maps at time t (red) and ﹩t^{\prime} ﹩ (blue). (c) Histogram distribution of the averaged maps. The vertical dashed black line indicates the corresponding FS threshold for the averaged phase shift as two times the standard deviation from the mean at 0.0.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: (a) GONG far-side helioseismic map on 2014 July 21, and (b) the corresponding phase-shift distribution. (c) Synchronic EUV map at 304 Å and (d) the corresponding logarithmic intensity distribution.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: (a) Mean (blue) and standard deviation (red) of the standardized logarithmic intensities of the 304 Å EUV synchronic maps. The gray-background sunspot areas (Earth side) are plotted for comparison. (b) Data coverage for the 304  Å EUV synchronic maps, representing the fraction of the expected data that was successfully covered. The gray shaded area represents missing EUV maps.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Illustration of steps in our EUV AR identification algorithm. (a) EUV/304 Å synchronic map on 2011 March 19 at 00:00 UT. (b) Preliminary AR binary map after intensity thresholding. (c) Filling of any holes in region interiors. (d) Morphological closing operation with a square kernel of 25 pixels, followed by another filling. (e) Area thresholding using a morphological opening operation with a square kernel of 1500 pixels. (f) Final EUV AR binary map with labeled enclosing boxes.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Composite phase-shift measurement on 2011 March 19 at 00:00 UT (a), where strong, persistent helioseismic signatures are visible. Binary map (b) is obtained by applying a threshold of −0.07 radian. (c) We limit the area of interest to the latitudes between ±50° (red horizontal lines). (d) Using a morphological opening operation with a kernel of 10 μHemi, all the small regions were removed. The candidate ARs are effectively partitioned and bounded by either green or red rectangles (e), based on their comparisons to the corresponding EUV ARs as true positives or false positives, respectively.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Example of recognition of global and far-side ARs from EUV and GONG maps, respectively, on 2011 November 23. The first row shows (a) the global EUV map in the 304 Å line at time 00:00 UT and (b) the corresponding EUV AR binary map. Second row shows (c) the GONG far-side helioseismic phase-shift map at time 00:00 UT and (d) the corresponding GONG AR binary map. Third row (panels (e) and (f)) represents the missing data set of the EUV observation at time 12:00 UT. Fourth row shows (g) the GONG far-side helioseismic phase-shift map at time 12:00 UT and (h) the corresponding GONG AR binary map.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: (a) Global EUV AR butterfly diagram through the Solar Cycle 24. The color shows the latitudinal sum of all the AR areas in millions of solar hemispheres (μHemi). The gap from mid-2014 to mid-2015 was due to the missing STEREO/EUVI data set. Red horizontal lines at ±50° latitudes divide the map between polar and mid/lower-latitude regions. (b) Total sum of the global EUV AR area with a reference sunspot area (Earth side) in gray background. The sunspot areas were smoothed with a 27 day average. (c) Northern and (d) southern hemispheric global EUV AR areas, with the corresponding sunspot-smoothed areas in the background. The gray shaded area represents missing EUV maps.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: (a) Mean (red) of the negative phase-shift values of the helioseismic far-side GONG maps. The gray-background sunspot areas (Earth side) are used for comparison. (b) Duty cycle (in black) of the helioseismic maps.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Far-side AR butterfly diagrams from (a) EUV observations and (b) GONG phase-shift measurements. (c) The total sum of the far-side AR area from EUV observations (black) and helioseismic GONG measurements (green). (d) Northern and (e) southern hemispheric far-side AR areas from EUV observations (red and blue) and GONG measurements (green), respectively. The gray shaded area represents missing EUV maps.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Binning/discretization of the (a) total, (b) northern, and (c) southern hemisphere AR areas based on the EUV and GONG maps. All the binning is done with equal-width intervals of 100 μHemi.
Paper: Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Authors: Amr Hamada, Kiran Jain, Charles Lindsey, Mitchell Creelman, Niles Oien
Figure caption: Bubble chart illustrating the temporal and spatial distribution of the predicted far-side GONG ARs. Each circle denotes an AR candidate region labeled as a True Positive (TP: green boxes) or False Positive (FP: red boxes) according to its matching with the nearest EUV AR. The circle size is proportional to the area of the region. The gray shaded area represents missing EUV maps.