adsabs/AstroCLIMB huggingface dataset:
Paper: Optical and Infrared Counterparts of the X-Ray Sources Detected in the Chandra Cygnus OB2 Legacy Survey
Authors: M. G. Guarcello, J. J. Drake, N. J. Wright, T. Naylor, E. Flaccomio, V. L. Kashyap, D. García-Alvarez
Figure caption: Distributions of the median photon energy (left panels) and net counts (right panels) of the X-ray sources with (top panels) and without (bottom panels) OIR counterparts. The vertical dotted lines in the left panels mark the median values of the distributions.
Paper: Optical and Infrared Counterparts of the X-Ray Sources Detected in the Chandra Cygnus OB2 Legacy Survey
Authors: M. G. Guarcello, J. J. Drake, N. J. Wright, T. Naylor, E. Flaccomio, V. L. Kashyap, D. García-Alvarez
Figure caption:  J magnitude of the OIR counterparts vs. the separation between the OIR and the X-ray counterparts for the sources matched only with the NBF or the ML methods.
Paper: Optical and Infrared Counterparts of the X-Ray Sources Detected in the Chandra Cygnus OB2 Legacy Survey
Authors: M. G. Guarcello, J. J. Drake, N. J. Wright, T. Naylor, E. Flaccomio, V. L. Kashyap, D. García-Alvarez
Figure caption: Magnitude distributions in the OSIRIS r (left panel) and UKIDSS J (right panel) bands for the entire OIR catalog (solid histogram) and for the OIR sources matched with the X-ray sources using an individual matching radius as described in Section 4.2.1, i.e., in the PM method (dashed histogram).
Paper: Optical and Infrared Counterparts of the X-Ray Sources Detected in the Chandra Cygnus OB2 Legacy Survey
Authors: M. G. Guarcello, J. J. Drake, N. J. Wright, T. Naylor, E. Flaccomio, V. L. Kashyap, D. García-Alvarez
Figure caption: Spatial distribution of the X-ray sources with reliable OIR counterparts. The red solid lines mark the emission contours at 8.0 μm.
Paper: Optical and Infrared Counterparts of the X-Ray Sources Detected in the Chandra Cygnus OB2 Legacy Survey
Authors: M. G. Guarcello, J. J. Drake, N. J. Wright, T. Naylor, E. Flaccomio, V. L. Kashyap, D. García-Alvarez
Figure caption: Distribution of the J magnitude of the closest OIR sources to the unmatched X-ray sources vs. their angular separation.
Paper: Clump-scale Gas Infall in High-mass Star Formation: A Multitransition View with James Clerk Maxwell Telescope HCN (4–3) Mapping
Authors: Fengwei Xu, Ke Wang, Yuxin He, Jingwen Wu, Lei Zhu, Diego Mardones
Figure caption: Averaged HCN (4–3) lines from the defined HCN cores, whose names are labeled in the top left of each panel. For solid detections, the HCN (4–3) lines are fitted by Gaussian profiles. The 2σ  threshold of the best-fitting model is shown with a green dashed line and a red line, respectively. The results of the Gaussian fitting (amplitude A, centroid velocity Δx, and velocity dispersion σ) are shown on the top right. Nondetection spectra are not fitted, and only the baselines (green horizontal lines) are shown. The systematic velocities in previous surveys are marked with orange dashed lines.
Paper: Clump-scale Gas Infall in High-mass Star Formation: A Multitransition View with James Clerk Maxwell Telescope HCN (4–3) Mapping
Authors: Fengwei Xu, Ke Wang, Yuxin He, Jingwen Wu, Lei Zhu, Diego Mardones
Figure caption: Overview of the 38 clumps. Background shows the Spitzer infrared three-color map (blue: 3.6 μm; green: 4.5 μm; red: 8 μm). White contours are the ATLASGAL 850 μm or the JCMT SCUBA-2 850 μm continuum emission for three sources without ATLASGAL data. The JCMT observing fields are outlined by yellow lines, and the field names are shown in the upper left of each panel. The ATLASGAL/JCMT beam and the scale bar of 1 pc are shown on the lower left and right, respectively.
Paper: Clump-scale Gas Infall in High-mass Star Formation: A Multitransition View with James Clerk Maxwell Telescope HCN (4–3) Mapping
Authors: Fengwei Xu, Ke Wang, Yuxin He, Jingwen Wu, Lei Zhu, Diego Mardones
Figure caption: Workflow of sample selection. Starting from the the 8358 BGPS sources, Schlingman et al. (2011) and Shirley et al. (2013), respectively, performed line surveys, finally covering a total of 6194 BGPS sources. Shirley et al. (2013) cataloged 48 sources with blue profiles, of which 38 are observed by our JCMT HCN (4–3) mapping surveys.
Paper: JWST's PEARLS: Transients in the MACS J0416.1−2403 Field
Authors: Haojing Yan, Zhiyuan Ma, Bangzheng Sun, Lifan Wang, Patrick Kelly, José M. Diego, Seth H. Cohen, Rogier A. Windhorst, Rolf A. Jansen, Norman A. Grogin, John F. Beacom, Christopher J. Conselice, Simon P. Driver, Brenda Frye, Dan Coe, Madeline A. Marshall, Anton Koekemoer, Christopher N. A. Willmer, Aaron Robotham, Jordan C. J. D'Silva, Jake Summers, Mario Nonino, Nor Pirzkal, Russell E. Ryan, Rafael Ortiz, Scott Tompkins, Rachana A. Bhatawdekar, Cheng Cheng, Adi Zitrin, S. P. Willner
Figure caption: (Top two rows) Transients D21-W1, D21-W2, and D21-W3 in the D21 (first row) and the D2c (second row) difference images in the eight NIRCam bands as labeled. The images are 2.″4 on a side and oriented north-up and east-left. The red arrows mark the locations of transients D21-W1, D21-W2, and D21-W3 from left to right in each image. D21-W1 (visible in all epochs) is fainter in D2c (13.3 days apart) than in D21 (83.4 days apart), suggesting that it faded rapidly after reaching its maximum in Ep2. D21-W2 (not visible in Ep1) is almost invisible in Dc2, suggesting that it decayed more slowly. D21-W3 (not visible in Ep1) was similar to D21-W2 (and is only 0.″28 away) but fainter. (bottom three rows, left to right) spectral energy distributions (SEDs) in each observable epoch, light curves in the SW bands, and light curves in the LW bands. D21-W1 was visible in all epochs, and the photometry was done on the original images. The other two transients were not seen in Ep1, and their photometry was done on the difference images with respect to Ep1, i.e., the D21, Dc1, and D31 images.
Paper: JWST's PEARLS: Transients in the MACS J0416.1−2403 Field
Authors: Haojing Yan, Zhiyuan Ma, Bangzheng Sun, Lifan Wang, Patrick Kelly, José M. Diego, Seth H. Cohen, Rogier A. Windhorst, Rolf A. Jansen, Norman A. Grogin, John F. Beacom, Christopher J. Conselice, Simon P. Driver, Brenda Frye, Dan Coe, Madeline A. Marshall, Anton Koekemoer, Christopher N. A. Willmer, Aaron Robotham, Jordan C. J. D'Silva, Jake Summers, Mario Nonino, Nor Pirzkal, Russell E. Ryan, Rafael Ortiz, Scott Tompkins, Rachana A. Bhatawdekar, Cheng Cheng, Adi Zitrin, S. P. Willner
Figure caption: (Top two rows) Transients Dc2-W4 in the Dc2 difference images (first row) and D31-W5, D31-W6, and D31-W7 in the D31 difference images (second row) in eight NIRCam bands. The images are 2.″4 on a side and oriented north-up and east-left. Dc2-W4 (position marked by the yellow arrow in each image) only appeared in Ec and was not visible in any other epoch, including Ep2 only 13.3 days prior. D31-W5, D31-W6, and D31-W7 (positions marked by the green arrows from left to right in each image) were visible only in Ep3, and their detections were significant only in the LW bands. (bottom two rows) SEDs of transients Dc2-W4 and D31-W5, D31-W6, and D31-W7.
Paper: JWST's PEARLS: Transients in the MACS J0416.1−2403 Field
Authors: Haojing Yan, Zhiyuan Ma, Bangzheng Sun, Lifan Wang, Patrick Kelly, José M. Diego, Seth H. Cohen, Rogier A. Windhorst, Rolf A. Jansen, Norman A. Grogin, John F. Beacom, Christopher J. Conselice, Simon P. Driver, Brenda Frye, Dan Coe, Madeline A. Marshall, Anton Koekemoer, Christopher N. A. Willmer, Aaron Robotham, Jordan C. J. D'Silva, Jake Summers, Mario Nonino, Nor Pirzkal, Russell E. Ryan, Rafael Ortiz, Scott Tompkins, Rachana A. Bhatawdekar, Cheng Cheng, Adi Zitrin, S. P. Willner
Figure caption: Locations of the transients found in the Warhol region. The upper panel shows the color images of this region in the four epochs, while the lower panels show the inverted F356W difference images of the same region between relevant epochs that led to the discovery of these transients as labeled. All these images are 2.″4 on a side and are oriented north-up and east-left.
Paper: JWST's PEARLS: Transients in the MACS J0416.1−2403 Field
Authors: Haojing Yan, Zhiyuan Ma, Bangzheng Sun, Lifan Wang, Patrick Kelly, José M. Diego, Seth H. Cohen, Rogier A. Windhorst, Rolf A. Jansen, Norman A. Grogin, John F. Beacom, Christopher J. Conselice, Simon P. Driver, Brenda Frye, Dan Coe, Madeline A. Marshall, Anton Koekemoer, Christopher N. A. Willmer, Aaron Robotham, Jordan C. J. D'Silva, Jake Summers, Mario Nonino, Nor Pirzkal, Russell E. Ryan, Rafael Ortiz, Scott Tompkins, Rachana A. Bhatawdekar, Cheng Cheng, Adi Zitrin, S. P. Willner
Figure caption: Color composite image of M0416 using the data from four epochs of NIRCam observations as described in the text. The color scheme is F090W+F115W+F150W as blue, F200+F277W as green, and F356W+F410M+F444W as red. The regions where the transients are found are also marked.
Paper: Testing Modules for Experiments in Stellar Astrophysics (MESA)
Authors: William M. Wolf, Josiah Schwab, R. Farmer, Evan B. Bauer
Figure caption: Workflow for testing MESA. Developers interact with the MESA TestHub via a web browser. The data for the TestHub comes from instances of mesa_test run on a heterogeneous set of computer systems (laptops, desktops, clusters). The internal operation of one instance of mesa_test is expanded and illustrated in the dotted rectangle.
Paper: Testing Modules for Experiments in Stellar Astrophysics (MESA)
Authors: William M. Wolf, Josiah Schwab, R. Farmer, Evan B. Bauer
Figure caption: An example of the single commit view. Basic information about the commit itself is shown at the top. Below that is a summary of the computers that have submitted tests, where full circles indicate all tests have been run, and an incomplete circle signifies that at least some test cases have not been submitted. Below that, we see three tests with a mixed status (yellow) and one that is failing on all computers.
Paper: Testing Modules for Experiments in Stellar Astrophysics (MESA)
Authors: William M. Wolf, Josiah Schwab, R. Farmer, Evan B. Bauer
Figure caption: Anatomy of a testhub.yml YAML file. Red text comes from each_test_run, green text from do_one, and blue text from MESA itself.
Paper: Testing Modules for Experiments in Stellar Astrophysics (MESA)
Authors: William M. Wolf, Josiah Schwab, R. Farmer, Evan B. Bauer
Figure caption: An example of the commits index view showing commits that are passing, failing, mixed, and untested. The “+” icon indicates that all optional inlists were run for all tests on at least one machine for that commit.
Paper: PEARLS: Near-infrared Photometry in the JWST North Ecliptic Pole Time Domain Field 
Authors: Christopher N. A. Willmer, Chun Ly, Satoshi Kikuta, S. A. Kattner, Rolf A. Jansen, Seth H. Cohen, Rogier A. Windhorst, Ian Smail, Scott Tompkins, John F. Beacom, Cheng Cheng, Christopher J. Conselice, Brenda L. Frye, Anton M. Koekemoer, Nimish Hathi, Minhee Hyun, Myungshin Im, S. P. Willner, X. Zhao, Walter A. Brisken, F. Civano, William Cotton, Günther Hasinger, W. Peter Maksym, Marcia J. Rieke, Norman A. Grogin
Figure caption: Differential galaxy number counts (N  c (m)) measured in this work (black open circles) compared to other measurements in the literature (symbols as noted in the key) for the NIR filters. The vertical line segments indicate the location of the 95%, 80%, and 50% completeness levels of Table 3 for extended sources as dashed, dotted–dashed, and dotted lines, respectively. Data being plotted come from Bielby et al. (2012), Conselice et al. (2008), Cristóbal-Hornillos et al. (2009), Davies et al. (2021), Fontana et al. (2014), Gardner et al. (1993), Iovino et al. (2005), Huang et al. (1997), Jeon et al. (2014), Keenan et al. (2010), Laigle et al. (2016), Metcalfe et al. (2006), and Windhorst et al. (2011). For references where Table 9 tabulates magnitude bins = 0.5, the number counts in galaxies 0.5 mag−1 per unit solid angle were converted into galaxies per unit magnitude per unit solid angle.
Paper: PEARLS: Near-infrared Photometry in the JWST North Ecliptic Pole Time Domain Field 
Authors: Christopher N. A. Willmer, Chun Ly, Satoshi Kikuta, S. A. Kattner, Rolf A. Jansen, Seth H. Cohen, Rogier A. Windhorst, Ian Smail, Scott Tompkins, John F. Beacom, Cheng Cheng, Christopher J. Conselice, Brenda L. Frye, Anton M. Koekemoer, Nimish Hathi, Minhee Hyun, Myungshin Im, S. P. Willner, X. Zhao, Walter A. Brisken, F. Civano, William Cotton, Günther Hasinger, W. Peter Maksym, Marcia J. Rieke, Norman A. Grogin
Figure caption: Star–galaxy separation using the total magnitude (mag_auto) vs. half-light radius measured from the HSC z-band photometry. Green plus signs represent sources classified as stars; small blue squares represent galaxies; brown asterisks represent saturated stars or merged images where one or more sources is a star. For sources without an HSC match within 0.″80, the NIR half-light radius is used, and these are represented by black diamonds. The vertical lines represent the completeness for extended sources for the 95% level (dashed line), 80% level (dotted–dashed line), and 50% level (dotted line).
Paper: PEARLS: Near-infrared Photometry in the JWST North Ecliptic Pole Time Domain Field 
Authors: Christopher N. A. Willmer, Chun Ly, Satoshi Kikuta, S. A. Kattner, Rolf A. Jansen, Seth H. Cohen, Rogier A. Windhorst, Ian Smail, Scott Tompkins, John F. Beacom, Cheng Cheng, Christopher J. Conselice, Brenda L. Frye, Anton M. Koekemoer, Nimish Hathi, Minhee Hyun, Myungshin Im, S. P. Willner, X. Zhao, Walter A. Brisken, F. Civano, William Cotton, Günther Hasinger, W. Peter Maksym, Marcia J. Rieke, Norman A. Grogin
Figure caption: Distribution of the average seeing FWHM and dispersion measured from all images during a night presented in Table 10. The colors represent different filters as noted in the key.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Flow plot representing how AA performance (GFLOPS) depends on parameters of the data and dedispersion plan. Each line symbolizes one run of the autotuned AA GPU dedispersion, with data and a dedispersion plan described by parameters shown in the figure. These parameters are the number of channels, DM trials, DM step, sampling time of the data, and observation time. For simplicity, only the 8-bit input data and a telescope central frequency set to 1400 MHz are shown. The left plot, where we have fixed the observation time and emphasized the DM step (color) and sampling time (hue), shows that the performance is mainly affected by the combination of two parameters: the DM step and the sampling time. Lines of rich dark color, where high sampling time is combined with a large DM step, show the lowest-performing case, where shared-memory GPU code cannot be used. Lines of brighter color have increasing performance as the sampling time gets lower. The right plot, where the sampling time is fixed and only the DM step is emphasized, shows that the performance is independent of the observation time.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Example of the loop tiling in the case of the CPU dedispersion algorithm. One thread computes a partial sum of D f frequency channels (f 1–f 4) of D dm for a number of DM trials (DM1 and DM2) and for D  t  time samples with the same DM trials (t 0–t 4); all of these are represented by the colored dashed lines, where different colors correspond to different DM trials, the x-axis to the time samples, and the y-axis to the frequency channels.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Schematic example of tiling optimization technique in the case of two dimensions (i and j). On the left and on the right, we see the situation when loop blocking is not used and the case when it is, respectively. The original large array is partitioned into smaller blocks (blue rectangle), which can fit into cache size.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Representation of the incoherent dedispersion approach. The top panel represents the input data, and the bottom shows the results (output). For simplicity, we present a clear single signal. The dotted lines correspond to individual DM trials (the summation of data points along the line) computed for a given time sample (showed for DM trials 1, 2, i and N). The sum of the line maps to one point (cross) in the output field.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: The output (dm, t) plane is partitioned into sections of size (D dm, D t) that are each processed by a single thread block, shown in blue.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Data are loaded from cache lines of constant frequency and contiguous time (gray boxes). Multiple dedispersion trials DM = constant, t values (red lines) are held in registers, and each thread updates a set of these in parallel.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Execution times of AstroAccelerate and Heimdall on different many-core platforms for three central frequencies and bit precisions operating on a simulated signal of an observation length of ∼300 s for the low central frequency and ∼50 s for all other central frequencies. The top row corresponds to the central frequency f c = 400, the middle row to f c = 800, and the bottom row to f c = 1400 MHz (low, mid, and high). In the left column are the results for 4-bit precision, in the middle column those for 8-bit precision, and in the right column those for 16-bit precision. The gray boxes show the execution time including all PCIe transfers and overheads needed to finish the plan. The color (lime green or dark green) bar shows the execution time of all kernels, for example, transposing of the input data, downsampling data, etc., needed by the pipeline.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Performance in FLOPS with an increasing number of channels for all tested codes. The first three rows correspond to 4-bit, 8-bit, and 16-bit precision.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: The execution time of the corresponding dedispersion plan (see Table 2) for 4-bit, 8-bit, and 16-bit precision (from top to bottom) input data with an increasing number of channels, observation time T = 10 s, central frequency f c = 1400 MHz, and total bandwidth of 300 MHz.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Zoomed-in normalized output of the dedispersion transform (DDTR) for Heimdall (top, green), Amber (middle, purple), and AstroAccelerate (bottom, orange) of an injected signal with DM = 90.0 pc cm−3.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Comparison of the dedispersed (DDTR) outputs and their percentage difference at DM = 90 pc cm−3 between AstroAccelerate, Heimdall, and Amber at the top, and our CPU implementation at the bottom.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Visualization of the continuous Heimdall survey DM plan with a discrete AstroAccelerate DM plan for all three cases: f  c  = 400, 800, and 1400 MHz (from left to right). Details can be found in Table 3.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Percentage difference of the normalized dedispersion outputs. From top to bottom: AstroAccelerate and Heimdall; AstroAccelerate and Amber.
Paper: Accelerating Dedispersion Using Many-core Architectures
Authors: Jan Novotný, Karel Adámek, M. A. Clark, Mike Giles, Wes Armour
Figure caption: Shared memory bandwidth, shown as a percentage of the theoretical maximum achieved by AstroAccelerate dedispersion for different bit precisions and an increasing number of channels. The theoretical maximum of shared memory bandwidth for each GPU card is in the top right corner.
Paper: Exploring the Ability of Hubble Space Telescope WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres through a Homogeneous Transmission Survey of 70 Gaseous Planets
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Kai Hou Yip, Ahmed F. Al-Refaie, Lara Anisman, Michelle F. Bieger, Amélie Gressier, Sho Shibata, Nour Skaf, Jeroen Bouwman, James Y-K. Cho, Masahiro Ikoma, Olivia Venot, Ingo Waldmann, Pierre-Olivier Lagage, Giovanna Tinetti
Figure caption: Fits from our BHM to the water ratio which was determined using the methods of Welbanks et al. (2019). Left: BHM applied to retrievals that yielded a fit which was preferred to >3σ compared to the flat model. Right: BHM applied to retrievals that yielded a fit which was preferred to >2σ compared to the flat model. The fits for a linear trend fit (top) and flat trend (i.e., constant with mass, bottom) are shown, with the thick line indicating the best-fit model and the thinner lines representing the traces from the fit that fell within 1σ of the best-fit model. In both cases, the flat model is preferred, implying we cannot conclude there is a trend between planet mass and the water-to-hydrogen ratio from these data.
Paper: Exploring the Ability of Hubble Space Telescope WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres through a Homogeneous Transmission Survey of 70 Gaseous Planets
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Kai Hou Yip, Ahmed F. Al-Refaie, Lara Anisman, Michelle F. Bieger, Amélie Gressier, Sho Shibata, Nour Skaf, Jeroen Bouwman, James Y-K. Cho, Masahiro Ikoma, Olivia Venot, Ingo Waldmann, Pierre-Olivier Lagage, Giovanna Tinetti
Figure caption: Retrieved metallicity from our GGChem runs. Left: BHM applied to retrievals that yielded a fit which was preferred to >3σ compared to the flat model. Right: BHM applied to retrievals that yielded a fit which was preferred to >2σ compared to the flat model. The fits for a linear trend fit (top) and flat trend (i.e., constant with mass, bottom) are shown with the thick line indicating the best-fit model and the thinner lines representing the traces from the fit that fell within 1σ of the best-fit model. In both cases, the flat model is preferred, implying we cannot conclude there is a trend between planet mass and the metallicity from these data.
Paper: Exploring the Ability of Hubble Space Telescope WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres through a Homogeneous Transmission Survey of 70 Gaseous Planets
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Kai Hou Yip, Ahmed F. Al-Refaie, Lara Anisman, Michelle F. Bieger, Amélie Gressier, Sho Shibata, Nour Skaf, Jeroen Bouwman, James Y-K. Cho, Masahiro Ikoma, Olivia Venot, Ingo Waldmann, Pierre-Olivier Lagage, Giovanna Tinetti
Figure caption: Same as Figure 37 but without accounting for the stellar metallicity. Again, there is no evidence for a mass–metallicity trend in these data as the constant-metallicity model provides a preferable fit to these data.