adsabs/AstroCLIMB huggingface dataset:
Paper: Analyses of ∼0.05–2 MeV Ions Associated with the 2022 February 16 Energetic Storm Particle Event Observed by Parker Solar Probe
Authors: Joe Giacalone, C. M. S. Cohen, D. J. McComas, X. Chen, M. A. Dayeh, W. H. Matthaeus, K. G. Klein, S. D. Bale, E. R. Christian, M. I. Desai, M. E. Hill, L. Y. Khoo, D. Lario, R. A. Leske, R. L. McNutt, D. G. Mitchell, J. G. Mitchell, O. Malandraki, N. A. Schwadron
Figure caption: Differential intensity over selected intervals upstream of the shock: far from (left) and near the shock (right). The energies are 75 keV (cyan), 133 keV (red), 237 keV (green), 421 keV (blue), and 750 keV (violet). The black lines in all panels represent the least-squares fit to the data, with the exponential rise time and correlation coefficient shown in Table 1.
Paper: Analyses of ∼0.05–2 MeV Ions Associated with the 2022 February 16 Energetic Storm Particle Event Observed by Parker Solar Probe
Authors: Joe Giacalone, C. M. S. Cohen, D. J. McComas, X. Chen, M. A. Dayeh, W. H. Matthaeus, K. G. Klein, S. D. Bale, E. R. Christian, M. I. Desai, M. E. Hill, L. Y. Khoo, D. Lario, R. A. Leske, R. L. McNutt, D. G. Mitchell, J. G. Mitchell, O. Malandraki, N. A. Schwadron
Figure caption: Magnetic field strength (black), plasma number density (red), and radial component of the solar wind speed (red) as a function of UT, DOY 47, 2022 from the ENLIL model of the 2022 February 16 CME at PSP.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: Calculated HRs for data simulated using the borus02 model for a range of N H values. HR2 continues to increase beyond N H ∼ 3 × 1023 cm−2 whereas HR1 loses sensitivity and eventually decreases. Neither is sensitive to changes in N H beyond N H ∼ 3 × 1024 cm−2 given the selected average torus properties.
Paper: Variability as a Predictor for the Hard-to-soft State Transition in GX 339−4
Authors: Matteo Lucchini, Marina Ten Have, Jingyi Wang, Jeroen Homan, Erin Kara, Oluwashina Adegoke, Riley Connors, Thomas Dauser, Javier Garcia, Guglielmo Mastroserio, Adam Ingram, Michiel van der Klis, Ole König, Collin Lewin, Labani Mallick, Edward Nathan, Patrick O'Neill, Christos Panagiotou, Joanna Piotrowska, Phil Uttley
Figure caption: Evolution of the other outbursts analyzed in this work, which have more limited coverage than GX 339−4. Top left: the 2005 outburst of GRO 1655−40. Top right: the 2009 outburst of XTE J1752−223. Remaining panels: the 2008, 2010, and 2011 outbursts of H1743-322. The color coding is identical to Figure 2.
Paper: Variability as a Predictor for the Hard-to-soft State Transition in GX 339−4
Authors: Matteo Lucchini, Marina Ten Have, Jingyi Wang, Jeroen Homan, Erin Kara, Oluwashina Adegoke, Riley Connors, Thomas Dauser, Javier Garcia, Guglielmo Mastroserio, Adam Ingram, Michiel van der Klis, Ole König, Collin Lewin, Labani Mallick, Edward Nathan, Patrick O'Neill, Christos Panagiotou, Joanna Piotrowska, Phil Uttley
Figure caption: Evolution of all the outbursts XTE J1550−564; similarly to the previous panel, the coverage of the rise or state transition. The panels from left to right and top to bottom correspond to the 1999, 2000 full outbursts and 2001, 2002, and 2003 HS outbursts. The color coding is identical to Figure 2.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: Spectra simulated for Chandra and XMM-Newton using the same method as in Figure 1. This is equivalent to the shapes of the responses for the ACIS-S and EPIC pn cameras, respectively. The red lines show the data on an absolute scale and the black lines show a normalized scale. The Chandra data have a much lower response in the hard band (4–10 keV) than pn, which need to be corrected.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: ROC curves for all three models. The threshold χ 2 used in calculating the TPR and FPR increases from right to left along the lines. The stars represent the location of two of the threshold ﹩{\chi }_{{\rm{c}}}^{2}﹩ values shown in the tables and confusion matrices. The gray dotted line represents a theoretical classifier with no predictive value. A larger distance from this line represents a more reliable classifier.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: Uncorrected HRs for 20 spectra simulated with borus02 (Γ = 1.9; ﹩\mathrm{log}({N}_{{\rm{H}},\mathrm{avg}})=24﹩) for different levels of obscuration. The shaded error bars represent individual spectra while the solid error bars represent the mean values for the 20 spectra at a given N H. The colors red, blue, orange, green, purple, and yellow represent N H = (0.9, 10, 30, 50, 70, 80) × 1022 cm–2, respectively. The black line connects Chandra simulated data and the red line connects XMM-Newton simulated data.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: Simulated data of an on-axis source emitting a flat power law (Γ = 0, norm = 1) as seen with the ACIS-S camera for cycles 1, 10, 17, and 23. An off-axis source (2.′786 from the center) is observed in cycle 9 as well.
Paper: A Simple Method for Predicting N H Variability in Active Galactic Nuclei
Authors: Isaiah S. Cox, Núria Torres-Albà, Stefano Marchesi, Xiurui Zhao, Marco Ajello, Andrealuna Pizzetti, Ross Silver
Figure caption: Same as Figure 4 but with the correction factors in Equation (7) applied.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Lomb–Scargle periodograms computed for the sectors separately. Gray shadow: total computed periodograms. The peaks at the highest frequencies are aliases due to the time resolution of the TESS observations (30 minutes in Cycle 1 and 10 minutes in Cycle 3). Black line: binned Lomb–Scargle periodograms. Here, the error bars represent the scatter within each bin. Colored lines (same color coding as Figure 7): normalized periodogram for the underlying DRW model. These models are more reliable than the Lomb–Scargle estimates (see Section 5.2 for more details).
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Left: periodograms from the DRW fit of the different sectors. Right: corresponding τ DRW. Depending on the sector, τ DRW ranges between 0.8 and 5.5 days.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Stitched light curves of Pictor A in Cycle 1 (Sectors 4–6; top panel) and Cycle 3 (Sectors 31 and 33; bottom panel). The time axis has been shifted to the galaxy’s rest frame. The colors correspond to the different extraction methods: simple PCA (orange), simple hybrid (blue), and fully hybrid (purple). In addition to the normalization applied for the stitching (see the text for details), the simple PCA and fully hybrid light curves are artificially normalized by subtracting a factor of ∼285 to match the simple hybrid light curve. We notice that while the main features (e.g., flares) are similar in each light curve, the simple hybrid one shows more variability at timescales comparable to the sector’s length. For the following analysis, we will use the light curve extracted with the simple hybrid method.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Plots of the scattered background light over time for Sector 3, Camera 2 (left) and Sector 5, Camera 3 (right), from the TESS Data Release Notes (https://archive.stsci.edu/tess/tess_drn.html). Differences in plot formatting are due to differences as presented in the Sector 3 and 5 release note documents.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Flux distribution histograms in the individual sectors with Gaussian (solid line), bimodal Gaussian (dashed–dotted line), and log-normal (dashed line) fits.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: TESS Sector 3 light curve of the non-AGN galaxy 2dFGRS TGS145Z214, restricted to a one-component PCA analysis with insufficient parameters for background and contaminant correction.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: TESS Sector 5 light curve of Pictor A, using the fully hybrid method and restricted to only one additive PCA component.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: Map of the variable star AP Sculptor and the nearby galaxy 2dFGRS TGS145Z214, each shown in a red box. The sources are separated by 4.′79.
Paper: The Rapid Optical Variability of the Nearby Radio-loud AGN Pictor A: Introducing the Quaver Pipeline for AGN Science with TESS
Authors: Krista Lynne Smith, Lia F. Sartori
Figure caption: TESS Sector 3 light curve of the variable star AP Sculptor, extracted with Quaver.
Paper: Understanding the Kinetic Energy Deposition within Molecular Clouds
Authors: Lixia Yuan, Ji Yang, Fujun Du, Yang Su, Shaobo Zhang, Qing-Zeng Yan, Yan Sun, Xin Zhou, Xuepeng Chen, Hongchi Wang, Zhiwei Chen
Figure caption: Left panel: Distributions of velocity-integrated intensities of 12CO (﹩{I}_{{}^{12}\mathrm{CO}}﹩, color map) and 13CO (﹩{I}_{{}^{13}\mathrm{CO}}﹩, green contours) line emission for an MC named G114.581-0.421-49.35 in our catalog. The green contours range from 10% to 90% stepped by 20% of the maximum value (31.1 K km s−1). Middle panel: Distributions of the pixel number in the intervals of values (﹩{I}_{{}^{12}\mathrm{CO}}﹩, ﹩{I}_{{}^{13}\mathrm{CO}}﹩, ﹩{\tau }_{{}^{13}\mathrm{CO}}﹩). Right panel: The averaged spectra for the extracted 12CO and 13CO lines emission within this MC. The vertical black-dashed line delineates the centroid velocity of 12CO line emission (﹩{V}_{\mathrm{cen}{,}^{12}\mathrm{CO}}﹩, km s−1) calculated as in Equation (1). The length of the horizontal black-dashed line shows the value of ﹩\sqrt{8\mathrm{ln}2}\times {\sigma }_{{}^{12}\mathrm{CO},\mathrm{tot}}﹩, which corresponds to the FWHM velocity width of the 12CO spectral line, and ﹩{\sigma }_{{}^{12}\mathrm{CO},\mathrm{tot}}﹩ is calculated as in Equation (5). The vertical green-dashed line delineates the centroid velocity of 13CO line emission (﹩{V}_{\mathrm{cen}{,}^{13}\mathrm{CO}}﹩, km s−1) calculated as in Equation (2). The length of the horizontal green-dashed line shows the value of ﹩\sqrt{8\mathrm{ln}2}\times {\sigma }_{{}^{13}\mathrm{CO},\mathrm{tot}}﹩, which corresponds to the FWHM velocity width of the 13CO spectral line, and the ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{tot}}﹩ is calculated as in Equation (5).
Paper: Understanding the Kinetic Energy Deposition within Molecular Clouds
Authors: Lixia Yuan, Ji Yang, Fujun Du, Yang Su, Shaobo Zhang, Qing-Zeng Yan, Yan Sun, Xin Zhou, Xuepeng Chen, Hongchi Wang, Zhiwei Chen
Figure caption: Correlations between the ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{tot}}^{2}﹩, ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{re}}^{2}﹩, and ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{in}}^{2}﹩ with ﹩{\sigma }_{{}^{12}\mathrm{CO},\mathrm{tot}}^{2}﹩, for the samples in the “Multiple” regime. The V sys used in Equation (5) is the ﹩{V}_{\mathrm{cen},{}^{13}\mathrm{CO}}﹩ of each MC. Each dot in the panels represents a 12CO MC. The colors on these dots represent the distribution of the probability density function (2D-PDF) of their 12CO MCs. The corresponding Spearman’s rank correlation coefficients (R-value) are noted in each panel.
Paper: A Dragon Out of Breath? Monitoring High-velocity Outflows from the High-mass Gamma-Ray Binary LS 2883/PSR B1259–63 During the 2017–2021 Binary Cycle
Authors: Jeremy Hare, George G. Pavlov, Gordon P. Garmire, Oleg Kargaltsev
Figure caption: Location of the pulsar in its orbit around the massive companion during each Chandra observation for this cycle. The gray/black shaded regions show where in the orbit the pulsar passes through the massive star's decretion disk (Chernyakova et al. 2006).
Paper: A Dragon Out of Breath? Monitoring High-velocity Outflows from the High-mass Gamma-Ray Binary LS 2883/PSR B1259–63 During the 2017–2021 Binary Cycle
Authors: Jeremy Hare, George G. Pavlov, Gordon P. Garmire, Oleg Kargaltsev
Figure caption: ACIS 0.5–8 keV image of ObsID 21247 with native pixel binning. Faint extended emission can be seen to the south of the binary.
Paper: Understanding the Kinetic Energy Deposition within Molecular Clouds
Authors: Lixia Yuan, Ji Yang, Fujun Du, Yang Su, Shaobo Zhang, Qing-Zeng Yan, Yan Sun, Xin Zhou, Xuepeng Chen, Hongchi Wang, Zhiwei Chen
Figure caption: Relations between the velocity dispersion from different components (﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{tot}}^{2}﹩, ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{re}}^{2}﹩, and ﹩{\sigma }_{{}^{13}\mathrm{CO},\mathrm{in}}^{2}﹩) with the ﹩{\sigma }_{{}^{12}\mathrm{CO},\mathrm{int}}^{2}﹩, whose values are revised the effect of optical depths as ﹩{\sigma }_{{}^{12}\mathrm{CO},\mathrm{int}}={\sigma }_{{}^{12}\mathrm{CO},\mathrm{tot}}/{\bar{\beta }}_{\tau }﹩. Their Spearman’s rank correlation coefficients (R-value) are noted in each panel.
Paper: A Dragon Out of Breath? Monitoring High-velocity Outflows from the High-mass Gamma-Ray Binary LS 2883/PSR B1259–63 During the 2017–2021 Binary Cycle
Authors: Jeremy Hare, George G. Pavlov, Gordon P. Garmire, Oleg Kargaltsev
Figure caption: Left: ACIS 0.5–8 keV image of ObsID 21245 with native pixel binning. Right: deconvolved image of the same observation. The green region shows the image distortion caused by the Chandra mirror asymmetry. Extended emission can be clearly seen to the south of the binary.
Paper: Understanding the Kinetic Energy Deposition within Molecular Clouds
Authors: Lixia Yuan, Ji Yang, Fujun Du, Yang Su, Shaobo Zhang, Qing-Zeng Yan, Yan Sun, Xin Zhou, Xuepeng Chen, Hongchi Wang, Zhiwei Chen
Figure caption: Same as Figure C1, but for another MC named G123.291-0.539-44.15 in our catalog. The green contours in the left panel range from 10% to 90% stepped by 20% of the maximum value (11.9 K km s−1).
Paper: A Dragon Out of Breath? Monitoring High-velocity Outflows from the High-mass Gamma-Ray Binary LS 2883/PSR B1259–63 During the 2017–2021 Binary Cycle
Authors: Jeremy Hare, George G. Pavlov, Gordon P. Garmire, Oleg Kargaltsev
Figure caption: Chandra ACIS 0.5–8 keV images from the 2017 to 2021 observing campaign with one half native pixel (0.″246) binning. The Δt shows how many days after periastron (2017 September 22) the observation was taken.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: Fraction of ICL as a function of halo mass for halos with different concentration c 200 (circles with different colors) derived by assuming an NFW profile (see the text for more details). The region between the two dotted lines shows the ±1σ distribution, while the solid black line is the mean of the data. The solid red and blue lines represent the average ICL fractions in the lowest and highest ranges in concentration, respectively. Contrary to what was found in C14 where the sample of halos was much smaller, here we do find a positive (although weak) trend between the fraction of ICL and its host halo mass, which is clear from the distribution up to halos with ﹩\mathrm{log}{M}_{\mathrm{halo}}\sim 14.0﹩, and then it stays constant at ∼0.4. Another important result coming from the plot is the tendency of the ICL fraction to be gradually higher for more concentrated halos (colors from blue to red, from the bottom to the top of the figure). Moreover, the most-massive halos are the least concentrated ones, as they are supposed to be, given the well-known relation between halo mass and concentration (e.g., Prada et al. 2012). The net message of the plot is that the ICL fraction weakly increases toward higher halo masses, and, at a given halo mass, it is higher in more concentrated ones.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: Distribution of the fraction of the ICL in our sample of halos (solid black line) and the corresponding Gaussian fit (IDL GAUSSFIT with n terms = 5, dashed red line). Here (and below unless otherwise specified) we sample only halos with ﹩\mathrm{log}{M}_{\mathrm{halo}}[{M}_{\odot }/h]\geqslant 13.0﹩ containing at least five galaxies (including the central one) with ﹩\mathrm{log}{M}_{* }\geqslant 8.5﹩. The total number of halos in our sample is 2509 from ﹩\mathrm{log}{M}_{\odot }[{M}_{\odot }/h]\geqslant 13.0﹩ up to more than ﹩\mathrm{log}{M}_{\mathrm{halo}}[{M}_{\odot }/h]\geqslant 15.0﹩, i.e., spanning a wide range of halo masses. The distribution peaks on f ICL ∼ 0.35, and the error is modest, σ ∼ 0.12, which, put together, give rise to a medium wide distribution of ICL fraction as predicted by several observations and numerical methods (see, e.g., the reviews by Contini 2021; Montes 2022). The fact that the distribution of the ICL fraction in galaxy groups and clusters can be approximated with a Gaussian also highlights the stochastic nature of the processes that bring to the formation of the ICL, such as stellar stripping and galaxy mergers.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: Fraction of ICL as a function of halo concentration c 200 (circles with different colors roughly indicate the number of galaxies within the virial radius). The range in concentration has been split in 10 bins all containing the same number of halos, and their ICL fraction averaged (purple diamonds), while the solid black line represents their linear fit (f ICL = (0.044 ± 0.008)c 200 − 0.110 ± 0.047). As already pointed out in C14, the halo concentration can explain large part of the scatter in the relation between the ICL fraction and the mass of the host halo. Here, with a much larger number of halos, we show that f ICL does depend on the concentration of the halos (as anticipated in Figure 2), although the dependence is seen more clearly in the fit rather than the data, which instead show a larger scatter. More concentrated halos can be translated in dynamically older objects, for which the process of formation of the ICL is in an advanced stage, given the fact that in a more concentrated halo the probability of stellar stripping is higher.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: In each halo mass bin, fraction of halos having concentration and fraction of ICL above (red) and below (green) their averages in the bin at the same time. The blue color refers to the sum of red and green, and the horizontal blue line marks the medium efficacy 0.5. As argued in the text, this is a way to quantify whether or not concentration and fraction of ICL go in the same direction if the former is driving the bulk of ICL formation through stellar stripping, i.e., higher fractions of ICL in halos more concentrated. At least 60% of halos in every halo mass bin, except the most-massive ones where we have only six objects, have either higher ICL fractions and concentrations above their mean, or below, and the other 40% with the two quantities going in opposite directions. The percentages increase in lower-mass halos, by reaching ∼80% in the lowest halo mass bin, which is also the one containing the largest statistics.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: The luminosity in the g band of the ICL as a function of halo mass (circles with different colors according to the number of galaxies within the virial radius, and their fit (﹩\mathrm{log}{L}_{{ICL}}=(0.82\pm 0.02)\mathrm{log}{M}_{{halo}}-0.31\pm 0.27﹩) represented by the solid black line), compared with the observed ones by Ragusa et al. (2023; purple diamonds) and by Kluge et al. (2021; cyan triangles), and their fit including all of them (solid purple line). For a full coverage of the halo mass range spanned by the observed data, in this figure we take advantage of the high-resolution runs (see Table 1), then increasing the sample of haloes from ∼2500 to more than 7700. Our model predictions compare fairly well with the observed data in a large part of the range explored, considering that most of the observed data points lie in the cloud of the predictions of our model, although it must be pointed out that the slopes of the two global relations (fits) are remarkably different. We refer the reader to the text for a full discussion of this point.
Paper: The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration
Authors: Emanuele Contini, Seyoung Jeon, Jinsu Rhee, San Han, Sukyoung K. Yi
Figure caption: Fraction of ICL as a function of the fraction of early-type galaxies (ETGs) within the virial radius R 200, for the same sample of halos considered in the former figures. ETGs are defined as those galaxies for which the bulge-to-total ratio (B/T) is > 0.7 (see the text for more details). As in Figure 2, colors from blue to red indicate an increasing concentration, while purple diamonds represent the mean ICL fractions in bins of ETGs fractions. The solid red and blue lines represent the average ICL fractions in the lowest and highest ranges in concentration, respectively. We do not find any correlation unless we increase the threshold in stellar mass by an order of magnitude, from ﹩\mathrm{log}{M}_{* }=8.5﹩ to ﹩\mathrm{log}{M}_{* }=9.5﹩ (not shown). This result somehow echoes that found by Ragusa et al. (2023), who showed that the observed fraction of ICL in the VEGAS survey is slightly higher in groups/clusters having a larger fraction of ETGs. However, a direct comparison with their data is not possible due to the different definitions of ETGs used (see the text for a full discussion about this point).