adsabs/AstroCLIMB huggingface dataset:
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: Similar to Figure 7, but in this case the probabilities assigned to “Other” class objects in the test set are omitted and the remaining probabilities are renormalized in the test data set. This illustrates more clearly the degeneracies between the known and the “Other” class objects. The large “subsuming” classification systematic visible in Figure 7 that arises since many classifiers provided a probability for “Other” class objects based on a weighted sum of probabilities from the known objects is no longer present (consider the SN II-“Other” class subsuming systematic in the Kyle Boone entry of Figure 7). We also show the results from the “validation classifier” run on the WFD data (without “Other” class objects).
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: Performance of the top three classifiers for the “Other” class. The predicted probability distributions for the 13087 true type “Other” class objects. The numbers in different colors reflect how many objects were incorrectly classified as a given type by each of the three classifiers, and the shaded regions show the predicted probability distributions for each class. Note how the “Other” class is most often confused with SN II and SN Ibc. This occurred as a result of many of the classifiers generating their “Other” class probability as a combination of probabilities from the other classes.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: The performance of the top three entrants to the PLAsTiCC across three different metrics. In each case a lower score indicates better performance of the classifier. The left panel shows the metrics for the full test data set including the unseen “Other” class objects. All classifiers were asked to return probabilities for the “Other” class objects. The right panel shows the scores on the test data set excluding the “Other” class objects. The scores have been normalized to the score of Kyle Boone, indicated in parenthesis on the x-axis label. This plot illustrates how close the first- and second-place finish was, and the importance of the unknown objects on the final classifier performance. In the case where the “Other” class objects were not included, the first- and second-place positions would have been reversed.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: Pseudoconfusion matrices for the top three entries to PLAsTiCC. The metric scores for these entries were 0.68503 (Kyle Boone), 0.69933 (Mike and Silogram), and 0.70016 (Major Tom, mamas, and nyanp). The top entries are described fully in Tables 3 and 4.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: The precision-recall plots for the top three classifiers and the “validation” classifier as a function of threshold (color) for three classes of interest: KN, TDE, and SLSN.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: The disagreement matrix for the top classifiers. Colors indicate the disagreement metric between each classifier (x-axis) and all classifiers ranked better than it (y-axis), where darker cells correspond to lower Kullback–Leibler Divergence from the worse-ranked classifiers’ probability densities of assigned posterior classification probabilities to those of the better-ranked classifier. The higher- and lower-ranked classifier pairs’ cells are labeled with the class that was most different between the two classifiers and the percentage of the total disagreement between the two classifiers for which that class is responsible. The methods used by each classifier are summarized in Table 3.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: Data augmentation as part of the KB method. The light curves have been interpolated using GP interpolation, shown as shaded regions with the largest spread where there is no data. The top panel shows the augmentation in light-curve space, where points are removed, the object is moved to a higher redshift and the errors on the light-curve points are increased. The bottom panel illustrates the augmentation in feature space for a subset of the PLAsTiCC models. The augmented sample not only has a larger spread in the maximum r-band flux, one of the features used by the avocado classifier but extends each class to a higher redshift and helps increase the sample.
Paper: Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Authors: R. Hložek, A. I. Malz, K. A. Ponder, M. Dai, G. Narayan, E. E. O. Ishida, T. Allam Jr, A. Bahmanyar, X. Bi, R. Biswas, K. Boone, S. Chen, N. Du, A. Erdem, L. Galbany, A. Garreta, S. W. Jha, D. O. Jones, R. Kessler, M. Lin, J. Liu, M. Lochner, A. A. Mahabal, K. S. Mandel, P. Margolis, J. R. Martínez-Galarza, J. D. McEwen, D. Muthukrishna, Y. Nakatsuka, T. Noumi, T. Oya, H. V. Peiris, C. M. Peters, J. F. Puget, C. N. Setzer, Siddhartha, S. Stefanov, T. Xie, L. Yan, K.-H. Yeh, W. Zuo
Figure caption: Performance of the “validation” classifier compared to the top three entries to PLAsTiCC. The naïve classifier does not correctly classify the rare KN objects, while a classifier more optimized to find rare objects through data augmentation is a very accurate classifier of these bright transients.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: The SMHM relation for SMUDGes sources. The red, filled symbols represent UDGs (r  e  ≥ 1.5 kpc), which correspond closely to a cut in M  h . The lighter colored points represent the SMUDGes that do not meet the UDG size criterion. The solid line represents the mean relation for a wide set of dwarf galaxies drawn from the literature (Zaritsky & Behroozi 2023). The dotted lines represent a decrease in the stellar mass by factors of 2 and 10. The large unfilled dark circles represent the results for a sample of UDGs with spectroscopically measured velocity dispersions and independent photometry. The shaded region highlights the range of M  h  where we compare the samples in the text. The lines in the inset represent how an individual galaxy will move with a 10% and 100% error in the distance. Small errors move points along the published relation but significantly overestimating the distance moves points in the direction populated by the largest UDGs. The UDGs are relatively inefficient integrated star-forming galaxies at a given M  h . At a quantitative level, the results await further validation of the methodology for estimating σ  v  and M  h .
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: The relationship between the number of globular clusters and total mass. The number of globular clusters comes from Saifollahi et al. (2022) and the masses come from our estimates. The dashed line is not a fit but rather the published relation from Burkert & Forbes (2020). The comparison indicates a modest offset and acceptable precision for our estimated masses (mean offset from the relation is 15%, and the scatter corresponds to an uncertainty in the mass of 25%). However, as detailed in the text, some exceptions to this behavior are known from other studies.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: Comparison of two environmental tracers. The velocity dispersion, σ  v , and number of galaxies refers to the parameters of the overdensity associated with an individual UDG candidate. We show the results for all lines of sight, regardless of whether we accepted the estimated redshift. The two quantities track each other as expected, although with large scatter. Because of the large scatter, we choose to define the low-density environment as satisfying both σ  v  ≤ 500 km s−1 and N < 15, and the high-density environment as satisfying both σ  v  > 500 km s−1 and N ≥ 15. The selected regions are shaded and labeled.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: Comparison of estimated velocity dispersions of dispersion supported galaxies of various masses to the spectroscopically measured values (Jorgensen et al. 1996; Geha et al. 2003; Chilingarian et al. 2008; Mieske et al. 2008; Collins et al. 2014). Twenty UDGs with velocity dispersion measurements from the literature (Beasley et al. 2016; van Dokkum et al. 2017; Toloba et al. 2018; Chilingarian et al. 2019; Martín-Navarro et al. 2019; van Dokkum et al. 2019b; Forbes et al. 2021; Gannon et al. 2022) are highlighted with larger, open red circles. The dispersion about the 1:1 line for UDGs corresponds to a velocity dispersion scatter of 11 km s−1.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: The SMHM relation for UDGs in poor and rich environments. The solid circles represent those UDGs in rich environments, and open circles represent those in poor ones, with environment defined as described in the text. The solid line represents the mean relation for the general population from Zaritsky & Behroozi (2023). The dotted lines represent a decrease in the stellar mass by factors of 2 and 10. The uncertainties due to distance errors are as shown in Figure 10.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: Color–magnitude distribution for SMUDGes candidates with estimated redshifts. In the left panel, we include all candidates with cz est and in the right panel only those that at the estimated distance have r  e  ≥ 1.5 kpc. The intensity scaling of each bin corresponds to a linear scaling of the number of objects in the bin, with different normalization in the two panels.
Paper: Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). V. The Complete SMUDGes Catalog and the Nature of Ultradiffuse Galaxies
Authors: Dennis Zaritsky, Richard Donnerstein, Arjun Dey, Ananthan Karunakaran, Jennifer Kadowaki, Donghyeon J. Khim, Kristine Spekkens, Huanian Zhang, 华年 张
Figure caption: Comparison of UDGs (red) and the general galaxy population (gray) with 10 < M  h /M ⊙ < 11.5. The first panel shows the stellar mass to total mass ratio within r  e , while the second shows the same ratio for the halo radius, r  h . Finally, the third panel shows the distribution of the ratio of these two quantities. Smaller ratios indicate a lower concentration of stars relative to dark matter.
Paper: General-relativistic Radiation Transport Scheme in Gmunu. I. Implementation of Two-moment-based Multifrequency Radiative Transfer and Code Tests
Authors: Patrick Chi-Kit Cheong, 志杰 張, Harry Ho-Yin Ng, Alan Tsz-Lok Lam, Tjonnie Guang Feng Li
Figure caption: Energy density E (upper panel) and the radial flux ratios F  r /E (lower panel) along x-axis at time t = 10 of the homogeneous radiation sphere test. Since the profiles are extracted along the x-axis, the x coordinate has the same value of radius r. The dots show the numerical results obtained by Gmunu while the solid lines show the corresponding analytic solution. For the high opacity cases (i.e., ﹩{\bar{\kappa }}_{a}\geqslant 10﹩, the red and green dots), the numerical results are mostly indistinguishable from the reference solution except the region nearby the discontinuity at the surface of the sphere. However, for the low-opacity case (﹩{\bar{\kappa }}_{a}=1﹩, the blue dots), the numerical results of the energy density E are less accurate inside the sphere. This is because the analytic closure does not give the correct second moment in the intermediate opacities regime (i.e., κ ≲ 1).
Paper: General-relativistic Radiation Transport Scheme in Gmunu. I. Implementation of Two-moment-based Multifrequency Radiative Transfer and Code Tests
Authors: Patrick Chi-Kit Cheong, 志杰 張, Harry Ho-Yin Ng, Alan Tsz-Lok Lam, Tjonnie Guang Feng Li
Figure caption: The energy density profile of the radiation E at t = 4 in a purely scattering medium, with high scattering opacity ﹩{\bar{\kappa }}_{s}={10}^{3}﹩, which moves with a relativistic velocity v  x  = 0.5. The red dots show the numerical solution obtained by Gmunu while the black solid line shows the corresponding semianalytic solution. Note that this is known to be a demanding test, in the sense that the result is highly sensitive to the treatment of the optically thick limit (Radice et al. 2022). This figure shows that the results returned by Gmunu agree with the semianalytic solution.
Paper: General-relativistic Radiation Transport Scheme in Gmunu. I. Implementation of Two-moment-based Multifrequency Radiative Transfer and Code Tests
Authors: Patrick Chi-Kit Cheong, 志杰 張, Harry Ho-Yin Ng, Alan Tsz-Lok Lam, Tjonnie Guang Feng Li
Figure caption: The L 1-norm of the difference between the exact and final (t = 10) values of the energy density of the radiation E at different resolution N. The second-order ideal scaling is given by the black dashed line. In this test, second-order, accurate strong-stability-preserving IMEX-SSP2(2,2,2) time integrator from Pareschi & Russo (2005), the Harten, Lax, and van Leer Riemann solver from Harten et al. (1983), and a second-order solver from Minmod limiter Roe (1986) are used. The code achieves roughly 1.86 order of convergence in this test.
Paper: General-relativistic Radiation Transport Scheme in Gmunu. I. Implementation of Two-moment-based Multifrequency Radiative Transfer and Code Tests
Authors: Patrick Chi-Kit Cheong, 志杰 張, Harry Ho-Yin Ng, Alan Tsz-Lok Lam, Tjonnie Guang Feng Li
Figure caption: The energy density profile of the radiation E at t = 10 in a purely scattering medium, with high scattering opacity ﹩{\bar{\kappa }}_{s}={10}^{3}﹩. The red dots show the numerical solution obtained by Gmunu while the black solid line shows the reference analytic solution from Equation (98) in the diffusive limit. This indicates that Gmunu captures correct diffusion rate even in diffusive limit.
Paper: General-relativistic Radiation Transport Scheme in Gmunu. I. Implementation of Two-moment-based Multifrequency Radiative Transfer and Code Tests
Authors: Patrick Chi-Kit Cheong, 志杰 張, Harry Ho-Yin Ng, Alan Tsz-Lok Lam, Tjonnie Guang Feng Li
Figure caption: The energy density profile of the radiation E at t = 1, where the radiation has propagated through the velocity jump at x = 0. Despite the discontinuity of the velocity profile, the numerical solution obtained by Gmunu does not have artificial oscillations at the velocity jump interface x = 0, or at the radiation front x = 0.5.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: SB profile measurement for MCG+07-34-043 at redshift z = 0.029. Top row: deep WWFI data; bottom row: shallower SDSS data. Left panel: observed image of the galaxy. The full image size is 3 × 3 times larger than shown. The images are smoothed using a 2D Gaussian kernel with 3 pixel standard deviation. This corresponds to 0.″6 for the WWFI images and 1.″2 for the SDSS images. North is up, east is left. Second panel: automatically masked image combined with a manually refined mask only for the WWFI data. Third panel: residual after subtracting an isophote model. Residuals at ﹩\sim 0\buildrel{\,\prime}\over{.} 5﹩ from the center toward the bottom left and top right arise from real isophotal distortions due to interactions with neighboring galaxies. Right panel: measured SB profile (black points) and best-fit Sérsic function (red continuous line). The 1σ uncertainty of the fit is given by the line width. Gray dashed lines enclose the fitting range. Best-fit Sérsic parameters are given in the text labels. The red dashed line is the MaNGA single Sérsic fit (Domínguez Sánchez et al. 2022): n = 6.49 ± 0.05, r e = 23.″15 ± 0.″44, SBe = 24.34 ± 0.04 g mag arcsec−2.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Comparison between photometric and kinematic parameters of 1420 normal Es. From left to right: effective radius r e, total brightness M tot, average effective surface brightness 〈SBe〉, and central (uncorrected; see Section 5.2) stellar velocity dispersion σ 0. Photometric parameters based on SDSS De Vaucouleurs models are plotted along the x-axes. Our nonparametric measurements on the same data are plotted along the y-axes. We calculate them by directly integrating the light profiles. Sérsic functions are only used for extrapolation. Best-fit Sérsic indices n are color-coded. Galaxies with n > 8 are discarded (see Section 3.1). The velocity dispersions in the fourth panel are published SDSS values (see Appendix B.1) along the x-axis, and our fits to the same spectra using pPXF are plotted along the y-axis.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: FJ relation for BCGs (red) and normal Es (black). The black continuous line is the linear relation with a broken slope. The black dashed line is the extrapolation of the fit to lower-luminosity ellipticals. Shades indicate the ±1σ confidence intervals. The fits are performed by minimizing the weighted, squared orthogonal residuals to the line. For the normal E sample, low-velocity-dispersion galaxies are discarded using an approximately orthogonal cut, and the remaining data points are weighted by the inverse of the luminosity function.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: Top row: edge-on projections of the FP for normal Es (black). Bottom row: edge-on projections of the FP for BCGs (red). The y-axes are scaled such that orthogonal distances remain orthogonal. Shaded regions give the ±1σ confidence intervals around the projected planes (black and red lines). Best-fit slopes are given as text labels for both planes, which are defined by ﹩\mathrm{log}({r}_{{\rm{e}}})=\alpha \mathrm{log}({\sigma }_{{\rm{e}}8})+\beta \mathrm{log}\langle {I}_{{\rm{e}}}\rangle +\gamma ﹩. Arrows point in the directions where 1:1 and 1:5 mass ratio mergers would shift the data points (Hilz et al. 2013). The 1:5 merger arrow is almost parallel to the plane.
Paper: Minor Mergers Are Not Enough: The Importance of Major Mergers during Brightest Cluster Galaxy Assembly
Authors: Matthias Kluge, Ralf Bender
Figure caption: FP projections in κ space. Arrows point in the direction where the data points would move as the corresponding parameter increases. Different formation processes shift the data points in different directions in the face-on view (bottom-right panel), as indicated by the arrows (Bender et al. 1992). We have updated the merger prediction using simulations from Hilz et al. (2013). The arrow marked as “PSF debroad.” has almost no length and shows the correction for PSF de-broadening according to Kluge et al. (2020).