scatter in this regime. BAHAMAS/MACSIS is offset by ≈0.2 dex towards lower $M_{200c,*}$ at all halo masses. These haloes are hosts to galaxies of lower stellar masses or are less compact (i.e. 30 kpc aperture cuts out a significant part of stellar mass) relative to the other two simulations. This is confirmed in panel (d), which includes all star particles associated with the FOF group within $R_{200c}$. Here, the offset is much smaller (below $1\sigma$ scatter) which suggests that BAHAMAS/MACSIS galaxies are substantially larger for the same host halo mass and more of the mass resides in the wings (intracluster light), instead. This is consistent with our findings for all central galaxies in the simulation.

## APPENDIX C: A CLOSER LOOK AT THE STELLAR MASS FUNCTION PREDICTIONS OF THE DIFFERENT SIMULATIONS

Considering the total stellar mass functions shown in the left-hand panel of Fig. 4, there is a small excess abundance of about a factor of two at $log_{10}(M_*/M_\odot) < 10.5$ in the BAHAMAS simulation, relative to the data. The same feature can be seen in fig. 13 of McCarthy et al. (2017), which they argue is due to finite mass resolution, which limits the ability of low-mass galaxies to regulate their star formation rates (see the appendix of that study).

While BAHAMAS shows reasonable agreement for the cluster GSMF (right-hand panel of Fig. 4) at the lowest and highest masses, there is also a significant deficit near the knee of the mass function ($10.5 < log_{10}(M_*/M_\odot) < 11.2$). One possible explanation for this feature is that tidal disruption of low-mass satellites may be overly efficient due to finite force resolution. Alternatively (or perhaps additionally), it is known that density only-based substructure finders, such as SUBFIND, can struggle to recover the full gravitationally bound stellar mass of substructures (Bahé 2021) and it is likely that this is more of an issue for comparatively lower resolution simulations. Indeed, excess tidal disruption and/or substructure finder issues are consistent with Fig. B1 in Appendix B. From that figure we conclude that the total stellar mass content summed over particles is approximately the same for the different simulations. When limited to summing the mass of galaxies within 30 kpc apertures, however, BAHAMAS/MACSIS has lower mass compared to Hydrangea and TNG300 (see bottom left-hand panel of Fig. B1). Some of this difference is because Hydrangea and TNG300 have higher BCG masses within 30 kpc, but most of the effect is likely either because satellites are too efficiently stripped in BAHAMAS (their stellar masses are reduced within 30 kpc) or they are destroyed altogether (or not detected by the substructure finder).

Moving on to EAGLE/Hydrangea, the field AGNdT9 simulation box is too small to contain a representative number of galaxies above $log_{10}(M_*/M_\odot) = 10.6$, which explains the premature decline of the field GSMF. At low masses ($M_* < 10^{10.6} M_\odot$), the amplitude is a reasonable match to the data but the slope is steeper, overpredicting the abundance of the lowest mass galaxies by a few tens of per cent. For the cluster comparison we can use the Hydrangea zoom simulations, using the same physics. We see similar behaviour to that in BAHAMAS: a reasonable match at the lowest masses, $log_{10}(M_*/M_\odot) \approx 10$, but a lower abundance of galaxies compared with GOGREEN near the knee of the GSMF. The discrepancy with GOGREEN at the knee of the GSMF is plausibly explained as a result of a slight mismatch in the halo mass selection for Hydrangea and GOGREEN, rather than the resolution-related issues discussed above for BAHAMAS/MACSIS. Indeed, Ahad et al. (2021) found reasonably good agreement between Hydrangea and GOGREEN where they used a simple parametric scaling to account for the

halo mass difference between Hydrangea and GOGREEN. Using the BAHAMAS/MACSIS suite we have derived a factor of ≈1.4 to scale the GSMF from the Hydrangea sample to that of a sample with the GOGREEN mean halo mass. We show that the scaled Hydrangea GSMF, shown in Fig. 4 with the dotted cyan curve, is in much better agreement with GOGREEN near the knee (i.e. consistent with Ahad et al. 2021). Scaling the curves up by this factor does, however, exacerbate the differences with respect to GOGREEN at the very lowest and highest masses. In other words, while Hydrangea reproduces the amplitude of the cluster GSMF relatively well (once the difference in halo mass is accounted for), the shape of the predicted GSMF differs in detail from that observed in GOGREEN clusters.

Finally, turning to the GSMF predictions of TNG300, we see a reasonably good match to the field, for all but the highest stellar masses. There is an excess of galaxies above $log_{10}(M_*/M_\odot) \approx 11.5$ when compared to GOGREEN data, which is also apparent in Fig. 3. This gives the field GSMF a flattened appearance. These very massive galaxies are highly likely to be BCGs at the centres of clusters which may indicate overcooling of BCGs in the model.<fnref n="3" /> A similar excess of massive galaxies is also seen in the cluster GSMF (right-hand panel). TNG300 also shows a similar deficit of galaxies near the knee with an excess at the highest masses, giving the GSMF a flat appearance. However, as for the case of Hydrangea, most of the discrepancy at the knee is due to a slight mismatch in the mean halo mass of the TNG300 haloes and the GOGREEN systems. Again, using BAHAMAS/MACSIS we derive a scaling factor to scale the TNG300 GSMF (dotted curve). Similar to Hydrangea, the issue at the highest masses is worsened somewhat by this rescaling. There is also a notable difference at $log_{10}(M_*/M_\odot) \lesssim 9.5$ between TNG300 and EAGLE/Hydrangea but, without observational data, it is unclear which one is more realistic.

In Fig. 5 we showed the GSMFs in the data and simulations split according to star-forming versus quiescent status. The observed behaviour is broadly replicated by BAHAMAS in the field; at least in that quiescent galaxies are more abundant at the high-mass end (although not significantly so) and the two curves cross at $log_{10}(M_*/M_\odot) \approx 10.75$. It overpredicts the abundance of star-forming galaxies by a factor of a few in the interval $11.0 < log_{10}(M_*/M_\odot) < 11.5$, while also overpredicting the abundance of quiescent galaxies at the low-mass end. Neither of the features can be accounted for by quantitatively altering the definition of quiescence (Section 3.3), as indicated by the shaded regions. For the cluster sample, the BAHAMAS/MACSIS mass function of star-forming galaxies is much flatter than in the field. This is in contrast with observations which show that the shape of the star-forming GSMF does not differ much between the field and cluster environments. The quiescent population is a better match but it shows the same features as what was seen for the total GSMF (largely because quiescent galaxies dominate the total population in this sample). Again these differences cannot be accounted for by star-forming/quenched designation (shaded regions) nor by uncertainties associated with choosing ten GOGREEN-like haloes (error bars).

EAGLE reproduces the observed field trends reasonably well below stellar masses of $log_{10}(M_*/M_\odot) = 10.75$. In the cluster population, Hydrangea performs comparatively well at matching the GOGREEN measurements. To within the uncertainty induced

<footnote n="3">³Using the scaled stellar masses from rTNG results in an improved agreement with the observations for the GSMF but does not significantly alter the main quenching results presented below.</footnote>
scatter in this regime. BAHAMAS/MACSIS is offset by ≈0.2 dex towards lower $M_{200c,*}$ at all halo masses. These haloes are hosts to galaxies of lower stellar masses or are less compact (i.e. 30 kpc aperture cuts out a significant part of stellar mass) relative to the other two simulations. This is confirmed in panel (d), which includes all star particles associated with the FOF group within $R_{200c}$. Here, the offset is much smaller (below $1\sigma$ scatter) which suggests that BAHAMAS/MACSIS galaxies are substantially larger for the same host halo mass and more of the mass resides in the wings (intracluster light), instead. This is consistent with our findings for all central galaxies in the simulation.

## APPENDIX C: A CLOSER LOOK AT THE STELLAR MASS FUNCTION PREDICTIONS OF THE DIFFERENT SIMULATIONS

Considering the total stellar mass functions shown in the left-hand panel of Fig. 4, there is a small excess abundance of about a factor of two at $log_{10}(M_*/M_\odot) < 10.5$ in the BAHAMAS simulation, relative to the data. The same feature can be seen in fig. 13 of McCarthy et al. (2017), which they argue is due to finite mass resolution, which limits the ability of low-mass galaxies to regulate their star formation rates (see the appendix of that study).

While BAHAMAS shows reasonable agreement for the cluster GSMF (right-hand panel of Fig. 4) at the lowest and highest masses, there is also a significant deficit near the knee of the mass function ($10.5 < log_{10}(M_*/M_\odot) < 11.2$). One possible explanation for this feature is that tidal disruption of low-mass satellites may be overly efficient due to finite force resolution. Alternatively (or perhaps additionally), it is known that density only-based substructure finders, such as SUBFIND, can struggle to recover the full gravitationally bound stellar mass of substructures (Bahé 2021) and it is likely that this is more of an issue for comparatively lower resolution simulations. Indeed, excess tidal disruption and/or substructure finder issues are consistent with Fig. B1 in Appendix B. From that figure we conclude that the total stellar mass content summed over particles is approximately the same for the different simulations. When limited to summing the mass of galaxies within 30 kpc apertures, however, BAHAMAS/MACSIS has lower mass compared to Hydrangea and TNG300 (see bottom left-hand panel of Fig. B1). Some of this difference is because Hydrangea and TNG300 have higher BCG masses within 30 kpc, but most of the effect is likely either because satellites are too efficiently stripped in BAHAMAS (their stellar masses are reduced within 30 kpc) or they are destroyed altogether (or not detected by the substructure finder).

Moving on to EAGLE/Hydrangea, the field AGNdT9 simulation box is too small to contain a representative number of galaxies above $log_{10}(M_*/M_\odot) = 10.6$, which explains the premature decline of the field GSMF. At low masses ($M_* < 10^{10.6} M_\odot$), the amplitude is a reasonable match to the data but the slope is steeper, overpredicting the abundance of the lowest mass galaxies by a few tens of per cent. For the cluster comparison we can use the Hydrangea zoom simulations, using the same physics. We see similar behaviour to that in BAHAMAS: a reasonable match at the lowest masses, $log_{10}(M_*/M_\odot) \approx 10$, but a lower abundance of galaxies compared with GOGREEN near the knee of the GSMF. The discrepancy with GOGREEN at the knee of the GSMF is plausibly explained as a result of a slight mismatch in the halo mass selection for Hydrangea and GOGREEN, rather than the resolution-related issues discussed above for BAHAMAS/MACSIS. Indeed, Ahad et al. (2021) found reasonably good agreement between Hydrangea and GOGREEN where they used a simple parametric scaling to account for the

halo mass difference between Hydrangea and GOGREEN. Using the BAHAMAS/MACSIS suite we have derived a factor of ≈1.4 to scale the GSMF from the Hydrangea sample to that of a sample with the GOGREEN mean halo mass. We show that the scaled Hydrangea GSMF, shown in Fig. 4 with the dotted cyan curve, is in much better agreement with GOGREEN near the knee (i.e. consistent with Ahad et al. 2021). Scaling the curves up by this factor does, however, exacerbate the differences with respect to GOGREEN at the very lowest and highest masses. In other words, while Hydrangea reproduces the amplitude of the cluster GSMF relatively well (once the difference in halo mass is accounted for), the shape of the predicted GSMF differs in detail from that observed in GOGREEN clusters.

Finally, turning to the GSMF predictions of TNG300, we see a reasonably good match to the field, for all but the highest stellar masses. There is an excess of galaxies above $log_{10}(M_*/M_\odot) \approx 11.5$ when compared to GOGREEN data, which is also apparent in Fig. 3. This gives the field GSMF a flattened appearance. These very massive galaxies are highly likely to be BCGs at the centres of clusters which may indicate overcooling of BCGs in the model.<fnref n="3" /> A similar excess of massive galaxies is also seen in the cluster GSMF (right-hand panel). TNG300 also shows a similar deficit of galaxies near the knee with an excess at the highest masses, giving the GSMF a flat appearance. However, as for the case of Hydrangea, most of the discrepancy at the knee is due to a slight mismatch in the mean halo mass of the TNG300 haloes and the GOGREEN systems. Again, using BAHAMAS/MACSIS we derive a scaling factor to scale the TNG300 GSMF (dotted curve). Similar to Hydrangea, the issue at the highest masses is worsened somewhat by this rescaling. There is also a notable difference at $log_{10}(M_*/M_\odot) \lesssim 9.5$ between TNG300 and EAGLE/Hydrangea but, without observational data, it is unclear which one is more realistic.

In Fig. 5 we showed the GSMFs in the data and simulations split according to star-forming versus quiescent status. The observed behaviour is broadly replicated by BAHAMAS in the field; at least in that quiescent galaxies are more abundant at the high-mass end (although not significantly so) and the two curves cross at $log_{10}(M_*/M_\odot) \approx 10.75$. It overpredicts the abundance of star-forming galaxies by a factor of a few in the interval $11.0 < log_{10}(M_*/M_\odot) < 11.5$, while also overpredicting the abundance of quiescent galaxies at the low-mass end. Neither of the features can be accounted for by quantitatively altering the definition of quiescence (Section 3.3), as indicated by the shaded regions. For the cluster sample, the BAHAMAS/MACSIS mass function of star-forming galaxies is much flatter than in the field. This is in contrast with observations which show that the shape of the star-forming GSMF does not differ much between the field and cluster environments. The quiescent population is a better match but it shows the same features as what was seen for the total GSMF (largely because quiescent galaxies dominate the total population in this sample). Again these differences cannot be accounted for by star-forming/quenched designation (shaded regions) nor by uncertainties associated with choosing ten GOGREEN-like haloes (error bars).

EAGLE reproduces the observed field trends reasonably well below stellar masses of $log_{10}(M_*/M_\odot) = 10.75$. In the cluster population, Hydrangea performs comparatively well at matching the GOGREEN measurements. To within the uncertainty induced

<footnote n="3">³Using the scaled stellar masses from rTNG results in an improved agreement with the observations for the GSMF but does not significantly alter the main quenching results presented below.</footnote>
by the SFMS offset, it matches the star-forming GSMF. However, it does not perform quite so well on the quiescent population: Hydrangea shows a steep increase in low mass ($log_{10}(M_*/M_\odot) \lesssim 9.75$) quiescent galaxies, with a steady power-law decline towards higher stellar masses. It underestimates the abundance of intermediate-mass quiescent galaxies, such that there is a complete absence of the 'knee' feature. This behaviour at $z \approx 1$ is in contrast to the behaviour of these simulations at the present day. In particular, Bahé et al. (2017b) showed that the Hydrangea quenched fractions of satellites (typically 0.8 but with a mild stellar- and host- mass dependence) in massive clusters at $z \approx 0$ exceeded that seen in the observations of Wetzel et al. (2012), implying a larger-than-observed abundance of quiescent cluster galaxies at the present day (see fig. 6 of that study). Lowest stellar masses aside, Hydrangea underestimates or, at best, matches the observed GOGREEN cluster quenched fractions. Evidently, the role of environment evolves substantially between $z \approx 0$ and the present day in these simulations.

Finally, TNG300 shows qualitatively the best match to the observations in the field. As noted in the main text, the predictions are more sensitive to the definition of quiescence than the other simulation. In addition, the abundance of quenched galaxies declines abruptly below $log_{10}(M_*/M_\odot) \approx 10.6$, which is likely linked to the transition from a very effective (at quenching) low accretion-rate mode of AGN feedback at higher masses to a regime where stellar feedback and high accretion-rate AGN are less effective at quenching galaxies (see fig. 3 and discussion in Donnari et al. 2019). In the cluster sample, TNG300 is similar to BAHAMAS/MACSIM in that it does a reasonably good job of matching the quiescent GSMF in abundance and shape, albeit missing the knee. The mass function for the star-forming galaxies is, on the other hand, typically a factor of 2 to 3 lower in amplitude than observed in GOGREEN clusters.

<footnote n="1">¹Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK</footnote>

<footnote n="2">²Department of Physics, The University of Liverpool, Liverpool L69 3BX, UK</footnote>

<footnote n="3">³The Cockcroft Institute of Accelerator Science and Technology, Warrington WA4 4AD, UK</footnote>

<footnote n="4">⁴Department of Physics and Astronomy, University of Waterloo, Waterloo ON N2L 3G1, Canada</footnote>

<footnote n="5">⁵Waterloo Centre for Astrophysics, University of Waterloo, Waterloo ON N2L 3G1, Canada</footnote>

<footnote n="6">⁶Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands</footnote>

<footnote n="7">⁷INAF – Osservatorio Astronomico di Trieste, via G.B. Tiepolo 11, I-34143 Trieste, Italy</footnote>

<footnote n="8">⁸Laboratoire d'astrophysique, École Polytechnique Fédérale de Lausanne (EPFL), CH-1290 Sauverny, Switzerland</footnote>

<footnote n="9">⁹GEPI, Observatoire de Paris, Université PSL, CNRS, Place Jules Janssen, F-92190 Meudon, France</footnote>

<footnote n="10">¹⁰INAF – Osservatorio astronomico di Padova, Vicolo Osservatorio 5, I-35122 Padova, Italy</footnote>

<footnote n="11">¹¹Department of Physics and Astronomy, University of California, Irvine, 4129 Reines Hall, Irvine, CA 92697, USA</footnote>

<footnote n="12">¹²IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, I-34014 Trieste, Italy</footnote>

<footnote n="13">¹³Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Concepción, Concepción, Chile</footnote>

<footnote n="14">¹⁴Department of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA</footnote>

<footnote n="15">¹⁵Departamento de Astronomía, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción, Chile</footnote>

<footnote n="16">¹⁶Department of Physics, University of Helsinki, Gustaf Hällströminkatu 2a, FI-00014 Helsinki, Finland</footnote>

<footnote n="17">¹⁷Research School of Astronomy and Astrophysics, The Australian National University, ACT 2601, Australia</footnote>

<footnote n="18">¹⁸Centre for Gravitational Astrophysics, College of Science, The Australian National University, ACT 2601, Australia</footnote>

<footnote n="19">¹⁹School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, England</footnote>

<footnote n="20">²⁰Department of Physics and Astronomy, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada</footnote>

<footnote n="21">²¹Departamento de Ciencias Físicas, Universidad Andres Bello, Fernandez Concha 700, Las Condes 7591538, Santiago, Región Metropolitana, Chile</footnote>

<footnote n="22">²²European Space Agency (ESA), European Space Astronomy Centre, Vilanueva de la Cañada, E-28691 Madrid, Spain</footnote>

<footnote n="23">²³Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan 1, E-44001 Teruel, Spain</footnote>

<footnote n="24">²⁴Department of Physics and Astronomy, The University of Kansas, 1251 Wescoe Hall Drive, Lawrence, KS 66045, USA</footnote>

<footnote n="25">²⁵Institut de Recherche en Astrophysique et Planétologie, 14 Avenue Edouard Belin, 31400, Toulouse, France</footnote>

<footnote n="26">²⁶European Southern Observatory, Karl-Schwarzschild-Str. 2, D-85748 Garching, Germany</footnote>

<footnote n="27">²⁷Department of Astronomy and Astrophysics, University of Toronto, Toronto, M5S 3H4, Canada</footnote>

<footnote n="28">²⁸Steward Observatory and Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA</footnote>

<footnote>This paper has been typeset from a TEX/LATEX file prepared by the author.</footnote>
by the SFMS offset, it matches the star-forming GSMF. However, it does not perform quite so well on the quiescent population: Hydrangea shows a steep increase in low mass ($log_{10}(M_*/M_\odot) \lesssim 9.75$) quiescent galaxies, with a steady power-law decline towards higher stellar masses. It underestimates the abundance of intermediate-mass quiescent galaxies, such that there is a complete absence of the 'knee' feature. This behaviour at $z \approx 1$ is in contrast to the behaviour of these simulations at the present day. In particular, Bahé et al. (2017b) showed that the Hydrangea quenched fractions of satellites (typically 0.8 but with a mild stellar- and host- mass dependence) in massive clusters at $z \approx 0$ exceeded that seen in the observations of Wetzel et al. (2012), implying a larger-than-observed abundance of quiescent cluster galaxies at the present day (see fig. 6 of that study). Lowest stellar masses aside, Hydrangea underestimates or, at best, matches the observed GOGREEN cluster quenched fractions. Evidently, the role of environment evolves substantially between $z \approx 0$ and the present day in these simulations.

Finally, TNG300 shows qualitatively the best match to the observations in the field. As noted in the main text, the predictions are more sensitive to the definition of quiescence than the other simulation. In addition, the abundance of quenched galaxies declines abruptly below $log_{10}(M_*/M_\odot) \approx 10.6$, which is likely linked to the transition from a very effective (at quenching) low accretion-rate mode of AGN feedback at higher masses to a regime where stellar feedback and high accretion-rate AGN are less effective at quenching galaxies (see fig. 3 and discussion in Donnari et al. 2019). In the cluster sample, TNG300 is similar to BAHAMAS/MACSIM in that it does a reasonably good job of matching the quiescent GSMF in abundance and shape, albeit missing the knee. The mass function for the star-forming galaxies is, on the other hand, typically a factor of 2 to 3 lower in amplitude than observed in GOGREEN clusters.

<footnote n="1">¹Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK</footnote>

<footnote n="2">²Department of Physics, The University of Liverpool, Liverpool L69 3BX, UK</footnote>

<footnote n="3">³The Cockcroft Institute of Accelerator Science and Technology, Warrington WA4 4AD, UK</footnote>

<footnote n="4">⁴Department of Physics and Astronomy, University of Waterloo, Waterloo ON N2L 3G1, Canada</footnote>

<footnote n="5">⁵Waterloo Centre for Astrophysics, University of Waterloo, Waterloo ON N2L 3G1, Canada</footnote>

<footnote n="6">⁶Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands</footnote>

<footnote n="7">⁷INAF – Osservatorio Astronomico di Trieste, via G.B. Tiepolo 11, I-34143 Trieste, Italy</footnote>

<footnote n="8">⁸Laboratoire d'astrophysique, École Polytechnique Fédérale de Lausanne (EPFL), CH-1290 Sauverny, Switzerland</footnote>

<footnote n="9">⁹GEPI, Observatoire de Paris, Université PSL, CNRS, Place Jules Janssen, F-92190 Meudon, France</footnote>

<footnote n="10">¹⁰INAF – Osservatorio astronomico di Padova, Vicolo Osservatorio 5, I-35122 Padova, Italy</footnote>

<footnote n="11">¹¹Department of Physics and Astronomy, University of California, Irvine, 4129 Reines Hall, Irvine, CA 92697, USA</footnote>

<footnote n="12">¹²IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, I-34014 Trieste, Italy</footnote>

<footnote n="13">¹³Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Concepción, Concepción, Chile</footnote>

<footnote n="14">¹⁴Department of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA</footnote>

<footnote n="15">¹⁵Departamento de Astronomía, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción, Chile</footnote>

<footnote n="16">¹⁶Department of Physics, University of Helsinki, Gustaf Hällströminkatu 2a, FI-00014 Helsinki, Finland</footnote>

<footnote n="17">¹⁷Research School of Astronomy and Astrophysics, The Australian National University, ACT 2601, Australia</footnote>

<footnote n="18">¹⁸Centre for Gravitational Astrophysics, College of Science, The Australian National University, ACT 2601, Australia</footnote>

<footnote n="19">¹⁹School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, England</footnote>

<footnote n="20">²⁰Department of Physics and Astronomy, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada</footnote>

<footnote n="21">²¹Departamento de Ciencias Físicas, Universidad Andres Bello, Fernandez Concha 700, Las Condes 7591538, Santiago, Región Metropolitana, Chile</footnote>

<footnote n="22">²²European Space Agency (ESA), European Space Astronomy Centre, Vilanueva de la Cañada, E-28691 Madrid, Spain</footnote>

<footnote n="23">²³Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan 1, E-44001 Teruel, Spain</footnote>

<footnote n="24">²⁴Department of Physics and Astronomy, The University of Kansas, 1251 Wescoe Hall Drive, Lawrence, KS 66045, USA</footnote>

<footnote n="25">²⁵Institut de Recherche en Astrophysique et Planétologie, 14 Avenue Edouard Belin, 31400, Toulouse, France</footnote>

<footnote n="26">²⁶European Southern Observatory, Karl-Schwarzschild-Str. 2, D-85748 Garching, Germany</footnote>

<footnote n="27">²⁷Department of Astronomy and Astrophysics, University of Toronto, Toronto, M5S 3H4, Canada</footnote>

<footnote n="28">²⁸Steward Observatory and Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA</footnote>

<footnote>This paper has been typeset from a TEX/LATEX file prepared by the author.</footnote>