Comparison with models

In the introduction, we mentioned that ram-pressure stripping of the cold gas is currently not included in most semianalytic models of galaxy formation. Ideally, a successful model should be able to statistically reproduce galaxy properties at low redshift, including the differences in galaxy properties as a function of environment. We show now that current models actually fail to reproduce trends in the cold gas content in galaxies as a function of density.

In semianalytic models, gas evolution is regulated by a set of analytic prescriptions that

2We remind the reader that C is an indicator of the bulge-to-total ratio. See Section 4.2 for more details.

describe the physics of the baryons, such as heating, cooling, star formation, supernovae and AGN feedback. Because our understanding of the physical processes involving baryons is still incomplete, the models themselves are approximate.

Here, we use the SAMs from Guo et al. (2011), which contain an improved description of the environmental effects. The models are implemented on the Millennium II simulation (Boylan-Kolchin et al. 2009). In Guo’s models, environmental processes are activated when a galaxy enters the virial radius of a more massive halo. Both tidal and ram-pressure stripping act on thehot gas distributed around the galaxy, which is then gradually stripped.

This roughly reproduces the fraction of actively star forming galaxies as a function of projected distance from the cluster center, as shown in Figure 3 of Guo’s paper. The stripping does not affect the cold gas component of galaxies. This assumption, as Guo et al. themselves discuss, is unrealistic for galaxies in the central regions of rich clusters. We now show that this implementation fails to reproduce the cold gas trends in less extreme environmental regimes.

From the results of Guo’s models on the MII simulation atz ∼0.05, we extract a mock catalogue of galaxies with Log M_?= [10; 11.5]. We then apply the same criteria used on our data to evaluate a local density parameter (N) for each target, counting the neighbours more massive than M_?= 10^9.5Minside a cylinder of 1 Mpc radius and depth±500km s⁻¹. In addition, we acquire from the catalogue the following quantities: stellar masses, cold gas masses (used as a proxy for Hi), bulge masses, and star formation rates. For details on how these different properties are computed, we refer the reader to Guo et al. (2011). Here we only recall how gas content and star formation are regulated. The cold gas content of a galaxy can be supplied both by diffuse infall from the surroundings and by new material from accreted satellites, and can be depleted by SN heating or SF. The ISM is distributed in a disk, with size which scales as the product of the virial radius and the spin parameter of the corresponding host halo. The total star formation rate δM_?/δt follows a simplified version of the Kennicutt-Schmidt law. It is proportional to the total mass of cold gas above a given threshold, at each timestep. This critical gas mass required for stars to form is set because star formation is believed to happen if the surface gas density exceeds a critical value given by the Toomre stability criterion. In the model, this stability criterion is a global rather than local one.

In Figure 6.7, we show the normalized local density parameter distribution and the stellar mass distribution for each density bin used in this section for our simulation cat-alogue. As expected, the mock data span a wider range of local densities than sample A data; for this comparison we restrict the analysis toN ≤30. With this cut, the meanN of

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Figure 6.7: Local density and stellar mass properties for the mock catalogue. Left: distribu-tion of the number density parameter N (+1 for convenience). In the mock data, we have objects located in higher density environments. Right: Normalized M_? distribution for each of the density bins considered.

the higher mock density bin results 17.1, similar to the mean value for sample A of 15.7.

In Figure 6.8 we reproduce the comparison plot between Hiand star formation of Figure 6.6 using mock data. Orange lines represent the cold gas fractions, blue lines the global specific star formation rates (we do not have fibre ones). We overplot sample A trends from Figure 6.6 as dashed lines, to allow the reader a direct comparison.

In the upper row, we notice that mock data exhibit a similar dependence on environment in both intervals of stellar mass. This is expected because a starvation-like mechanism does not vary significantly with M_?. The models reproduce the observedaverage star formation decrease withN, but the gas trends (in particular for lower mass galaxies) are too shallow:

the cold gas drops to only 40-50% of the field values, as opposed to the 20-30% measured for the Hi in the previous section.

We note that the apparently puzzling result that the star formation (blue line) is more significantly affected by environment than the cold gas (orange) is in fact a consequence of the way star formation is treated in the models. In galaxies where the cold gas mass has fallen below the threshold value, star formation shuts down³. Cold gas can linger just below the threshold. This is the reason why star formation is quenched faster than the cold gas is depleted. In Figure 6.9 we compare the specific star formation distributions of sample A galaxies (filled black histograms) with values from the mock catalogue (empty

3Excluding episodes of merger-induced starburts.

Figure 6.8: Mock data from SAMs: Comparison of Hi gas fraction (orange) depletion, and global specific star formation rate (blue) quenching, as a function of local density for 2 bins of M_?, as indicated on top. Colours and panels as in Figure 6.6. The dashed lines are the trends for sample A(Figure 6.6).

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Figure 6.9: Comparison of specific star formation rates of our data (hatched histograms) and the mock ones (solid histograms). The two different rows are for the different stellar mass bins, while the bins refer to different densities (as reported on top).

red histograms) in the two M? bins considered (top and bottom row), and for each den-sity bins (as indicated on top). The relative behavior of real and mock data is similar, but mock data exhibit a tail of very low values down to SFR/M?∼10⁻¹⁶yr⁻¹, which con-tributes in lowering the mean (note that observational estimates of star formation rates are not reliable in this regime; it is more meaningful to focus on star forming objects only).

An opposite effect appears for the cold gas content, which is not properly depleted in the models. Most of mock galaxies (76%) have more than 10% of cold gas; in the same mass range, only 50% of the detections of the GASS representative sample are characterised by a high gas fraction (and the number decreases if we include the non-detections).

If we restrict the analysis to galaxies with SFR/M_?≥ 10^−11.2yr⁻¹, for which the gas is above the star formation threshold (middle row in Figure 6.8), we see how the star formation and the cold gas are coupled by construction. The slow decrease with density is the result of the starvation mechanism: since the cold gas is not directly removed, only star formation consumes it. Without new replenishment of the cold fuel, star formation in satellites galaxies declines more rapidly than in isolated galaxies, so that the fraction of

quenched objects increases with N. The trend is weak, because we are selecting against the objects that have already been strongly affected. The model clearly does not reproduce the observed dependence of cold gas content on density in lower mass galaxies.

We can also select disk-dominated galaxies in the mock catalogue, using the cut in bulge-to-total ratio at 0.3. In this case, as seen in Figure 6.8, bottom row, the cut does not affect the global trends with environmental local density. Over the whole stellar mass range, the disk galaxies in our mock catalogue exhibit almost exactly the same trends as seen in the first row, as starvation does not preferentially act on disks. For this reason, the models once again fail to reproduce the observed trends for galaxies with M_?≤10^10.5M, where gas is clearly depleted faster than the SF is quenched.

Figure 6.10: Comparison of the fraction of galaxies quenched by environmental effect (with respect to the isolated objects) for our data (black, hatched lines) and the mock catalogue (solid red ones). The two different panels refer to the different stellar mass bins, as reported on top.

A robust comparison can also be performed between the fraction of quenched objects with SFR/M_?≤10^−11.2yr⁻¹. Although in each bin the fraction of quenched galaxies in the data is higher than in the models (Figure 6.9), models and observations agree reasonably well in terms of the trend with density. In Figure 6.10, we compare the quenched fraction N/N_q of each density bin to the one of isolated objects (N/N_q)₀, at fixed stellar mass, in both real (black) and mock data (red points). The values of N/N_q-(N/N_q)₀ are consistent, and, as expected, increase with increasing local density.