Hydrogen reionization

Figure 4.27: Median gas temperature as a function of overdensity in AREPO (dashed) and GADGET (solid) at redshift z= 3. AREPO produces higher temperatures at low redshift.

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Figure 4.28: Median neutral fraction as a function of overdensity in AREPO (dashed) and GAD-GET (solid) at redshift z = 7.6 (left) and z = 3 (right). In the beginning of reionization, both codes show similar neutral fraction distribution, where high density regions get ionized first. At redshift z = 3, the neutral fraction is directly proportional to the overdensity, except at star forming densities. The low density gas in AREPO is less ionized.

calculations since the AREPO code is still in a development stage, where such treatment is not yet implemented. Even though the treatment of stellar winds is already included in GADGETand we have used is before, we do not adopt it here in order to ensure consistent comparison between the two codes.

4.4.2. Star formation rate density and neutral fraction evolution

Since both codes use the same sub-resolution model for star formation, we expect that the redshift evolution of the SFR density is similar in the simulations. However, in Figure 4.24 we show that there is a difference between the two codes for all redshift and GADGET produces systematically more stars than AREPO in this scenario. We expect that already this difference in the simulation properties will result in some differences in the reionization histories.

In both simulations the global SFR density decreases when photoheating during reion-ization is included in the simulations. As the gas gets heated up it can escape the deep potential wells of the high density regions, leaving them with less gas to convert to stars.

The effect is larger for GADGET, where the decrease reaches ∼ 20% at redshift z = 3, versus ∼5% for AREPO.

The extent to which the SFR density is inhibited is different from our previous findings in Chapter 3, where a more significant decrease is reported. This suggests that stellar winds are very important in effectively distributing hot gas and therefore modulating star

Figure 4.29: Lyman-α flux probability (left) and power spectrum (right) in both simulations, compared with observations from McDonald et al. (2000) and Kim et al. (2007). Both AREPO and GADGET results fall outside of the errors of the observations. However, both simulations recover the given trend, indicating that they both represent reionization to some extent.

formation, an effect already found by Pawlik & Schaye (2009). Without winds photoheated gas tends to linger in dense regions and cool down more efficiently, thus being subject to star formation. When winds are present, the photoheated gas is expelled from high density regions more efficiently and has more time to cool down, resulting in a reduction of the global star formation rate.

The volume-averaged neutral hydrogen fraction evolution differs substantially between the two codes. In AREPO, the universe is reionized at lower redshift z ∼5, in comparison to z ∼ 6 in GADGET. This difference is most likely primarily due to the SFR densities, mentioned in the previous section. The lower SFR in AREPO results in a lower total number of photons available for ionizing the hydrogen atoms and leads to a delay in the reionization. In Figure 4.25, we show the time evolution of the neutral fraction. In both cases, the epoch of reionization has a similar redshift duration, ∆z ∼ 1.1, where we have computed ∆z as

∆z =z(nHI = 0.5)−z(nHI = 10⁻³). (4.40) However, in physical units, reionization in AREPO takes more time. One reason for this extended process might be that at lower redshifts structures are further apart and therefore it takes more time for photons to travel to the ionization sites and the overlap phase, at the end stages of reionization, is prolonged. Another reason might be the ability of the scheme to produce accurate shadows, which is absent in GADGET. In this case, some regions are more effectively shielded from the photons and are ionized at later times.

4.4.3. Temperature and ionization states

We also compare visually the ionization and temperature states of the gas in both simula-tions at two different redshift z = 7.6 – before reionization, and z = 3 – after reionization is completed. The maps are shown in Figure 4.26. The morphology and structure of the

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Figure 4.30: Baryon fraction as a function of DM halo mass. Results are compared for AREPO and GADGET for simulations both with and without radiative transfer. Since both simulations are without stellar winds, the baryon fraction is around the cosmological valuef_b = 0.18. When reionization is included gas and stellar content in low mass DM halos is decreased. The change in AREPO is insignificant - around 1%, compared to the change in GADGET - around 20%.

ionized regions agree well in the simulations – they are in the same places and have similar shapes. There are some differences in the level of ionization of the regions and at later times, at redshift z = 3, it is clear that in GADGET the gas is on average more highly ionized, as we have discussed in the previous section. In both simulations filaments are less ionized than the lower density gas.

The temperature maps also show differences in the internal structure of the ionized re-gions. Although in both simulations ionized gas is heated above 10⁴K, the cooling efficiency in GADGET appears to be higher and the gas cools below 10⁴K in some regions. This is also evident in comparing the median temperature of the gas at different overdensities for the two simulations (Figure 4.27). AREPO produces higher temperature gas at low over-densities and lower temperature gas at high overover-densities, compared to GADGET. This distribution of temperatures suggests thatGADGETis more efficient in cooling the ionized gas at low overdensities or, alternatively, thatAREPOis more efficient in photoheating the it. It is interesting to note that despite the lower temperature, low density gas is more ionized in GADGET than in AREPO, as shown in Figure 4.28.

At higher redshift, as reionization begins, the dense gas gets ionized first. This is the material closest to he sources of ionizing photons. After reionization is completed, the neutral fraction becomes becomes directly proportional to the overdensity of the gas, with under-dense region being more ionized than overdense regions, except the star forming gas.

4.4.4. Lyman-α forest

We construct Lyman-αabsorption spectra along different lines of sight in both simulations and construct probability distribution functions and power spectra at redshift z = 3.

As in Chapter 3, we compare our results to observations by McDonald et al. (2000) and Kim et al. (2007), plotted in Figure 4.29. Both functions agree marginally well with the observations. Since our models do not include stellar winds, we can not expect a very good agreement with the data. However, the results suggest that with the proper treatment of winds, we will be able to reproduce a sensible thermal state of the gas at redshift z = 3, as shown in Chapter 3.

4.4.5. Baryon fraction

Finally, we want to study how the gas and stellar content of DM halos changes when photoheating from reionization takes place. In Figure 4.30 we show the baryon fraction of DM halos at redshift z = 3. Since there are no winds in our simulations, the baryon fractions is approximately 0.18. It decreases for low mas halos when photoheating is included. We have already observed this effect in Chapter 3. The decrease in the gas and stellar component of halos is larger forGADGET –∼20% than for AREPO –∼1%. This difference suggest that GADGETis more efficient in expelling hot gas from low mass halos and thus decreasing the baryon fraction.