Week 6

Hello there, avid blog readers, I’m Adam, the group coordinator, and this will be the final update from the SHREDS team before our eagerly awaited paper is published in just a couple of week’s time. This record of our exploits over the past 6 weeks will undoubtedly sit beside the great literary works of authors such as Dickens, Austen, and Bronte in a library of timeless classics. There have been highs, and there have been lows. There have been laughs, and quite possibly tears. Python has worked first time, and QTI has crashed without saving. Group meetings were forgotten, and entire lab sessions wasted. Nothing has been quite so emotional for young British adults since Bake-off moved to Chanel 4. All of this was done not because the IOP said we had to (yeah it was), but because we lay awake at night longing to determine the properties of high redshift galaxies in order to see what local galaxies might have looked like when they were younger. I therefore dedicate this final testament to my fellow members of the SHREDS team, may our time together not be quickly forgotten by a couple of Jager bombs in Sugar.

This week, the team wrapped up the data collection phase, began writing the report, and started thinking about their results and what the significance of them are. Throughout the project, my main area of investigation has been star formation rates (SFR), and how it changes with redshift and stellar mass in galaxies. There are a number of different methods to determine the SFR in a distant galaxy at our disposal, but throughout this project, we have used simple scaling relations, which have been calibrated in previous work. Essentially, we measure the flux of light at certain wavelengths emitted by each galaxy, then convert this flux to a luminosity using the redshift of that galaxy. Luminosity is a measure of the amount of light emitted by a galaxy and is therefore information about how many stars are forming. However, different wavelengths are absorbed by dust and the interstellar medium (ISM) to a greater or lesser degree, for example, UV light is absorbed by dust, and re-emitted in the far infrared (FIR), whereas radio (tracing mostly supernovae) is not attenuated. Thus, radio gives us an upper limit to SFR, and explains why SFRs calculated using UV are so much lower. I have included one of the firsts plots I made of SFR against redshift (figure 1: left), and as you can see, a lot of progress has been made since week one.

Left (end of first week of labs): Mean SFR against redshift, calculated using Lyman-Alpha luminosity. Right (final plot and result of the project!): SFR evolution with redshift using Lyman-alpha, UV, Radio, and FIR. Some results from the literature of typical SFR (SFR*) are also plotted.

Figure 1 shows that across all our data, observational SFRs tends to increase with redshift. Specifically, we find that Lyman-Alpha SFR increases at a rate of 0.140 ±0.004 solar masses per year per redshift (logged values), and that UV SFR increases at a rate of 0.099 ±0.004 solar masses per year per redshift. It is important to keep in mind with these results (the increase in SFR with redshift) are a consequence of a selection bias. As we observe galaxies at higher redshift with a more or less constant flux limit we end up having a higher and higher luminosity limit, which means that we are only able to observe galaxies with high enough SFRs, which will shift the average SFR up with redshift. The reason that UV SFRs are generally lower than Lyman-Alpha SFRs is most likely due to dust (Lyman-alpha SFRs include a dust correction while UV are observed) and the ionisation efficiency, which means that the UV SFRs themselves can be off if Lyman-alpha emitters are experiencing bursty SFRs.

The results for Smit et al. (2018), Sobral et al. (2012), and Reddy et al. (2018) are results for the SFR function, not (like our results) for the average SFR of their sample. At lower redshift, we are able to observe a larger number of galaxies forming stars at a much lower rate than SFR*, thus our results are lower at this redshift (SFR < SFR*). According to figure 1, we find that at redshifts around 4-5, the flux limit is such that we observe the typical SFR. Above this redshift, our selection bias pushes the observed SFRs up, and hence at higher redshifts we find SFR>SFR*.

SFRD against redshift calculated using Lyman-Alpha and UV. Some results from the literature are also plotted.

In addition to looking at SFR evolution with redshift, I also looked into the star formation rate density (SFRD). SFRD is essentially a measure of the number of stars being produced per unit volume and can be a better indication of how SFR has changed since the early universe. It is important to note that the SFRDs shown in figure 2 were not calculated by simply dividing the SFRs shown in figure 1 by the volume of the corresponding redshift slice. For example, the Lyman-Alpha SFRDs below were calculated by integrating Lyman-Alpha luminosity functions, to give a Lyman-Alpha luminosity density, which we converted to a SFRD, see Sobral et al. (2018) for more detail.

Figure 2 shows that in general, SFRD decreases with redshift. In a similar fashion to figure 1, we see that Lyman-Alpha SFRD and UV SFRD diverge, thought to be due to dust and the ionisation fraction increasing with redshift. This is in good agreement with previous work by Sobral et al. (2018), where a more detailed analysis of the increase in (Lyman-Alpha SFRD/ UV SFRD) with redshift is presented. The results from Gruppioni et al. (2013) are calculated using FIR data, and Rowan-Robertson et al. (2016) data is for starburst galaxies (galaxies forming stars at a very high rate), and hence form upper limits which are consistent with our results. 

For more detailed insight into SFRs, how they were calculated, how they vary with stellar mass in galaxies, and everything else discussed in this blog, look out for our paper: “Properties of high redshift galaxies and their evolution with cosmic time: morphologies, SFR, and AGN”. I hope you have enjoyed reading this blog, but for now, goodbye.

SHREDS out (mic drop).

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