SPT PS » History » Version 31
Nikhel Gupta, 10/15/2014 02:31 PM
1 | 13 | Nikhel Gupta | h1. SPT 150 GHz luminosity function and SZE contamination |
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2 | 11 | Nikhel Gupta | |
3 | 6 | Nikhel Gupta | h2. Cluster and galaxy sample |
4 | 3 | Nikhel Gupta | |
5 | 2 | Nikhel Gupta | We build luminosity function from the SPT AGNs (at 150 and 90 GHz) and SUMSS galaxy catalog (at 843 MHz). We use MCXC catalog of galaxy clusters with a total number of 1734 clusters out of which 139 and 333 clusters are there in SPT and SUMSS region respectively. |
6 | 1 | Nikhel Gupta | |
7 | 1 | Nikhel Gupta | h3. SPT AGN sample |
8 | 6 | Nikhel Gupta | |
9 | 16 | Nikhel Gupta | There are 4769 SPT point sources in the present catalog (all_fields_list_brady_astrom_corrected.txt) with information about the S/N as well as Flux (in mJy). Out of these point sources we chose those which have counterparts in the SUMSS catalog within the positional uncertainty of the SPT point sources at 3-sigma level. This allow us to select the AGN sample from the SPT point sources. |
10 | 9 | Nikhel Gupta | |
11 | 22 | Nikhel Gupta | The positional uncertainty is {{latex(\sigma_{total}^2 = \sigma_{sys}^2 + ((FWHM_{beam})/S2N)^2)}}, where the FWHM is 1.6' at 90 GHz, 1' at 150 GHz and 0.8' at 220 GHz. Also, sigma_sys is about 10". We chose the smallest sigma_total out of 90, 150 or 220 GHz. The 3-sigma level is chosen by plotting the surface density of the SUMSS galaxies at different sigma levels as in the figure below (at 3-sigma level the SUMSS surface density is equivalent to background density). |
12 | 23 | Nikhel Gupta | |
13 | 1 | Nikhel Gupta | !SUMSS_in_SPT_surface_density.png! |
14 | 1 | Nikhel Gupta | |
15 | 17 | Nikhel Gupta | There are 3446 SPT point sources (AGNs) which have SUMSS counterparts at 3-sigma level. |
16 | 17 | Nikhel Gupta | |
17 | 17 | Nikhel Gupta | h3. SUMSS sample |
18 | 17 | Nikhel Gupta | |
19 | 17 | Nikhel Gupta | There are 211,050 galaxies in SUMSS catalog (2007). More information about the catalog can be found here: [[http://www.physics.usyd.edu.au/sifa/Main/SUMSS]] |
20 | 18 | Nikhel Gupta | |
21 | 18 | Nikhel Gupta | h2. Luminosity function |
22 | 18 | Nikhel Gupta | |
23 | 20 | Nikhel Gupta | h3. Estimation of the total number of PS or galaxies in a luminosity bin for each cluster |
24 | 19 | Nikhel Gupta | |
25 | 20 | Nikhel Gupta | * We do not have the redshift information about the SPT PS and SUMSS galaxies, so we assume that they are at the redshift of the cluster in concern. In order to construct the luminosity function we take a logarithmic luminosity bin and loop over all the clusters which are there in the SPT or SUMSS region. |
26 | 1 | Nikhel Gupta | |
27 | 30 | Nikhel Gupta | * For each cluster we use its redshift to calculate luminosity distance and the K-correction for the luminosities. Using mass and redshift we calculate the radius and theta_200 for the cluster. In MCXC catalog mass and radius of the cluster are given as the region where the overdensity is 500 times the critical density of the universe and we use NFW profile and Duffy et al. to change them to M_200cr and R_200cr. |
28 | 20 | Nikhel Gupta | |
29 | 20 | Nikhel Gupta | * We use a flux cut of 10 mJy, 6 mJy and 30 mJy for the SPT 90 GHz, SPT 150 GHz and SUMSS samples, which are found from the flux histograms (logN-logS plots) for these samples. We find the luminosity cut for each cluster corresponding to these flux cut. |
30 | 20 | Nikhel Gupta | |
31 | 20 | Nikhel Gupta | * For each cluster we find all matching radio galaxies within the theta_200, convert the given flux of SPT (both at 90 and 150 GHz) and SUMSS sample to luminosity and count those galaxies whose luminosity lies in the logarithmic luminosity bin we took. |
32 | 20 | Nikhel Gupta | |
33 | 20 | Nikhel Gupta | h3. Background estimation for a luminosity bin for each cluster |
34 | 20 | Nikhel Gupta | |
35 | 20 | Nikhel Gupta | * For the background estimation we use the field logN-logS plots and find the number of PS or galaxies in the logarithmic flux bin that corresponds to the logarithmic luminosity bin we took. In order to find background for each cluster we multiply this field number with the surface area of that cluster (pi*theta_200^2). |
36 | 1 | Nikhel Gupta | |
37 | 25 | Nikhel Gupta | We do this for all the clusters and stack the total number and background number of galaxies for each logarithmic luminosity bin. The subtraction of the background number from the total number for each logarithmic luminosity bin gives us the number of PS or galaxies within theta_200 and at the redshift of the clusters. This is then normalized by the total M_200cr of the clusters which contributed to each luminosity bin to get <N_PS>. This can be further divided by the luminosity bin size to get the luminosity function as dn/dlogP. |
38 | 20 | Nikhel Gupta | |
39 | 20 | Nikhel Gupta | h3. SPT luminosity function |
40 | 29 | Nikhel Gupta | |
41 | 28 | Nikhel Gupta | !luminosity_func_SPT150.png! |
42 | 30 | Nikhel Gupta | As described before the luminosity function is normalized by the total M_200cr of the clusters contributing to a luminosity bin. Another way is to normalize it with the total volume of the contributing clusters. There is a complication when placing the LF in units of Mpc^-3 (volume). Because we define the virial region R_200cr as the region with overdensity of 200 with respect to critical density, there is then a natural redshift sensitivity. That is cluster virial region densities will scale as E^2(z). So normalizing by total mass is a better choice as the number of galaxies per unit mass is about the same independent of the redshift. |
43 | 26 | Nikhel Gupta | |
44 | 1 | Nikhel Gupta | h3. SUMSS luminosity function |
45 | 31 | Nikhel Gupta | !luminosity_func_SUMSS.png! |