Dependence of IR-Bright AGN Activity on Halo Mass and Redshift in the SPT Cluster Surveys Benjamin Floyd B 1 Mark Brodwin 1 Rebecca E. A. Canning 2 The SPT Collaboration 1University of Missouri–Kansas City 2University of Portsmouth Motivation We know that environment plays a role in galaxy evolution (e.g., Dressler, 1980, 1984), but how exactly? What are the predominant environmental-dependent mechanisms that are behind the rapid mass assembly (Mancone et al., 2010), en- hanced star formation (Brodwin et al., 2013) and increased AGN activity (Martini et al., 2013) that has been observed in galaxy clusters at high-redshift (z ≳1.4). Independently both Brodwin et al. (2013) and Ehlert et al. (2015) arrived at sim- ilar conclusions that galaxy–galaxy merging within the clusters may be the mechanisms driving galaxy evolution in galaxy clusters early in their history. In sufficiently gas-rich galaxy–galaxy merging, the gas will be dynamically disrupted triggering both enhanced star-formation activity and allows for gas to funnel into the galactic nucleus and accrete onto the central supermassive black hole creating an AGN. Similar to Ehlert et al. (2015), we aim to model the AGN incidence in galaxy clusters as tracers of galaxy evolution of the cluster members. SPT Cluster Sample Our cluster sample derives from two galaxy cluster surveys carried out by the 10-meter South Pole Telescope (SPT; Carlstrom et al., 2011) the 2500 deg2 SPT-SZ survey (Bleem et al., 2015) and the deep, 100 deg2 SPTpol 100d survey (Huang et al., 2020). Combined we have a cluster sample of over 300 clusters with a median redshift of z ∼0.69 and median cluster mass of M500 ∼3.73 × 1014M⊙. By using the SPT cluster surveys we are able to span a wide range of redshifts (see Fig 1.) and as the SPT detects clusters using the Sunyaev–Zel’dovich Effect (SZE) we have an effectively uniform-selected mass sample. Figure 1. Mass and redshift distribution of the SPT cluster surveys. IR-Bright AGN Selection Our AGN are selected using Spitzer/IRAC imaging in 3.6 µm and 4.5 µm wavelengths. We find a color selection of [3.6 µm]− [4.5 µm] ≥0.7 to be optimal in selecting AGN that would have been otherwise selected by a IRAC color-color selection (e.g., Stern et al., 2005) as shown in Figure 2. Furthermore, to account for Eddington bias in the form of photometric uncertainty we employ a fuzzy membership selection to our data in order to create the final sample as seen in Figure 3. Using our selection criteria, we select ∼2200 AGN candidates. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Photometric Redshift 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 [3.6] - [4.5] (Vega) Stern Wedge AGN Non-Active Galaxies 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 Lookback Time (Gyr) 101 103 N Figure 2. IRAC colors of all objects in the Spitzer Deep Wide-Field Survey (SDWFS; Ashby, Stern, et al., 2009) detected in 4.5 µm with SNR ≥5. Red points are objects that satisfy the AGN selection criterion as described in Stern et al. (2005). Blue hexbins are SDWFS objects that do not fall within the Stern wedge criterion. Our IRAC color selection is shown as the solid black line. 0.25 0.00 0.25 0.50 0.75 1.00 1.25 1.50 [3.6 m] - [4.5 m] (Vega) Original: = 0.89, = 0.15 Corrected: = 0.72, = 0.12 Degree of Membership = 0.56 SDWFS Number Counts Figure 3. Example of applying our Eddington bias correction and computing degrees of membership into our AGN sample. The original color and color error of the object is assumed to form a Normal distribution as given by the dashed curve which is then convolved with the deeper SDWFS number count distribution to account for Eddington bias. The object’s degree of membership into our sample is given by the integral of the corrected color (solid curve) above our cut off. Preliminary Trends Preliminary analysis on the our data sample and has found some initial interesting trends such as indications of an inverse trend of AGN richness with cluster mass at a fixed redshift as can be seen in Figure 4.