We present nearinfrared NIR spectroscopy using the MMT and Magellan infrared spectrograph for a sample of 29 massive galaxies inlineformula mmlmath overflowscrollmmlmilogmmlmimmlmspace width0.33emmmlmspacemmlmsubmmlmiMmmlmimmlmommlmommlmsubmmlmo stretchytruemmlmommlmsubmmlmiMmmlmimmlmommlmommlmsubmmlmommlmommlmn10mmlmnmmlmath inlineformula at z  0.7 with optical spectroscopy from the Large Early Galaxy Astrophysics Census LEGAC survey. Having both optical and NIR spectroscopy at this redshift allows us to measure the full suite of restoptical strong emission lines enabling the study of ionization sources and the restoptical selection of active galactic nuclei AGN as well as the measurement of dustcorrected Hbased star formation rates SFRs. We find that 11 out of 29 galaxies host AGN. We infer the nonparametric star formation histories with the spectral energy distribution fitting code Prospector and classify galaxies as starforming green valley or quiescent based on their most recent specific SFRs. We explore the connection between AGN activity and suppressed star formation and find that 89  15 of galaxies in the green valley or below host AGN while only 15  8 of galaxies above the green valley host AGN. We construct the starforming main sequence SFMS and find that the AGN host galaxies are 0.37 dex below the SFMS while galaxies without detectable AGN are consistent with being on the SFMS. However when compared to a bootstrapped massmatched sample the SFRs of our sample of AGN host galaxies are consistent with the full LEGAC sample. Based on this massmatched analysis we cannot rule out that this suppression of star formation is driven by other processes associated with the higher mass of the AGN sample. We therefore cannot link the presence of AGN activity to the quenching of star formation.
['Galaxy evolution', 'AGN host galaxies', 'Galaxy quenching', '594', '2017', '2040', 'Astrophysics - Astrophysics of Galaxies']
Active Galactic Nuclei in the Green Valley at z  0.7
{'Active Galactic Nuclei in the Green Valley at z ∼ 0.7': "CHARITY WOODRUM , 1 CHRISTINA C. WILLIAMS , 2 MARCIA RIEKE , 1 KEVIN N. HAINLINE , 1 RAPHAEL E. HVIDING , 1, 3 ZHIYUAN JI , 1 ROBERT KENNICUTT , 1, 4 AND CHRISTOPHER N. A. WILLMER 1  \n<!-- image -->  \n1 Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  \n2 NSF's National Optical-Infrared Astronomy Research Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719, USA  \n3 Max-Planck-Institut fur Astronomie, Konigstuhl 17, D-69117 Heidelberg, Germany  \n4 Department of Physics and Astronomy and George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A&M University, 4242 TAMU, College Station, TX 77843-4242, USA", 'ABSTRACT': 'We present NIR spectroscopy using MMT/MMIRS for a sample of twenty-nine massive galaxies ( log M ∗ / M ⊙ ≳ 10 ) at z ∼ 0 . 7 with optical spectroscopy from the LEGA-C survey. Having both optical and NIR spectroscopy at this redshift allows us to measure the full suite of rest-optical strong emission lines, enabling the study of ionization sources and the rest-optical selection of active galactic nuclei (AGN), as well as the measurement of dust-corrected H α -based SFRs. We find that eleven out of twenty-nine galaxies host AGN. We infer the nonparametric star formation histories with the SED fitting code Prospector and classify galaxies as star-forming, green valley, or quiescent based on their most recent sSFRs. We explore the connection between AGN activity and suppressed star formation and find that 89 ± 15% of galaxies in the green valley or below host AGN, while only 15% ± 8% of galaxies above the green valley host AGN. We construct the star-forming main sequence (SFMS) and find that the AGN host galaxies are 0.37 dex below the SFMS while galaxies without detectable AGN are consistent with being on the SFMS. However, when compared to a bootstrapped mass-matched sample, the SFRs of our sample of AGN host galaxies are consistent with the full LEGA-C sample. Based on this mass-matched analysis, we cannot rule out that this suppression of star formation is driven by other processes associated with the higher mass of the AGN sample. We therefore cannot link the presence of AGN activity to the quenching of star formation.  \nKeywords: Galaxy evolution (594); AGN host galaxies (2017); Galaxy quenching (2040)', '1. INTRODUCTION': "One of the most outstanding open questions in galaxy evolution is identifying the physical processes that drive the quenching of star formation in massive galaxies. A critical finding is the existence of the galaxy bimodality: the majority of galaxies are either blue, lower mass, and star-forming, or are red, massive galaxies that no longer form stars (Strateva et al. 2001; Baldry et al. 2004, 2006; Bell et al. 2004; Willmer et al. 2006; Franzetti et al. 2007). The star-forming population follows a well-defined correlation between the star formation rate (SFR) and stellar mass ( M ∗ ), known as the star forming main sequence (SFMS; Daddi et al. 2007; Noeske et al. 2007; Salim et al. 2007; Karim et al. 2011; Rodighiero et al. 2011; Whitaker et al. 2012, 2014; Speagle et al. 2014; Renzini & Peng 2015; Schreiber et al. 2015; Tomczak et al. 2016; Leslie et al. 2020; Leja et al. 2022). Between the blue cloud and the red sequence lies a transitional region, often called the green valley (Bell et al. 2004; Faber et al. 2007; Martin et al. 2007; Schiminovich et al. 2007; Wyder et al.  \n2007; Mendez et al. 2011; Gonc¸alves et al. 2012). Due to its sparse population, it has been suggested that galaxies rapidly ( ≈ 10 8 yr; e.g. Wild et al. 2009; Barro et al. 2013; Moutard et al. 2016; Wu et al. 2018; Belli et al. 2019; Wild et al. 2020) transition through this region, making it a short-lived phase in galactic evolution. Studying galaxies in the green valley, and any physical processes that preferentially occur there, can provide critical constraints on galaxy quenching (e.g., Strateva et al. 2001; Bell et al. 2004; Faber et al. 2007).  \nOne hypothesized process for quenching involves supermassive black holes (SMBHs), which are now understood to co-evolve and grow in mass alongside the mass growth of their host galaxy. Active galactic nucleus (AGN) activity can induce feedback such as winds, jets, and radiation (for reviews, see Alexander & Hickox 2012; Fabian 2012). AGN feedback is thought to possibly quench galaxies through many different mechanisms, such as heating the gas or removing gas and dust from the host galaxy (e.g., Silk & Rees 1998; Springel et al. 2005). Also, low-power AGN may in-  \n<!-- image -->  \nject turbulence, which could potentially stabilize the gas disk from fragmentation and prevent star formation (Alatalo et al. 2015). However, simulations show that these mechanisms can also occur as a result of many other processes in the evolution of a galaxy unrelated to AGN activity, such as virial shock heating (Birnboim & Dekel 2003; Kereˇs et al. 2005; Dekel & Birnboim 2006), stellar feedback (Hopkins et al. 2010; Grudi'c et al. 2019), morphological quenching (Martig et al. 2009; Gensior et al. 2020), and cosmological starvation (Feldmann & Mayer 2015). Many empirical studies have attempted to place constraints on these processes (e.g. Tremonti et al. 2007; Williams et al. 2015; Diamond-Stanic et al. 2012; Williams et al. 2017; Forster Schreiber et al. 2019; Spilker et al. 2020; Williams et al. 2021; Lammers et al. 2022) and have also found evidence for environmental quenching (Peng et al. 2010, 2015). Modern galaxy formation simulations require incorporating feedback into the galaxy formation process to match observational properties of galaxy populations, such as the mass function and colors of quiescent galaxies. At the most massive end, AGN are thought to be the source of this feedback and without it, both cosmological simulations and semi-analytic models produce too many massive blue galaxies (Hatton et al. 2003; Croton et al. 2006; Gabor et al. 2011). However, the feedback implementations used in these simulations and models vary substantially between each other yet produce relatively similar galaxy populations (Naab & Ostriker 2017). Even though there is strong theoretical evidence for AGN quenching, it has been challenging to causally link AGN to quenching empirically. Therefore, it is vital to place observational constraints on the impact of AGN on their host galaxies.  \nAGN can be identified across the entire electromagnetic spectrum. For example, the narrow-line regions surrounding AGN are traced by nebular emission lines at rest-frame optical wavelengths. Some of the most widely used AGN selection techniques are diagnostics called the Baldwin, Phillips, and Terlevich diagrams (BPT; Baldwin et al. 1981), which are used to determine the dominant source of ionization in galaxies. This method is one of the most sensitive AGN selection techniques, as it is able to probe to lower accretion rates than other techniques that use X-ray emission or IR colors (Juneau et al. 2013). However, BPT-derived optical selection traditionally requires spectroscopic coverage of [O III] and H β , as well as the H α , [N II], or [S II] emission lines, which have longer wavelengths and are redshifted out of the visible spectrum at z ≳ 0 . 5 . Consequently, large statistical studies using BPT diagrams have been limited to low redshift, such as with the Sloan Digital Sky Survey (SDSS). To look for observational evidence of AGN quenching, we need to study galaxies at higher redshifts while they are in the process of quenching. A solution to this problem is follow-up observations with NIR spectrographs. Sky emission is very  \nbright in this part of the electromagnetic spectrum, making NIR observations challenging from ground-based facilities. Therefore, completing the full suite of diagnostics at higher redshifts currently requires significant investments from both optical and NIR spectrographs, such as with the MOSFIRE DEEP Evolution Survey (MOSDEF; Kriek et al. 2015) and the Keck Baryonic Structure Survey with MOSFIRE (KBSSMOSFIRE; Rudie et al. 2012; Steidel et al. 2014; Strom et al. 2017).  \nBy studying populations of galaxies at different epochs, it has been shown that massive galaxies form rapidly at z > 2 (e.g., Thomas et al. 2010; McDermid et al. 2015) and most are fully assembled and quenched by z ≈ 1 (Renzini 2006). We can also explore the individual evolutionary pathways of galaxies by utilizing the archaeological record of their stellar populations (e.g., Thomas et al. 1999; Renzini 2006; Graves et al. 2009; Trager & Somerville 2009; Webb et al. 2020; Tacchella et al. 2022; Woodrum et al. 2022). However, as the stellar populations age, these star formation history (SFH) signatures fade. It is therefore necessary to study the SFHs of galaxies closer to the peak epoch of quenching at z ≈ 1 to provide constraints on their quenching mechanisms.  \nTraditionally, parametric models, such as the commonly used delayedτ model, have been used to infer the detailed SFHs of galaxies using stellar population modeling. Quenching events may cause sharp transitions in SFR(t), which cannot be captured by these models. A solution to this problem is to infer nonparametric SFHs, which use a highly flexible functional form. Nonparametric models have been shown to match the complexity of galaxy SFHs, such as sharp bursts of star formation or quenching events (Leja et al. 2019; Lower et al. 2020; Johnson et al. 2021b). To infer accurate SFHs of galaxies, both high-quality spectra and photometry are needed to break the SFH-dust-metallicity degeneracy (Tacchella et al. 2022). Studies with such high quality data have often been limited to the local universe, e.g. SDSS. The Large Early Galaxy Astrophysics Census (LEGA-C) Survey (van der Wel et al. 2016; Straatman et al. 2018; van der Wel et al. 2021) has produced similar quality data for galaxies at 0 . 6 < z < 1 with high-signal-to-noise spectroscopy, enabling the estimation of the properties of galaxies to be characterized in exquisite detail. However, due to their higher redshifts, these galaxy spectra currently lack the full suite of diagnostics available in SDSS, i.e. all of the strong emission lines in the rest-optical ( λ rest ≈ 3000 -7000 ˚ A).  \nTo complete this full suite of diagnostics, we follow up a subsample of LEGA-C galaxies to obtain deep NIR spectroscopy using the MMT and Magellan infrared spectrograph (MMIRS; McLeod et al. 2012) at the 6.5 m MMT Observatory, targeting three strong rest-frame optical emission lines, H α , [N II], and [S II]. By combining these line fluxes with [O III] and H β from LEGA-C, we are thus able to construct  \nthe [N II] BPT diagram. We infer the individual, nonparametric SFHs for twenty-nine galaxies at z ∼ 0 . 7 . We compare the SFHs of galaxies that host AGN to galaxies without detectable AGN to determine if there is a link between the presence of an AGN and the suppression of star formation. We thus explore the role AGN have on the quenching of massive galaxies at z ∼ 0 . 7 , soon after the peak epoch of quenching (e.g. Bell et al. 2004; Faber et al. 2007; Muzzin et al. 2013; Tomczak et al. 2014; Davidzon et al. 2017).  \nIn Section 2, we present our data and data reduction methods. In Section 3, we explain our data analysis methods and models. In Section 4, we explain our AGN selection methods. In Section 5, we discuss the inferred nonparametric SFHs and compare between AGN host galaxies and non-AGN galaxies. In Section 6 we compare our results to other studies in the literature. We assume a flat Λ cold dark matter ( Λ CDM) cosmology with WMAP-9 parameters, Ω m = 0 . 287 , H 0 = 69 . 3 km s -1 Mpc -1 (Hinshaw et al. 2013).", '2.1. LEGA-C and UltraVISTA': "LEGA-C (van der Wel et al. 2016; Straatman et al. 2018; van der Wel et al. 2021) is an ESO public spectroscopic survey conducted over 128 nights with VIMOS on the Very Large Telescope in the wide-area COSMOS field (Scoville et al. 2007). The survey includes ∼ 3500 K s -band selected galaxies at redshifts 0 . 6 < z < 1 . 0 with stellar masses M ∗ > 10 10 M ⊙ . The 20 hour long integrations produce continuum spectra with S/N ∼ 20 ˚ A -1 and high-resolution ( R≈ 3500 ; Straatman et al. 2018) at the observed wavelength range 6300 ˚ A ≤ λ ≤ 8800 ˚ A. We use the spectra from the third data release (DR3: van der Wel et al. 2021).  \nIn addition, we utilize the exceptional ancillary data for the LEGA-C galaxies from the UltraVISTA catalog (Muzzin et al. 2013), which is a collection of photometric data across 30 passbands from 0.2 to 24 µ m . This catalog includes UV imaging from the GALEX satellite (Martin et al. 2005), optical imaging from the Canada-France-Hawaii Telescope (CFHT) and Subaru telescope (Taniguchi et al. 2007; Capak et al. 2007), near-infrared data from the VISTA telescope (McCracken et al. 2012), and mid-infrared data from Spitzer (Sanders et al. 2007; Frayer et al. 2009). The IRAC and MIPS deblended photometry from Spitzer was measured using a source-fitting code that has been thoroughly tested for many K s selected catalogs (e.g. Labb'e et al. 2005; Wuyts et al. 2007; Marchesini et al. 2009; Williams et al. 2009; Whitaker et al. 2011; Marchesini et al. 2012; Labb'e et al. 2010, 2013). For more details, see Section 3.5 of Muzzin et al. (2013).", '2.2. Sample Selection and Follow-up at MMT': "In this work, we select a subsample of galaxies drawn from the LEGA-C survey. To complete the full suite of rest-frame diagnostics, we obtained follow-up NIR observations using the MMIRS spectrograph (McLeod et al. 2012) at MMT Observatory, targeting three strong rest-frame optical emission lines, including H α , [N II], and [S II]. For our selection, we prioritized LEGA-C galaxies for which the measured line fluxes for H β and [O III] were well-detected with S/N > 3 after continuum subtraction and were not likely to be contaminated by sky lines. We chose mask pointings that would maximize the number of these galaxies for each mask. When remaining mask space was available, we assigned slits to other LEGA-C galaxies as fillers if they had a quality flag in the catalog that indicated they were primary survey targets that can be used for scientific purposes (see Section 3.3 of Straatman et al. (2018) for more details). We obtained spectra with four masks for a total of sixty-one galaxies, thirty-five filler galaxies and twenty-six galaxies that met our primary criteria. Twenty-nine galaxies in total showed H α emission and are the sample discussed in this paper, seven of which were filler galaxies.  \nOur NIR spectroscopy is observed with MMIRS, which is an IR multi-object spectrograph with a wide field of view ( 4 ' × 7 ' ). We observed with the J grism and the zJ filter with a wavelength range of 0.94-1.51 µm and R≈ 1020 at the mid-wavelength of 1.225 µ m . We obtained spectra with a total of 4 masks, each of which was observed for ∼ 5.5-6.5 hours (combining individual exposures of 300 s), with average seeing of 0.51-1.08 '' . The slit widths are 1 '' and the slit lengths are 8 '' to be consistent with the LEGA-C observations. The orientation of the slits are shown for our observations as well as the LEGA-C observations in Appendix A.  \nTo investigate selection effects related to the local environment of the galaxies in our sample, we use the overdensity value estimates from Darvish et al. (2016). The overdensity is defined as the local number density of galaxies, which is estimated using a Voronoi tessellation technique. The mean and standard deviation of the overdensity value for the full LEGA-C sample is log(1 + δ ) = 0 . 25 + -0 . 28 . For the sample studied in this paper, the mean and standard deviation of the overdensity value is log(1 + δ ) = 0 . 03 + -0 . 31 . Therefore, our sample is not in systematically overdense environments compared to the full LEGA-C sample or the field overall. We therefore find no evidence that potential quenching mechanisms are driven by the local environmental density of the galaxies in our sample.", '2.3. Data Reduction': "The raw frames were processed through the MMIRS data reduction pipeline (Chilingarian et al. 2015). The raw data consist of science images and dark exposures of the same  \nTable 1. Prospector ParametersNOTEz spec and σ are the spectroscopic redshift and velocity dispersion from LEGA-C DR3  \nduration as the science data. All data use a non-destructive readout such that a 3 dimensional file is created where the third dimension contains the individual readouts. The dark frames are averaged on a frame-by-frame basis and then subtracted from each of the science frames. Subsequently, data rate frames are calculated by using a linear fit for each pixel which also allows to reject cosmic rays. These data rate frames are calibrated in wavelength using the OH sky lines and re-sampled to be linear in wavelengths. The linearized spectra are combined on a per-mask basis taking into account the dither pattern used during the observations, to generate the combined 2-dimensional spectrum.  \nWe extract 1D spectra by first fitting the profile along the spatial axis with a Gaussian function and then using optimal extraction (Horne 1986). The aperture size for this extraction varies from galaxy to galaxy, but has an average FWHM of 1.2 '' . The LEGA-C 1d spectra were extracted with an aperture that also varied from galaxy to galaxy, however the individual aperture sizes are not published, but they have a very similar typical size of 1 '' . The absolute flux calibration was done using the photometry from UltraVISTA, as was done for the LEGA-C spectra. To scale the MMT spectra to the photometry, we first fit the photometry alone with Prospector (e.g., Johnson et al. 2021b, see Section 3.1)  \nwhere the fit redshift is set to the spectroscopic redshift to obtain a best-fit photometric SED. We use a median filter, 100 ˚ A wide, along the wavelength range for both the MMT spectra and the best-fit photometric SED and find the ratio between the two. We use this ratio to scale the MMT spectra to the best-fit photometric SED in a wavelength-dependent way. We note that any differential wavelength effects should be negligible in our AGN selection technique, which depends on the intensity ratios of emission lines that are sufficiently close in wavelength. We also note that while the flux calibration does compensate for slit losses under the assumption that the spectrum is the same shape across the galaxy, it does not account for any spatial gradients along the slit that may vary depending on wavelength.", '3. DATA ANALYSIS': 'Weuse SED fitting to infer physical properties for our sample including M ∗ , the detailed SFHs, and dust attenuation. In addition, we fit emission lines to measure H β , [O III], [N II], H α , and [S II] fluxes. In this section we discuss the details of our analysis. The simultaneous fitting of the photometry and LEGA-C optical spectra with Prospector is described in Section 3.1. The emission line fitting of the spectra with the  \nflexible framework GELATO (Hviding 2022) is described in Section 3.2.', '3.1. Prospector': "We simultaneously fit the photometry and LEGA-C optical spectra with the Prospector inference framework. Prospector (Johnson et al. 2021b) uses the Flexible Stellar Population Synthesis code ( FSPS ) (Conroy et al. 2009) via python-FSPS (Foreman-Mackey et al. 2014). The posterior distributions are sampled using the dynamic nested sampling code dynesty (Speagle 2020).  \nWe use a similar physical model as in Woodrum et al. (2022), modified for star-forming galaxies. In brief, we employ the MIST stellar evolutionary tracks and isochrones (Choi et al. 2016; Dotter 2016) which utilizes the MESA stellar evolution package (Paxton et al. 2011, 2013, 2015, 2018). We use MILES for the stellar spectral library (Vazdekis et al. 2015; Falc'on-Barroso et al. 2011) and adopt a Chabrier IMF (Chabrier 2003). The IGM absorption is modeled after Madau (1995). For dust attenuation, we assume a flexible attenuation curve with the UV bump tied to the slope of the curve (Kriek & Conroy 2013) and a two-component dust model (Charlot & Fall 2000). For nebular emission, we use nebular marginalization, which models the emission lines with a Gaussian fit, therefore decoupling the emission lines from the SFH, see Section 5.1. Compared to our model in Woodrum et al. (2022), in this work we allowed a wider range in priors for the gas-phase metallicity and gas ionization parameter. Our dust emission model assumes energy balance, where all starlight is attenuated by dust and re-emitted in the IR (da Cunha et al. 2008). We use the Draine & Li (2007) dust emission templates, which constrains the shape of the IR SED. We include both noise and calibration models for the simultaneous fitting of both high resolution spectra and photometry. We adopt a ten-component nonparametric SFH using the continuity prior.  \nWe adopt AGN templates from Nenkova et al. (2008a) and Nenkova et al. (2008b), which are CLUMPY AGN models incorporated into FSPS . Only dust emission from the AGN's dusty torus is included in this model. The UV and optical emission from the central engine is mostly obscured by the AGNdust torus, and if any emission leaks out it is then attenuated by the galaxy dust attenuation model. CLUMPY AGN models successfully reproduce the observed MIR characteristics of AGN in the local universe (e.g., Mor et al. 2009; Honig & Kishimoto 2010), however are not the best method to identify emission from unobscured AGN (see also, Leja et al. 2018). The free parameters for our physical model are listed in Table 1.", '3.2. GELATO': 'To study the source of ionization in our sample, we fit the emission lines in the NIR and optical spectra with the flex-  \nible framework GELATO (Hviding 2022). GELATO models the stellar continuum as a combination of simple stellar populations (SSPs) from the Extended MILES stellar library (E-MILES; Vazdekis et al. 2016). The SSP models assume a Chabrier IMF and isochrones of Girardi et al. (2000) with solar alpha abundance, and span a range of metallicities and ages. We extract fluxes for H β , [O III], [N II], H α , and [S II]. Each emission line is treated as a Gaussian model and parameterized by its redshift, velocity dispersion (the standard deviation of the Gaussian), and flux. For more information, see Hviding et al. (2022). GELATO also allows emission lines to be fit with an additional broad line component. However, fitting with broad lines for our sample does not statistically improve the fit. The GELATO fits for our entire sample are shown and the fluxes are listed in Table 3 in Appendix A.  \nFigure 1. [N II] BPT diagnostic diagram showing the relation between two line ratios: log([O III] λ 5007 /Hβ ) vs. log([N II] λ 6584 /Hα ). The solid curve is from Kewley et al. (2001) and the dashed line is from Kauffmann et al. (2003). Seven galaxies do not have measurements for [O III] because the line was redshifted out of range for the LEGA-C observations and are therefore not included in the BPT diagram, however their values for log([N II] λ 6584 /Hα ) are shown in the top panel as a histogram. Based on this diagram, eleven galaxies in our sample are AGN.  \n<!-- image -->', '4. IDENTIFYING AGN': "We identify AGN activity in our sample through the BPT diagnostic diagram. In Figure 1, we show the [N II] BPT diagram, which compares the ratio of [O III] λ 5007 ˚ A to H β with [N II] λ 6583 ˚ Ato H α . We note that these line ratios have  \nbeen shown to evolve at higher redshift (e.g., Shapley et al. 2005; Juneau et al. 2014; Steidel et al. 2014; Shapley et al. 2015), however galaxies at our redshift of z ≈ 0 . 7 have interstellar medium conditions consistent with local galaxies (e.g., Kewley et al. 2013; Helton et al. 2022). Demarcation lines from Kewley et al. (2001) and Kauffmann et al. (2003) are shown as solid and dashed curves, respectively, to separate star-forming galaxies from AGN. AGN tend to lie above the Kewley et al. (2001) line, while galaxies below this line but above the Kauffmann et al. (2003) line are considered to be in an area on the BPT diagram where composite galaxies lie, meaning both star formation and AGN activity may contribute to the ionization. In this work, we consider composite galaxies to be AGN.  \nUsing this diagnostic, we find that five galaxies have line ratios consistent with AGN ionization, and four are in the composite region. Seven galaxies do not have measurements for [O III] or H β because the line was redshifted out of range for the LEGA-C observations but not redshifted into the range accessible to MMIRS, lying between the two. These galaxies are therefore not included in the BPT diagram, however their values for log([N II] λ 6584 / H α ) are shown in the top panel as a histogram; we consider two of these galaxies with log([N II]) > -0 . 2 to be either AGN or composite galaxies. Therefore, in total, our sample contains eleven AGN selected using the [N II] BPT diagram. We note that when using optical diagnostics, it is difficult to separate line emission between excitation sources if shock emission is suspected (e.g., Kewley et al. 2019). We therefore cannot rule out that the galaxies in the AGN and composite regions may be indicative of the presence of shocks instead of AGN.  \nWe match our sample of galaxies with the Chandra COSMOS Legacy Survey catalogs (Civano et al. 2016; Marchesi et al. 2016). Four galaxies in our sample were Xray detected, all of which were also identified as AGN or composite galaxies with the BPT diagram. We include the 0.5-7.0 keV rest frame luminosities ( L X , int ) in Table 2. The criterion to be classified as an X-ray AGN requires L X , int ≥ 3 × 10 42 erg s -1 (Luo et al. 2017). Three of the four X-ray detections meet this criterion.  \nIn addition, we check the IRAC colors for evidence of obscured AGN that might be missed by X-ray or BPT selection. We use the IRAC color-color diagram from Donley et al. (2012) and find that none of the galaxies in our sample meet the criteria for IR AGN. This is not surprising because some optically-selected AGN can have IR colors consistent with star-forming galaxies and IR selection is very dependent on AGN to host luminosity ratio (Hviding 2022). In addition, Leja et al. (2018) found that only 46% of AGN identified with Prospector , defined as f AGN > 0 . 1 , would be detected with typical MIR color selections. The two galaxies in  \nour sample with f AGN > 0 . 1 were also identified as AGN or composite using the BPT diagram.  \nSome AGN can produce jets which emit synchrotron radiation that can be detected at radio wavelengths. We match our sample with the radio-loud galaxy catalog in Bariˇsi'c et al. (2017). They classify radio-loud AGN by using the radio luminosity limit from Best et al. (2005). We find that one of our galaxies is included in their radio-loud AGN catalog. Based on its location in the BPT diagram, this radio-loud AGN is also a composite galaxy.  \nIn summary, all of the galaxies in our sample that were classified as AGN from radio, IR, or X-ray emission were also classified as AGN by the BPT diagram. Seven of the BPT-selected AGN do not have detectable X-ray or radio emission. Juneau et al. (2013) also found that opticallyselected AGN were not always detected using alternate methods (X-ray, IR, radio) and explained that this is likely because optical emission lines are one of the most sensitive AGN tracers. All of the results for this section are summarized in Table 2.  \nFigure 2. Comparison of the dust-corrected SFRs derived from H α fluxes with the SFRs inferred from the Prospector fits, showing good agreement with only 0.1 dex in scatter. Only galaxies without detectable AGN are shown in this comparison because AGN significantly contribute to H α flux.  \n<!-- image -->  \n/circledot", '5.1. SFR Measurements': "We aim to measure robust SFRs in order to explore the role of AGN in the quenching of massive galaxies. Many AGN indicators are also SFR indicators, making it difficult to disentangle the two. If AGN are present, they may contribute to the flux of the emission lines. Therefore, for the Prospector SED fitting, we use nebular marginalization,  \nFigure 3. The individual SFHs for all galaxies in our sample. The shaded regions show the 16th and 84th percentile range of the posterior probability while the solid lines show the median. The top row shows AGN and the bottom row shows galaxies without detectable AGN. The columns are separated by bins in redshift. The empirically determined green valley is shown as a shaded green region. The individual SFHs are color-coded based on whether galaxies are classified as star-forming (blue), transitioning (green), or quiescent (red) at the epoch of observation. Twenty-two galaxies are star-forming, six galaxies are transitioning, and one galaxy is quiescent.  \n<!-- image -->  \nwhich decouples the emission lines from the SFR, as discussed in Section 3.  \nHydrogen recombination lines are thought to be one of the most robust tracers of SFR. In particular, H α is the SFR indicator of choice, which probes the SFR over the past 10 Myr (Kennicutt & Evans 2012). However, as mentioned in Section 1, H α redshifts out of the visible spectrum at z > 0 . 5 and consequently requires NIR spectroscopy. Our NIR data cover the H α line, enabling a cross-comparison of SFR tracers between the spectrophotometric SED-modeling of the optical data while accurately correcting for dust attenuation. Tacchella et al. (2022) showed that simultaneously fitting photometry and spectroscopy is needed to break the dustage-metallicity degeneracy, and that the dust attenuation is mainly constrained by the photometry.  \nTo measure SFR H α , we first correct the H α flux for dust attenuation using the best inferred dust attenuation law parameters from our Prospector fits. We use the dustcorrected fluxes to derive SFRs using the conversion factor in Kennicutt & Evans (2012). To check if the recent SFR inferred from our SED fitting with Prospector is robust, in Figure 2 we show the SFR H α compared with the SFR SED . We only include galaxies with no detectable AGN in this comparison because AGN contribute significantly to H α flux. We calculate the scatter as the standard deviation of the perpendicular distances to the one-to-one line and find  \nthat it is 0.1 dex. Therefore, the results are in excellent agreement.  \nIn Figure 3, we show the SFHs of individual galaxies inferred with our nonparametric model, displaying a diversity of evolutionary pathways for our sample of galaxies. The top 3 panels show galaxies hosting AGN and the bottom 3 panels show galaxies without detectable AGN. The columns represent different redshift bins. The green shaded region is the transition regime, or the 'green valley,' defined by Tacchella et al. (2022) using cuts in specific SFRs (sSFR = SFR/ M ∗ ) based on the mass doubling number, which is a measure of the number of times the stellar mass would double over the age of the universe at the current sSFR. Tacchella et al. (2022) verified these cuts empirically using the SFRM ∗ plane from Leja et al. (2022). Galaxies with sSFRs below this region are classified as quiescent and galaxies with sSFRs above this region are classified as star-forming.  \nUsing this SFH classification scheme, three galaxies are quiescent, six galaxies are in the green valley, and the remaining twenty galaxies are star-forming. All of the quiescent and green valley galaxies have higher masses ( log M ∗ > 10 . 7 M ⊙ ). Next, we discuss the SFHs of AGN compared to galaxies without detectable AGN.  \nTable 2. AGN  \nNOTE-All of the galaxies in our sample with detectable AGN are included in this table. AGN that were classified from radio, IR, or X-ray emission were also classified as AGN by the BPT diagram. Column 3 indicates whether the galaxy lies above the Kewley et al. (2001) or Kauffmann et al. (2003) line in the BPT diagram, labeled as AGN or composite, respectively, see Figure 1. Galaxies labelled as AGN/Composite do not have [O III] measurements because the line was redshifted out of range for the LEGA-C observations, however their values for log([N II]/ H α ) are high enough to consider them either AGN or composite galaxies. Column 4 indicates whether the galaxy was detected in the Chandra Legacy Survey (if yes, the luminosities are listed; Civano et al. 2016; Marchesi et al. 2016). Column 5 indicates whether the galaxy was determined a radio-loud AGN (Bariˇsi'c et al. 2017). Column 6 shows the inferred AGN luminosity as a fraction of the galaxy's bolometric luminosity from our Prospector fits. Column 7 indicates whether the galaxy is classified as star-forming (SF), transitioning in the green valley (GV), or quiescent (Q) based on the cuts in sSFR from (Tacchella et al. 2022).  \nThe left panel of Figure 4 shows the SFMS for the galaxies in our sample using SFR SED , with galaxies hosting BPT AGN shown in purple and galaxies without detectable AGN shown in yellow. Kaushal et al. (2023) fit the entire LEGA-C sample using Prospector with a similar physical model as used in this work. Their results are shown as contours for the 25th, 50th, and 75th percentiles. We compare SFR SED relative to the redshift-dependent SFMS from Leja et al. (2022). We calculate the mean and standard error of the mean for log (SFR SED / SFR MS (z)) and find that for AGN host galaxies it is -0 . 37 ± 0 . 15 and for galaxies without detectable AGN it is 0 . 14 ± 0 . 07 . Therefore AGN host galaxies lie significantly below the SFMS while galaxies without detectable AGN are consistent with being on or above the SFMS.  \nThe right panel of Figure 4 shows the sSFR vs. stellar mass, as determined from the SED fitting, with the green valley at z = 0 . 75 shown as a green shaded region. As mentioned in Section 5, the majority of galaxies in our sample are star-forming (above the green valley), based on the inferred SFHs from the SED fitting with Prospector . Six galaxies are in the transition region, five of which are AGN. In addition, the three recently quenched galaxies, which lie below the green valley, are AGN. Therefore, 89 ± 15% of galaxies in the green valley or below host AGN, while 15 ± 8% of galaxies above the green valley host AGN. The uncertainties for these percentages were calculated using the binomial distribution. In Section 5.3 we discuss if this suppression of star  \nformation in AGN host galaxies is consistent with the overall trend with increasing stellar mass of the parent sample.  \nUsing the nonparametric SFHs, we calculate the amount of time spent in the green valley ( t GV ) for the five AGN, which differs significantly between galaxy and is in the range t GV ≈ 0 . 03 -2 Gyr. This is consistent with the large diversity of quenching timescales for quiescent galaxies, defined as time spent in the green valley before quiescence, found by Tacchella et al. (2022), whose definition of green valley is used in this work.", '5.3. Star Formation History Comparison as a Function of Stellar Mass': 'The number of AGN in our sample increases with M ∗ , in agreement with many other studies (Kauffmann et al. 2003; Xue et al. 2010; Aird et al. 2012; Ji et al. 2022). This is not surprising given the positive correlations between the black hole mass ( M BH ), bulge mass, and stellar mass (Kormendy & Ho 2013; Delvecchio et al. 2019). Specifically, galaxies with larger M ∗ also have larger M BH with higher absolute accretion rates (Mullaney et al. 2012; Yang et al. 2018). Therefore, it is expected that AGN are more likely to be detected in more massive galaxies. In addition, the majority of the high mass galaxies in the full LEGA-C sample are below the SFMS. As a result, it is necessary to compare SFRs as a function of M ∗ when looking for differences between AGN host galaxies and galaxies without detectable AGN to determine  \nif any SFR differences are driven by the AGN and not other physical processes associated with higher stellar masses.  \nTo accomplish this, we need to compare the star formation properties of AGN host galaxies to a mass-matched sample of non-AGN galaxies. Therefore, we compare the SFRs of our AGN host galaxy sample with a mass-matched bootstrapped sample from the Kaushal et al. (2023) catalog. We find that the mean and standard deviation of the SFR SED for our AGN host galaxies is 2 . 9 ± 9 . 1 and for the mass-matched bootstrapped sample it is 3 . 4 ± 3 . 1 . These are consistent with one another, meaning that the SFRs of our AGN host galaxies follow the same SFR trends at a given stellar mass as the full LEGA-C sample. We therefore cannot connect the lower SFRs with the current AGN activity, as it is possibly driven by other processes associated with the higher mass of the AGN sample.', '6. DISCUSSION': "In this work, we explore the connection between AGN activity and star formation. Many studies have compared the SFRs of AGN host galaxies to galaxies without detectable AGN and find varying results. We find that the green valley galaxies in our sample preferentially host AGN, consistent with other studies (Nandra et al. 2007; Martin et al. 2007; Salim et al. 2007; Schiminovich et al. 2007; Hasinger 2008; Silverman et al. 2008; Cimatti et al. 2013; Man et al. 2019; Dodd et al. 2021; Lammers et al. 2022). Compared to galaxies without detectable AGN, some studies find higher SFRs for the AGN host galaxies (Silverman et al. 2009; Rovilos et al. 2012; Santini et al. 2012; Juneau et al. 2013; Rosario et al. 2015; Magliocchetti et al. 2016), others find similar SFRs (Harrison et al. 2012; Rosario et al. 2013; Stanley et al. 2015; Xu et al. 2015), while others find lower SFRs (Salim et al. 2007; Mullaney et al. 2012; Page et al. 2012; Hardcastle et al. 2013; Barger et al. 2015; Gurkan et al. 2015; Shimizu et al. 2015; Leslie et al. 2016; Aird et al. 2019).  \nTo compare to these results, we utilize the entire LEGA-C sample from the Kaushal et al. (2023) Prospector catalog which uses a similar physical model as in this work. We select X-ray, IR, and radio AGN in the full LEGA-C sample using the same selection techniques discussed in Section 4. Figure 5 shows the distributions of the stellar masses and sSFRS for each AGN selection type. Consistent with other results, the IR-selected AGN tend to have enhanced star formation (Ellison et al. 2016; Ji et al. 2022), while X-ray AGN have a broad range of sSFRs, showing both enhanced and suppressed star formation (Juneau et al. 2013; Aird et al. 2019; Ji et al. 2022). The radio-selected AGN tend to have low sSFRs and high stellar masses.  \nThese differences in the SFRs of AGN host galaxies may be due to the AGN selection technique (Ji et al. 2022) or the type of AGN (Ellison et al. 2016; Comerford et al. 2020;  \nGeorgantopoulos et al. 2023). For example, Ellison et al. (2016) suggest that IR-selected AGN may have a higher fraction of mergers compared to the merger fraction of optically selected AGN, and these mergers are also thought to trigger episodes of star formation. Other studies have found a connection between radio-loud AGN and quiescence (Best et al. 2014; Bariˇsi'c et al. 2017). Trump et al. (2015) show that BPT-selected AGN are preferentially found in high-mass, green, moderately star-forming hosts due to 'star formation dilution,' which causes a significant bias against BPT AGN selection in lower mass star-forming galaxies. Therefore, different techniques may select for AGN and host galaxies in different stages of their co-evolutionary pathways, or they may simply be the result of observational selection bias.  \nGalaxies can reside in the green valley for two main reasons: (1) a quiescent galaxy can move from the red sequence to the green valley via the rejuvenation of star formation or (2) the galaxy is in the process of quenching. Other studies that find lower SFRs for AGN host galaxies have suggested the former scenario, where the build-up of the central bulge through inflowing gas can trigger AGN activity as well as rejuvenate star formation (Hasinger 2008; Aird et al. 2019). With our detailed individual, non-parametric SFH modeling that has been shown to capture rejuvenation events (e.g., Tacchella et al. 2022; Woodrum et al. 2022), we are able to test this scenario for the first time. We use the following criteria to define rejuvenation: galaxies that were once quiescent (their median sSFR fell below the green valley) and have risen to either the green valley or above at some time after. Weinspect the individual SFHs of the green-valley AGN host galaxies in our sample and find that they do not meet this rejuvenation criteria. We also inspect the SFHs of the rest of our sample and find that none of our sample meets this rejuvenation criteria. Therefore, there is no evidence that the galaxies in the green valley in our sample are rejuvenating and they are more likely in the process of quenching.  \nHowever, we cannot attribute this quenching to the AGN activity. In Section 5.3, we found that the SFRs of the AGN host galaxies are consistent with galaxies at similar masses from the full LEGA-C sample. Since the majority of high mass galaxies of the sample are below the SFMS, we cannot rule out that the suppression of star formation is driven by the higher mass of the AGN sample. Due to AGN variability, the timescale for when AGN are actively accreting and visible is much shorter ( ≳ 10 5 yr ; Hickox et al. 2014; Schawinski et al. 2015) than even the most rapid quenching events ( ≈ 10 8 yr ). Also, our SFR tracers probe the most recent SFR on timescales of 10 7 yrs, which is still longer than the timescale of AGN activity. Figure 5 shows that different empirical AGN selections are identifying evidence of AGN with overlapping distributions across the entire sSFR range of the full LEGA-C sample. This means we see evi-  \n0  \n<!-- image -->  \n∗  \n/circledot  \n∗  \n/circledot  \nFigure 4. The left panel shows the SFR determined from the SED-fitting vs. the stellar mass for the purple and yellow circles. The contours show the 25th, 50th and 75th percentiles from the full LEGA-C sample fit with a similar Prospector model in Kaushal et al. (2023). The SFMS from Leja et al. (2022) is shown as a blue dashed lines, with 1.5 × above and below the SFMS shown as a shaded region. The right panel shows the sSFR determined from the SED fitting vs. the stellar mass. The green valley, as discussed in Section 5, is shown as a green shaded region.Figure 5. The distributions of the stellar masses and sSFRs inferred for the entire LEGA-C sample (Kaushal et al. 2023), with columns showing the X-ray AGN, the radio AGN (Bariˇsi'c et al. 2017), the IR AGN, the BPT AGN, and the galaxies in our NIR sample that were not classified as AGN on the BPT diagram. The green valley, at z=0.75, is shown as a green shaded region.  \n<!-- image -->  \ndence for current AGN activity in galaxies above, through, and below the SFMS. This makes it difficult to determine if the instantaneous AGN activity influences the large-scale star formation activity in galaxies. However, there is growing  \nevidence in recent simulations and observational studies that the integrated history of AGN feedback, on Gyr timescales, rather than instantaneous AGN feedback suppresses star formation (Lammers et al. 2022; Piotrowska et al. 2022; Baker et al. 2023; Bluck et al. 2023). Bluck et al. (2023) suggest that AGN release energy over long periods, which prevents gas from cooling and accreting from the CGM into massive galaxies, leading to the quenching of star formation due to the unavailability of fuel. This scenario would explain the difficulty of connecting instantaneous AGN activity with the suppression of star formation in observational studies.", '7. SUMMARY AND CONCLUSIONS': "We have followed up a subset of LEGA-C galaxies with NIR observations at MMT Observatory, allowing us to select AGN with the [N II] BPT diagram at higher redshift ( z ∼ 0 . 7 ) to look for observational evidence of AGN quenching during the epoch of cosmic star formation decline. We study the nonparametric SFHs of AGN hosts and compare them to star-forming galaxies to check for observational evidence of AGN quenching. Our main findings are as follows:  \n- · The SFRs inferred from our SED fitting with Prospector are in excellent agreement with the SFRs measured from the dust-corrected H α line fluxes, showing only 0.1 dex in scatter.\n- · 89 ± 15% of galaxies in the green valley or below host AGN, based on sSFRs inferred from SED fitting with Prospector .\n- · The AGN host galaxies are 0 . 37 ± 0 . 15 dex below the SFMS while the galaxies without detectable AGN are consistent with being on or above the SFMS. However, when comparing to a bootstrapped mass-matched sample, the SFRs of the AGN host galaxies are consistent with the same SFR trends at a given stellar mass with the full LEGA-C sample. Therefore, we cannot rule out that this suppression of star formation is driven by the higher mass of the AGN sample.  \nWe therefore show that despite having high quality data and state-of-the-art SFH modeling, it remains difficult to connect the suppression of star formation with current AGN activity. This conclusion should guide the design of future experiments with mass-complete distributions and similar quality data at intermediate redshifts of BPT-selected AGN. This is currently difficult to obtain as it requires large time investments on telescopes in both optical and NIR spectrographs. However, in the near future, surveys using instruments such as the Multi-Object Optical and Near-infrared Spectrograph (MOONS) on the European Southern Observatory (ESO) Very Large Telescope (VLT; Taylor et al. 2018), the spectroscopic galaxy survey with the Dark Energy Spectroscopic Instrument (DESI; DESI Collaboration et al. 2016), the galaxy evolution survey with the Prime Focus Spectrograph (PFS; Greene et al. 2022), and the wide field redshift surveys planned with Euclid (Euclid Collaboration et al. 2022) will enable large samples with the complete rest-optical emission line diagnostics used in this work. This will enable future studies to further investigate the connection between AGN activity and suppressed star formation with larger sample sizes during the peak epoch of quenching.  \nObservations reported here were obtained at the MMT Observatory, a joint facility of the University of Arizona and the Smithsonian Institution. We thank Sean Moran and Igor Chilingarian for help with the data reduction pipeline and Joannah Hinz for help with the mask design.  \nBased on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 194A.2005 (The LEGA-C Public Spectroscopy Survey). CW and REH are supported by the National Science Foundation through the Graduate Research Fellowship Program funded by Grant Award No. DGE-1746060. CCW and MR were supported by the National Aeronautics and Space Administration (NASA) Contract NAS50210 to the University of Arizona.  \nThis material is based upon High Performance Computing (HPC) resources supported by the University of Arizona TRIF, UITS, and Research, Innovation, and Impact (RII) and maintained by the UArizona Research Technologies department.  \nWe respectfully acknowledge the University of Arizona is on the land and territories of Indigenous peoples. Today, Arizona is home to 22 federally recognized tribes, with Tucson being home to the O'odham and the Yaqui. Committed to diversity and inclusion, the University strives to build sustainable relationships with sovereign Native Nations and Indigenous communities through education offerings, partnerships, and community service.", 'Facilities: HST, VLT, MMT (MMIRS)': 'Software: Prospector (v1.1.0, Johnson et al. 2021b), python-fsps (v0.4.1, Johnson et al. 2021a), sedpy (v0.3.0, Johnson 2021), fsps (v3.2, Conroy et al. 2009; Conroy & Gunn 2010), astropy (v5.0.4, Astropy Collaboration et al. 2013, 2018), matplotlib (v3.5.1, Hunter 2007), dynesty (v1.2.3, Speagle 2020), scipy (v1.7.3 Virtanen et al. 2020), numpy (v1.22.3, Harris et al. 2020), corner (v2.2.1, Foreman-Mackey 2016), GELATO (v2.5.1 Hviding 2022)', 'A. ADDITIONAL FIGURES': "Figure 6. This figure shows the observational data for our sample. The GELATO spectroscopic fits are displayed separately for each galaxy, showing the H β and [O III] emission lines observed by VLT in the leftmost column and the Hα , [N II], and [S II] emission lines observed by MMT in the rightmost column. The subpanels in the rightmost column show the sky lines, obtained from the 2D spectra, as light blue lines. The spectra are plotted as a gray line, the components of the emission lines are plotted as a gray dotted line, and the overall fit is plotted in purple for AGN candidates and yellow for galaxies without detectable AGN. The center column shows the HST/ACS F814W images of each galaxy as 4 × 4 '' cutouts. The slit alignments for the optical LEGA-C observations are shown as dashed lines and the slit alignments for the NIR MMTobservations are shown as solid lines.  \n<!-- image -->  \nFigure 6 (Cont.).  \n<!-- image -->  \nFigure 6 (Cont.).  \n<!-- image -->  \nFigure 6 (Cont.).  \n<!-- image -->  \nFigure 6 (Cont.).  \n<!-- image -->  \nFigure 6 (Cont.).  \n<!-- image -->  \nMeasurements  \n.  \n3  \nable  \nT  \n<!-- image -->  \nN O T E -Columns 3-8 are flux measurements from the GELA T O fitting results in units of 10 -19 erg cm -2 s -1 ˚ A -1 . Columns 9-11 are the dust measurements from the Prospector fits and includes all components of the dust model: po wer -la w modifier to the shape of the attenuation curv e of the dif fuse dust ( n ), the birth cloud dust ( τ dust , 1 ), and the dif fuse dust screen ( τ dust , 2 ). The [S II ] line flux es are fitted completely independently . 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