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Register a new userJWST/MIRI Simulated Imaging: Insights into Obscured Star-Formation and AGN for Distant Galaxies in Deep Surveys
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The JWST MIRI instrument will revolutionize extragalactic astronomy with unprecedented sensitivity and angular resolution in mid-IR. Here, we assess the potential of MIRI photometry to constrain galaxy properties in the Cosmic Evolution Early Release Science (CEERS) survey. We derive estimated MIRI fluxes from the spectral energy distributions (SEDs) of real sources that fall in a planned MIRI pointing. We also obtain MIRI fluxes for hypothetical AGN-galaxy mixed models varying the AGN fractional contribution to the total IR luminosity ($rm frac_{AGN}$). Based on these model fluxes, we simulate CEERS imaging (3.6-hour exposure) in 6 bands from F770W to F2100W using MIRISIM, and reduce these data using JWST PIPELINE. We perform PSF-matched photometry with TPHOT, and fit the source SEDs with X-CIGALE, simultaneously modeling photometric redshift and other physical properties. Adding the MIRI data, the accuracy of both redshift and $rm frac_{AGN}$ is generally improved by factors of $gtrsim 2$ for all sources at $zlesssim 3$. Notably, for pure-galaxy inputs ($rm frac_{AGN}=0$), the accuracy of $rm frac_{AGN}$ is improved by $sim 100$ times thanks to MIRI. The simulated CEERS MIRI data are slightly more sensitive to AGN detections than the deepest X-ray survey, based on the empirical $L_{rm X}$-$L_{rm 6mu m}$ relation. Like X-ray observations, MIRI can also be used to constrain the AGN accretion power (accuracy $approx 0.3$ dex). Our work demonstrates that MIRI will be able to place strong constraints on the mid-IR luminosities from star formation and AGN, and thereby facilitate studies of the galaxy/AGN co-evolution.
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While the evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations, a theoretical understanding of this complex process remains elusive. Here, we build a deep learning framework to learn this non-linear relationship, and develop techniques to physically interpret the learnt mapping. A three-dimensional convolutional neural network (CNN) is trained to predict the mass of dark matter halos from the initial conditions. We find no change in the predictive accuracy of the model if we retrain the model removing anisotropic information from the inputs. This suggests that the features learnt by the CNN are equivalent to spherical averages over the initial conditions. Our results indicate that interpretable deep learning frameworks can provide a powerful tool for extracting insight into cosmological structure formation.
Recent models of super-massive black hole (SMBH) and host galaxy joint evolution predict the presence of a key phase where accretion, traced by obscured Active Galactic Nuclei (AGN) emission, is coupled with powerful star formation. Then feedback processes likely self-regulate the SMBH growth and quench the star-formation activity. AGN in this important evolutionary phase have been revealed in the last decade via surveys at different wavelengths. On the one hand, moderate-to-deep X-ray surveys have allowed a systematic search for heavily obscured AGN, up to very high redshifts (z~5). On the other hand, infrared/optical surveys have been invaluable in offering complementary methods to select obscured AGN also in cases where the nuclear X-ray emission below 10 keV is largely hidden to our view. In this review I will present my personal perspective of the field of obscured accretion from AGN surveys.
We present a new prospective analysis of deep multi-band imaging with the James Webb Space Telescope (JWST). In this work, we investigate the recovery of high-redshift $5<z<12$ galaxies through extensive image simulations of accepted JWST programs such as CEERS in the EGS field and HUDF GTO. We introduce complete samples of $sim300,000$ galaxies with stellar masses $log(M_*/M_odot)>6$ and redshifts $0<z<15$, as well as galactic stars, into realistic mock NIRCam, MIRI and HST images to properly describe the impact of source blending. We extract the photometry of the detected sources as in real images and estimate the physical properties of galaxies through spectral energy distribution fitting. We find that the photometric redshifts are primarily limited by the availability of blue-band and near-infrared medium-band imaging. The stellar masses and star-formation rates are recovered within $0.25$ and $0.3$ dex respectively, for galaxies with accurate photometric redshifts. Brown dwarfs contaminating the $z>5$ galaxy samples can be reduced to $<0.01$ arcmin$^{-2}$ with a limited impact on galaxy completeness. We investigate multiple high-redshift galaxy selection techniques and find the best compromise between completeness and purity at $5<z<10$ using the full redshift posterior probability distributions. In the EGS field, the galaxy completeness remains higher than $50%$ for $m_text{UV}<27.5$ sources at all redshifts, and the purity is maintained above $80$ and $60%$ at $zleq7$ and $10$ respectively. The faint-end slope of the galaxy UV luminosity function is recovered with a precision of $0.1-0.25$, and the cosmic star-formation rate density within $0.1$ dex. We argue in favor of additional observing programs covering larger areas to better constrain the bright end.
We present the star formation rates (SFRs) of a sample of 109 galaxies with X-ray selected active galactic nuclei (AGN) with moderate to high X-ray luminosities (L(2-8keV)= 10^42-10^45 erg/s), at redshifts 1 < z < 4.7, that were selected to be faint or undetected in the Herschel bands. We combine our deep ALMA continuum observations with deblended 8-500{mu}m photometry from Spitzer and Herschel, and use infrared (IR) SED fitting and AGN - star formation decomposition methods. The addition of the ALMA photometry results in an order of magnitude more X-ray AGN in our sample with a measured SFR (now 37%). The remaining 63% of the sources have SFR upper limits that are typically a factor of 2-10 times lower than the pre-ALMA constraints. With the improved constraints on the IR SEDs, we can now identify a mid-IR (MIR) AGN component in 50% of our sample, compared to only ~1% previously. We further explore the F870{mu}m/F24{mu}m-redshift plane as a tool for the identification of MIR emitting AGN, for three different samples representing AGN dominated, star formation dominated, and composite sources. We demonstrate that the F870{mu}m/F24{mu}m-redshift plane can successfully split between AGN and star formation dominated sources, and can be used as an AGN identification method.
The bulk of the stellar growth over cosmic time is dominated by IR luminous galaxies at cosmic noon (z=1-2), many of which harbor a hidden active galactic nucleus (AGN). We use state of the art infrared color diagnostics, combining Spitzer and Herschel observations, to separate dust-obscured AGN from dusty star forming galaxies (SFGs) in the CANDELS and COSMOS surveys. We calculate 24 micron counts of SFGs, AGN/star forming Composites, and AGN. AGN and Composites dominate the counts above 0.8 mJy at 24 micron, and Composites form at least 25% of an IR sample even to faint detection limits. We develop methods to use the Mid-Infrared Instrument (MIRI) on JWST to identify dust-obscured AGN and Composite galaxies from z~1-2. With the sensitivity and spacing of MIRI filters, we will detect >4 times as many AGN hosts than with Spitzer/IRAC criteria. Any star formation rates based on the 7.7 micron PAH feature (likely to be applied to MIRI photometry) must be corrected for the contribution of the AGN, or the SFR will be overestimated by ~35% for cases where the AGN provides half the IR luminosity and ~50% when the AGN accounts for 90% of the luminosity. Finally, we demonstrate that our MIRI color technique can select AGN with an Eddington ratio of $lambda_{rm Edd}sim0.01$ and will identify AGN hosts with a higher sSFR than X-ray techniques alone. JWST/MIRI will enable critical steps forward in identifying and understanding dust-obscured AGN and the link to their host galaxies.
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