Environmental quenching -- where interactions with other galaxies and/or the intra-cluster medium (ICM) suppress star formation in low-mass galaxies -- has long been proposed as the primary driver to establish the red sequence for low-mass galaxies within clusters at low redshift ($z<1$). However, we still do not know whether these environmental quenching mechanisms are also active at higher redshifts in proto-cluster environments that have yet to fully virialize. In large part, this regime has remained unexplored due to observational limitations; however, the James Webb Space Telescope has recently opened a new window into the role of environmental quenching on low-mass (log(M$_{\star}$/M$_{\odot}$)$<$9.5) galaxies at cosmic noon ($2 < z < 3$). Here, we use data from the JWST UNCOVER and MegaScience programs to directly probe the role of environmental quenching on low-mass galaxies in a newly-discovered $z\approx 2.6$ overdensity. Leveraging the deep imaging and R $\sim$ 15 spectrophotometry enabled by these JWST/NIRCam data, we analyze the stellar populations and inferred star formation histories (SFHs) of 20 low-mass (8.5$<$log(M$_{\star}$/M$_{\odot}$)$\leq$9.0) quiescent galaxies in the overdense environment and compare to a similar sample of 18 such galaxies in the field. The SFHs of quiescent galaxies in the proto-cluster and field across the entire probed stellar mass regime (8.5$<$log(M$_{\star}$/M$_{\odot}$)$\leq$11.0) are indistinguishable, demonstrating that the environment at cosmic noon is not yet accelerating quenching compared to the field. This is consistent with expectations that proto-clusters at $z>2$ have yet to virialize and develop a dense enough environment to efficiently quench low-mass galaxies.
We present MACER3D (Multiscale AGN-regulated Cosmic Ecosystem Resolver in 3D), a new suite of three-dimensional hydrodynamic simulations that study active galactic nuclei (AGN) feedback on galactic scales over Gyr duration, with major enhancement in subgrid models and gas physics over its predecessor -- MACER (Massive AGN Controlled Ellipticals Resolved) which is in two dimensions (hereafter MACER2D). MACER3D resolves gas dynamics from within the Bondi radius ($\sim 25\,\mathrm{pc}$) to halo scales. Combined with black hole accretion theory, it enables an accurate calculation of AGN outputs and subsequently their large-scale feedback effects. We present results from simulating an isolated elliptical galaxy with different feedback configurations. In the fiducial model with both AGN and supernova (SN) feedback, the temporal evolution of AGN luminosity and star formation rate are strongly correlated, suggesting shared dependence on the availability of gas supply for SMBH accretion and star formation. AGN duty cycles of several percent with a single-cycle timescale of $\sim 10^2\,\mathrm{Myr}$ agree with observations, while models with only AGN or SN feedback fail to reproduce observed cycles. While all models maintain a quiescent galaxy state, fiducial AGN+SN feedback model results in higher star formation than no-SN feedback, suggesting SN feedback, when acting synergistically with AGN feedback, may positively impact star formation. Combined AGN and SN feedback enhances halo-scale metal enrichment compared to single-feedback models. The simulated X-ray properties match observations and predict transient cavities produced by cold-mode AGN winds from past burst events. The differences between the results obtained by MACER2D and MACER3D are also discussed.
We preform a systematic search for galactic-scale quasar pairs and small-scale (<3 arcsec) lenses using the SDSS DR16 quasar catalog and Gaia DR3. Candidate double quasars (both are unobscured) are identified as Gaia resolved pairs around spectroscopically confirmed SDSS quasars ($L_{\rm bol} > 10^{44.5} \ {\rm erg \ s^{-1}}$) at $0.5 < z \lesssim 4.5$. Gaia astrometric information and SDSS spectral decomposition are used to exclude foreground star superpositions, which dominate ($\gtrsim 80\%$ of) the pair sample. We identify $136$ double quasar candidates from $1120$ Gaia-resolved pairs after a magnitude and redshift cut of $G<20.5$ and $z>0.5$ (803 double quasars out of 2,497 pairs without any cuts applied) with separations of $\sim$ 0.3 - 3 arcsec, corresponding to projected physical separations of $\sim$ 3 - 30 kpc at the median redshift of the sample of $z = 1.7$. We estimate an overall double quasar (lens and physical pairs combined) fraction using this sample, corrected for pair-resolving completeness, of $5.7_{-0.3}^{+0.3} \times 10^{-4}$ (bootstrapping errors). This double quasar fraction increases toward smaller separations, consistent with earlier findings. We also find little redshift evolution of the double quasar fraction for the luminous SDSS sample, consistent with previous observations and simulation predictions. However, the observed fraction is lower than simulation predictions by $\sim$0.8 - 1.6 dex, suggesting a significant population of obscured quasar pairs are missed in our search. Future wide-area space missions targeting both unobscured and obscured quasar pairs at sub-arcsec resolutions will reveal this population of obscured quasar pairs, and extend to much lower AGN luminosities.
Growing numbers of exoplanet detections continue to reveal the diverse nature of planetary systems. Planet formation around late-type M dwarfs is of particular interest. These systems provide practical laboratories to measure exoplanet occurrence rates for M dwarfs, thus testing how the outcomes of planet formation scale with host mass, and how they compare to Sun-like stars. Here, we report the discovery of TOI-6478b, a cold ($T_{\text{eq}}=204\,$K) Neptune-like planet orbiting an M5 star ($R_\star=0.234\pm0.012\,\text{R}_\odot$, $M_\star=0.230\pm0.007\,\text{M}_\odot$, $T_{\text{eff}}=3230\pm75\,$K) which is a member of the Milky Way's thick disc. We measure a planet radius of $R_b=4.6\pm0.24\,\text{R}_\oplus$ on a $P_b=34.005019\pm0.000025\,$d orbit. Using radial velocities, we calculate an upper mass limit of $M_b\leq9.9\,\text{M}_\oplus$ ($M_b\leq0.6\,\text{M}_{\text{Nep}})$, with $3\,\sigma$ confidence. TOI-6478b is a milestone planet in the study of cold, Neptune-like worlds. Thanks to its large atmospheric scale height, it is amenable to atmospheric characterisation with facilities such as JWST, and will provide an excellent probe of atmospheric chemistry in this cold regime. It is one of very few transiting exoplanets that orbit beyond their system's ice-line whose atmospheric chemical composition can be measured. Based on our current understanding of this planet, we estimate TOI-6478b's spectroscopic features (in transmission) can be $\sim2.5\times$ as high as the widely studied planet K2-18b.
Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 910 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $\Delta \chi^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.
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Modern slitless spectroscopic surveys, such as Euclid and the Roman Space Telescope, collect vast numbers of galaxy spectra but suffer from low signal-to-noise ratios. This often leads to incorrect redshift assignments when relying on a single emission line, due to noise spikes or contamination from non-target emission lines, commonly referred to as redshift interlopers. We propose a machine learning approach to correct the impact of interlopers at the level of measured summary statistics, focusing on the power spectrum monopole and line interlopers as a proof of concept. To model interloper effects, we use halo catalogs from the Quijote simulations as proxies for galaxies, displacing a fraction of halos by the distance corresponding to the redshift offset between target and interloper galaxies. This yields contaminated catalogs with varying interloper fractions across a wide range of cosmologies from the Quijote suite. We train a neural network on the power spectrum monopole, alone or combined with the bispectrum monopole, from contaminated mocks to estimate the interloper fraction and reconstruct the cleaned power spectrum. We evaluate performance in two settings: one with fixed cosmology and another where cosmological parameters vary under broad priors. In the fixed case, the network recovers the interloper fraction and corrects the power spectrum to better than 1% accuracy. When cosmology varies, performance degrades, but adding bispectrum information significantly improves results, reducing the interloper fraction error by 40-60%. We also study the method's performance as a function of the size of the training set and find that optimal strategies depend on the correlation between target and interloper samples: bispectrum information aids performance when target and interloper galaxies are uncorrelated, while tighter priors are more effective when the two are strongly correlated.