Mountain‐Facilitated Lee‐Slope Transport and Daytime Boundary Layer Mixing of Volcano Plumes Exacerbates Air Pollution Over Arequipa, Peru
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We study the multi-phase circumgalactic medium (CGM) of emission line galaxies (ELGs) at $z\sim1.5$, traced by MgII$\lambda2796$, $\lambda2803$ and CIV$\lambda1548$, $\lambda1550$ absorption lines, using approximately 7,000 ELG-quasar pairs from the Dark Energy Spectroscopic Instrument. Our results show that both the mean rest equivalent width ($W_{0}$) profiles and covering fractions of MgII and CIV increase with ELG stellar mass at similar impact parameters, but show similar distributions when normalized by the virial radius. Moreover, warm CIV gas has a more extended distribution than cool MgII gas. The dispersion of MgII and CIV gas velocity offsets relative to the galaxy redshifts rises from $\sim100 \, \rm km \, s^{-1}$ within halos to $\sim 200 \, \rm km \, s^{-1}$ beyond. We explore the relationships between MgII and CIV $W_{0}$ and show that the two are not tightly coupled: at a fixed absorption strength of one species, the other varies by several-fold, indicating distinct kinematics between the gas phases traced by each. We measure the line ratios, FeII/MgII and CIV/MgII, of strong MgII absorbers and find that at $<0.2$ virial radius, the FeII/MgII ratio is elevated, while the CIV/MgII ratio is suppressed compared with the measurements on larger scales, both with $\sim4-5\, Ï$ significance. We argue that multiphase gas that is not co-spatial is required to explain the observational results. Finally, by combining with measurements from the literature, we investigate the redshift evolution of CGM properties and estimate the neutral hydrogen, metal, and dust masses in the CGM of DESI ELGs -- found to be comparable to those in the ISM.
We investigate the properties of the cool circumgalactic medium (CGM) of massive galaxies and their cosmic evolution. By using the year 1 dataset of luminous red galaxies (LRGs) and QSOs from the Dark Energy Spectroscopic Instrument survey, we construct a sample of approximately 600,000 galaxy-quasar pairs and measure the radial distribution and kinematics of the cool gas traced by Mg II absorption lines as a function of galaxy properties from redshift 0.4 to redshift 1.2. Our results show that the covering fraction of the cool gas around LRGs increases with redshift, following a trend similar to the global evolution of galaxy star formation rate. At small radii (< 0.3rvir), the covering fraction anti-correlates with stellar mass, suggesting that mass-dependent processes suppress the cool gas content in the inner region. In addition, we measure the gas dispersion by modeling the velocity distribution of absorbers with a narrow and a broad components -- sigma_n ~ 160 and sigma_b ~ 380 km/s -- and quantify their relative contributions. The results show that the broad component becomes more prominent in the outer region, and its relative importance in the central region grows with increasing stellar mass. Finally, we discuss possible origins of the cool gas around massive galaxies, including the contribution of satellite galaxies and the precipitation scenario.
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We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.