arXiv subjects

John F. Wu

Publications and source records attributed to John F. Wu.

44 records · Page 3Linked to original sources

arXiv2022

LADUMA: Discovery of a luminous OH megamaser at $z > 0.5$

In the local Universe, OH megamasers (OHMs) are detected almost exclusively in infrared-luminous galaxies, with a prevalence that increases with IR luminosity, suggesting that they trace gas-rich galaxy mergers. Given the proximity of the rest frequencies of OH and the hyperfine transition of neutral atomic hydrogen (HI), radio surveys to probe the cosmic evolution of HI in galaxies also offer exciting prospects for exploiting OHMs to probe the cosmic history of gas-rich mergers. Using observations for the Looking At the Distant Universe with the MeerKAT Array (LADUMA) deep HI survey, we report the first untargeted detection of an OHM at $z > 0.5$, LADUMA J033046.20$-$275518.1 (nicknamed "Nkalakatha"). The host system, WISEA J033046.26$-$275518.3, is an infrared-luminous radio galaxy whose optical redshift $z \approx 0.52$ confirms the MeerKAT emission line detection as OH at a redshift $z_{\rm OH} = 0.5225 \pm 0.0001$ rather than HI at lower redshift. The detected spectral line has 18.4$σ$ peak significance, a width of $459 \pm 59\,{\rm km\,s^{-1}}$, and an integrated luminosity of $(6.31 \pm 0.18\,{\rm [statistical]}\,\pm 0.31\,{\rm [systematic]}) \times 10^3\,L_\odot$, placing it among the most luminous OHMs known. The galaxy's far-infrared luminosity $L_{\rm FIR} = (1.576 \pm 0.013) \times 10^{12}\,L_\odot$ marks it as an ultra-luminous infrared galaxy; its ratio of OH and infrared luminosities is similar to those for lower-redshift OHMs. A comparison between optical and OH redshifts offers a slight indication of an OH outflow. This detection represents the first step towards a systematic exploitation of OHMs as a tracer of galaxy growth at high redshifts.

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arXiv2022

Extending the SAGA Survey (xSAGA) I: Satellite Radial Profiles as a Function of Host Galaxy Properties

We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA), a method for identifying low-$z$ galaxies on the basis of optical imaging, and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify $z < 0.03$ galaxies from more distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of $> 100,000$ CNN-selected low-$z$ galaxies, we identify $>20,000$ probable satellites located between 36-300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar mass range $9.5 < \log(M_\star/M_\odot) < 11$. We characterize the incompleteness and contamination for CNN-selected samples, and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36-300 kpc does not depend strongly on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations, and highlight the promise of using machine learning for extending galaxy samples of wide-area surveys.

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arXiv2021

Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine learning

Wu & Peek (2020) predict SDSS-quality spectra based on Pan-STARRS broad-band \textit{grizy} images using machine learning (ML). In this letter, we test their prediction for a unique object, UGC 2885 ("Rubin's galaxy"), the largest and most massive, isolated disk galaxy in the local Universe ($D<100$ Mpc). After obtaining the ML predicted spectrum, we compare it to all existing spectroscopic information that is comparable to an SDSS spectrum of the central region: two archival spectra, one extracted from the VIRUS-P observations of this galaxy, and a new, targeted MMT/Binospec observation. Agreement is qualitatively good, though the ML prediction prefers line ratios slightly more towards those of an active galactic nucleus (AGN), compared to archival and VIRUS-P observed values. The MMT/Binospec nuclear spectrum unequivocally shows strong emission lines except H$β$, the ratios of which are consistent with AGN activity. The ML approach to galaxy spectra may be a viable way to identify AGN supplementing NIR colors. How such a massive disk galaxy ($M^* = 10^{11}$ M$_\odot$), which uncharacteristically shows no sign of interaction or mergers, manages to fuel its central AGN remains to be investigated.

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arXiv2020

Predicting galaxy spectra from images with hybrid convolutional neural networks

Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy evolution, spectroscopic data are observationally expensive to obtain. For the first time, we are able to robustly predict galaxy spectra directly from broad-band imaging. We present a powerful new approach using a hybrid convolutional neural network with deconvolution instead of batch normalization; this hybrid CNN outperforms other models in our tests. The learned mapping between galaxy imaging and spectra will be transformative for future wide-field surveys, such as with the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope, by multiplying the scientific returns for spectroscopically-limited galaxy samples.

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arXiv2020

Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning

A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to learn a galaxy's optical morphological information in order to estimate its neutral atomic hydrogen (HI) content directly from SDSS $gri$ image cutouts. We are able to accurately predict a galaxy's logarithmic HI mass fraction, $\mathcal{M} \equiv \log(M_{\rm HI}/M_\star)$, by training a CNN on galaxies in the ALFALFA 40% sample. Using pattern recognition (PR), we remove galaxies with unreliable $\mathcal{M}$ estimates. We test CNN predictions on the ALFALFA 100%, xGASS, and NIBLES catalogs, and find that the CNN consistently outperforms previous estimators. The HI-morphology connection learned by the CNN appears to be constant in low- to intermediate-density galaxy environments, but it breaks down in the highest-density environments. We also use a visualization algorithm, Gradient-weighted Class Activation Maps (Grad-CAM), to determine which morphological features are associated with low or high gas content. These results demonstrate that CNNs are powerful tools for understanding the connections between optical morphology and other properties, as well as for probing other variables, in a quantitative and interpretable manner.

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arXiv2019

The Star-Forming Interstellar Medium of Lyman Break Galaxy Analogs

We present VLT/SINFONI near-infrared (NIR) integral field spectroscopy of six $z \sim 0.2$ Lyman break galaxy "analogs" (LBAs), from which we detect HI, HeI, and [FeII] recombination lines, and multiple H$_2$ ro-vibrational lines in emission. Pa$α$ kinematics reveal high velocity dispersions and low rotational velocities relative to random motions ($\langle v/σ\rangle = 1.2 \pm 0.8$). Matched-aperture comparisons of H$β$, H$α$, and Pa$α$ reveal that the nebular color excesses are lower relative to the continuum color excesses than is the case for typical local star-forming systems. We compare observed HeI/HI recombination line ratios to photoionization models to gauge the effective temperatures (T$_{\rm eff}$) of massive ionizing stars, finding the properties of at least one LBA are consistent with extra heating from an active galactic nucleus (AGN) and/or an overabundance of massive stars. We use H$_2$ 1-0 S($\cdot$) ro-vibrational spectra to determine rotational excitation temperature $T_{\rm ex} \sim 2000$ K for warm molecular gas, which we attribute to UV heating in dense photon-dominated regions. Spatially resolved NIR line ratios favor excitation by massive, young stars, rather than supernovae or AGN feedback. Our results suggest that the local analogs of Lyman break galaxies are primarily subject to strong feedback from recent star formation, with evidence for AGN and outflows in some cases.

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arXiv2019

Using convolutional neural networks to predict galaxy metallicity from three-color images

We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity ($Z$) of galaxies derived from spectroscopic information ($Z \equiv 12 + \log(\rm O/H)$) using only three-band $gri$ images from the Sloan Digital Sky Survey. When trained and tested on $128 \times 128$-pixel images, the root mean squared error (RMSE) of $Z_{\rm pred} - Z_{\rm true}$ is only 0.085 dex, vastly outperforming a trained random forest algorithm on the same data set (RMSE $=0.130$ dex). The amount of scatter in $Z_{\rm pred} - Z_{\rm true}$ decreases with increasing image resolution in an intuitive manner. We are able to use CNN-predicted $Z_{\rm pred}$ and independently measured stellar masses to recover a mass-metallicity relation with $0.10$ dex scatter. Because our predicted MZR shows no more scatter than the empirical MZR, the difference between $Z_{\rm pred}$ and $Z_{\rm true}$ can not be due to purely random error. This suggests that the CNN has learned a representation of the gas-phase metallicity, from the optical imaging, beyond what is accessible with oxygen spectral lines.

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arXiv2018

Herschel and ALMA Observations of Massive SZE-selected Clusters

We present new Herschel observations of four massive, Sunyaev-Zel'dovich Effect (SZE)-selected clusters at $0.3 \leq z \leq 1.1$, two of which have also been observed with ALMA. We detect 19 Herschel/PACS counterparts to spectroscopically confirmed cluster members, five of which have redshifts determined via CO($4-3$) and [CI](${}^3P_1 - {}^3P_0$) lines. The mean [CI]/CO line ratio is $0.19 \pm 0.07$ in brightness temperature units, consistent with previous results for field samples. We do not detect significant stacked ALMA dust continuum or spectral line emission, implying upper limits on mean interstellar medium (H$_2$ + HI) and molecular gas masses. An apparent anticorrelation of $L_{IR}$ with clustercentric radius is driven by the tight relation between star formation rate and stellar mass. We find average specific star formation rate log(sSFR/yr$^{-1}$) = -10.36, which is below the SFR$-M_*$ correlation measured for field galaxies at similar redshifts. The fraction of infrared-bright galaxies (IRBGs; $\log (L_{IR}/L_\odot) > 10.6$) per cluster and average sSFR rise significantly with redshift. For CO detections, we find $f_{gas} \sim 0.2$, comparable to those of field galaxies, and gas depletion timescales of about 2 Gyr. We use radio observations to distinguish active galactic nuclei (AGNs) from star-forming galaxies. At least four of our 19 Herschel cluster members have $q_{IR} < 1.8$, implying an AGN fraction $f_{AGN} \gtrsim 0.2$ for our PACS-selected sample.

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