HST Morphologies of z~2 Dust Obscured Galaxies I: Power-law Sources

We present high spatial resolution optical and near-infrared imaging obtained using the ACS, WFPC2 and NICMOS cameras aboard the Hubble Space Telescope of 31 24um--bright z~2 Dust Obscured Galaxies (DOGs) identified in the Bootes Field of the NOAO Deep Wide-Field Survey. Although this subset of DOGs have mid-IR spectral energy distributions dominated by a power-law component suggestive of an AGN, all but one of the galaxies are spatially extended and not dominated by an unresolved component at rest-frame UV or optical wavelengths. The observed V-H and I-H colors of the extended components are 0.2-3 magnitudes redder than normal star-forming galaxies. All but 1 have axial ratios >0.3, making it unlikely that DOGs are composed of an edge-on star-forming disk. We model the spatially extended component of the surface brightness distributions of the DOGs with a Sersic profile and find effective radii of 1-6 kpc. This sample of DOGs is smaller than most sub-millimeter galaxies (SMGs), but larger than quiescent high-redshift galaxies. Non-parametric measures (Gini and M20) of DOG morphologies suggest that these galaxies are more dynamically relaxed than local ULIRGs. We estimate lower limits to the stellar masses of DOGs based on the rest-frame optical photometry and find that these range from ~10^(9-11) M_sun. If major mergers are the progenitors of DOGs, then these observations suggest that DOGs may represent a post-merger evolutionary stage.Comment: 23 pages, 9 figures, 6 tables, accepted to ApJ; lower limits on stellar mass revised upwards by factor of (1+z

Morphologies of High Redshift, Dust Obscured Galaxies from Keck Laser Guide Star Adaptive Optics

Spitzer MIPS images in the Bootes field of the NOAO Deep Wide-Field Survey have revealed a class of extremely dust obscured galaxy (DOG) at z~2. The DOGs are defined by very red optical to mid-IR (observed-frame) colors, R - [24 um] > 14 mag, i.e. f_v (24 um) / f_v (R) > 1000. They are Ultra-Luminous Infrared Galaxies with L_8-1000 um > 10^12 -10^14 L_sun, but typically have very faint optical (rest-frame UV) fluxes. We imaged three DOGs with the Keck Laser Guide Star Adaptive Optics (LGSAO) system, obtaining ~0.06'' resolution in the K'-band. One system was dominated by a point source, while the other two were clearly resolved. Of the resolved sources, one can be modeled as a exponential disk system. The other is consistent with a de Vaucouleurs profile typical of elliptical galaxies. The non-parametric measures of their concentration and asymmetry, show the DOGs to be both compact and smooth. The AO images rule out double nuclei with separations of greater than 0.1'' (< 1 kpc at z=2), making it unlikely that ongoing major mergers (mass ratios of 1/3 and greater) are triggering the high IR luminosities. By contrast, high resolution images of z~2 SCUBA sources tend to show multiple components and a higher degree of asymmetry. We compare near-IR morphologies of the DOGs with a set of z=1 luminous infrared galaxies (LIRGs; L_IR ~ 10^11 L_sun) imaged with Keck LGSAO by the Center for Adaptive Optics Treasury Survey. The DOGs in our sample have significantly smaller effective radii, ~1/4 the size of the z=1 LIRGs, and tend towards higher concentrations. The small sizes and high concentrations may help explain the globally obscured rest-frame blue-to-UV emission of the DOGs.Comment: 9 pages, 7 figures, 2 tables, accepted for publication in the Astronomical Journa

A Significant Population of Very Luminous Dust-Obscured Galaxies at Redshift z ~ 2

Observations with Spitzer Space Telescope have recently revealed a significant population of high-redshift z~2 dust-obscured galaxies (DOGs) with large mid-IR to UV luminosity ratios. These galaxies have been missed in traditional optical studies of the distant universe. We present a simple method for selecting this high-z population based solely on the ratio of the observed mid-IR 24um to optical R-band flux density. In the 8.6 sq.deg Bootes NDWFS Field, we uncover ~2,600 DOG candidates (= 0.089/sq.arcmin) with 24um flux densities F24>0.3mJy and (R-[24])>14 (i.e., F[24]/F[R] > 1000). These galaxies have no counterparts in the local universe, and become a larger fraction of the population at fainter F24, representing 13% of the sources at 0.3~mJy. DOGs exhibit evidence of both star-formation and AGN activity, with the brighter 24um sources being more AGN- dominated. We have measured spectroscopic redshifts for 86 DOGs, and find a broad z distribution centered at ~2.0. Their space density is 2.82E-5 per cubic Mpc, similar to that of bright sub-mm-selected galaxies at z~2. These redshifts imply very large luminosities LIR>~1E12-14 Lsun. DOGs contribute ~45-100% of the IR luminosity density contributed by all z~2 ULIRGs, suggesting that our simple selection criterion identifies the bulk of z~2 ULIRGs. DOGs may be the progenitors of ~4L* present-day galaxies seen undergoing a luminous,short- lived phase of bulge and black hole growth. They may represent a brief evolution phase between SMGs and less obscured quasars or galaxies. [Abridged]Comment: Accepted for publication in the Astrophysical Journa

VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics to achieve improved label efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong improvements over supervised baselines with and without data augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art purely image-based data augmentation techniques and self-supervised reconstruction methods on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven data augmentations. Our code is available at https://github.com/ad12/meddlr.Comment: Accepted to MIDL 202

Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Global Analysis of RNA Secondary Structure in Two Metazoans

The secondary structure of RNA is necessary for its maturation, regulation, processing, and function. However, the global influence of RNA folding in eukaryotes is still unclear. Here, we use a high-throughput, sequencing-based, structure-mapping approach to identify the paired (double-stranded RNA [dsRNA]) and unpaired (single-stranded RNA [ssRNA]) components of the Drosophila melanogaster and Caenorhabditis elegans transcriptomes, which allows us to identify conserved features of RNA secondary structure in metazoans. From this analysis, we find that ssRNAs and dsRNAs are significantly correlated with specific epigenetic modifications. Additionally, we find key structural patterns across protein-coding transcripts that indicate that RNA folding demarcates regions of protein translation and likely affects microRNA-mediated regulation of mRNAs in animals. Finally, we identify and characterize 546 mRNAs whose folding pattern is significantly correlated between these metazoans, suggesting that their structure has some function. Overall, our findings provide a global assessment of RNA folding in animals

HST Morphologies of z ~ 2 Dust-Obscured Galaxies II: Bump Sources

We present Hubble Space Telescope (HST) imaging of 22 ultra-luminous infrared galaxies (ULIRGs) at z~2 with extremely red R-[24] colors (called dust-obscured galaxies, or DOGs) which have a local maximum in their spectral energy distribution (SED) at rest-frame 1.6um associated with stellar emission. These sources, which we call "bump DOGs", have star-formation rates of 400-4000 Msun/yr and have redshifts derived from mid-IR spectra which show strong polycyclic aromatic hydrocarbon emission --- a sign of vigorous on-going star-formation. Using a uniform morphological analysis, we look for quantifiable differences between bump DOGs, power-law DOGs (Spitzer-selected ULIRGs with mid-IR SEDs dominated by a power-law and spectral features that are more typical of obscured active galactic nuclei than starbursts), sub-millimeter selected galaxies (SMGs), and other less-reddened ULIRGs from the Spitzer extragalactic First Look Survey (XFLS). Bump DOGs are larger than power-law DOGs (median Petrosian radius of 8.4 +/- 2.7 kpc vs. 5.5 +/- 2.3 kpc) and exhibit more diffuse and irregular morphologies (median M_20 of -1.08 +/- 0.05 vs. -1.48 +/- 0.05). These trends are qualitatively consistent with expectations from simulations of major mergers in which merging systems during the peak star-formation rate period evolve from M_20 = -1.0 to M_20 = -1.7. Less obscured ULIRGs (i.e., non-DOGs) tend to have more regular, centrally peaked, single-object morphologies rather than diffuse and irregular morphologies. This distinction in morphologies may imply that less obscured ULIRGs sample the merger near the end of the peak star-formation rate period. Alternatively, it may indicate that the intense star-formation in these less-obscured ULIRGs is not the result of a recent major merger.Comment: Accepted to ApJ; 22 pages, 8 Figures, 7 Table