43 research outputs found
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