The influence of the cluster environment on the star formation efficiency of 12 Virgo spiral galaxies

The influence of the environment on gas surface density and star formation efficiency of cluster spiral galaxies is investigated. We extend previous work on radial profiles by a pixel-to pixel analysis looking for asymmetries due to environmental interactions. The star formation rate is derived from GALEX UV and Spitzer total infrared data. As in field galaxies, the star formation rate for most Virgo galaxies is approximately proportional to the molecular gas mass. Except for NGC 4438, the cluster environment does not affect the star formation efficiency with respect to the molecular gas. Gas truncation is not associated with major changes in the total gas surface density distribution of the inner disk of Virgo spiral galaxies. In three galaxies, possible increases in the molecular fraction and the star formation efficiency with respect to the total gas, of factors of 1.5 to 2, are observed on the windward side of the galactic disk. A significant increase of the star formation efficiency with respect to the molecular gas content on the windward side of ram pressure-stripped galaxies is not observed. The ram-pressure stripped extraplanar gas of 3 highly inclined spiral galaxies shows a depressed star formation efficiency with respect to the total gas, and one of them (NGC 4438) shows a depressed rate even with respect to the molecular gas. The interpretation is that stripped gas loses the gravitational confinement and associated pressure of the galactic disk, and the gas flow is diverging, so the gas density decreases and the star formation rate drops. However, the stripped extraplanar gas in one highly inclined galaxy (NGC 4569) shows a normal star formation efficiency with respect to the total gas. We propose this galaxy is different because it is observed long after peak pressure, and its extraplanar gas is now in a converging flow as it resettles back into the disk.Comment: 34 pages, 24 figures, accepted for publication by A&

Personalized Pancreatic Tumor Growth Prediction via Group Learning

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method

Radio Galaxy Zoo: Cosmological Alignment of Radio Sources

We study the mutual alignment of radio sources within two surveys, FIRST and TGSS. This is done by producing two position angle catalogues containing the preferential directions of respectively $30\,059$ and $11\,674$ extended sources distributed over more than $7\,000$ and $17\,000$ square degrees. The identification of the sources in the FIRST sample was performed in advance by volunteers of the Radio Galaxy Zoo project, while for the TGSS sample it is the result of an automated process presented here. After taking into account systematic effects, marginal evidence of a local alignment on scales smaller than $2.5\deg$ is found in the FIRST sample. The probability of this happening by chance is found to be less than $2$ per cent. Further study suggests that on scales up to $1.5\deg$ the alignment is maximal. For one third of the sources, the Radio Galaxy Zoo volunteers identified an optical counterpart. Assuming a flat $\Lambda$CDM cosmology with $\Omega_m = 0.31, \Omega_\Lambda = 0.69$, we convert the maximum angular scale on which alignment is seen into a physical scale in the range $[19, 38]$ Mpc $h_{70}^{-1}$. This result supports recent evidence reported by Taylor and Jagannathan of radio jet alignment in the $1.4$ deg$^2$ ELAIS N1 field observed with the Giant Metrewave Radio Telescope. The TGSS sample is found to be too sparsely populated to manifest a similar signal

H I-deficient galaxies in intermediate-density environments

Observations show that spiral galaxies in galaxy clusters tend to have on average less neutral hydrogen (H I) than galaxies of the same type and size in the field. There is accumulating evidence that such H I-deficient galaxies are also relatively frequent in galaxy groups. An important question is that which mechanisms are responsible for the gas deficiency in galaxy groups. To gain a better understanding of how environment affects the gas content of galaxies, we identified a sample of six H I-deficient galaxies from the H I Parkes All Sky Survey (HIPASS) using H I-optical scaling relations. One of the galaxies is located in the outskirts of the Fornax cluster, four are in loose galaxy groups and one is in a galaxy triplet. We present new high-resolution H I observations with the Australia Telescope Compact Array (ATCA) of these galaxies. We discuss the possible cause of H I-deficiency in the sample based on H I observations and various multi-wavelength data. We find that the galaxies have truncated H I discs, lopsided gas distribution and some show asymmetries in their stellar discs. We conclude that both ram-pressure stripping and tidal interactions are important gas removal mechanisms in low-density environments

Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits

NGC922 – a new drop-through ring galaxy

We have found the peculiar galaxy NGC 922 to be a new drop-through ring galaxy using multiwavelength (ultraviolet–radio) imaging and spectroscopic observations. Its ‘C’-shaped morphology and tidal plume indicate a recent strong interaction with its companion which was identified with these observations. Using numerical simulations we demonstrate that the main properties of the system can be generated by a high-speed off-axis drop-through collision of a small galaxy with a larger disc system, thus making NGC 922 one of the nearest known collisional ring galaxies. While these systems are rare in the local Universe, recent deep Hubble Space Telescope images suggest they were more common in the early Universe