The AGN Luminosity Fraction in Merging Galaxies

Galaxy mergers are key events in galaxy evolution, often causing massive starbursts and fueling active galactic nuclei (AGN). In these highly dynamic systems, it is not yet precisely known how much starbursts and AGN respectively contribute to the total luminosity, at what interaction stages they occur, and how long they persist. Here we estimate the fraction of the bolometric infrared (IR) luminosity that can be attributed to AGN by measuring and modeling the full ultraviolet to far-infrared spectral energy distributions (SEDs) in up to 33 broad bands for 24 merging galaxies with the Code for Investigating Galaxy Emission. In addition to a sample of 12 confirmed AGN in late-stage mergers, found in the $Infrared$ $Array$ $Satellite$ Revised Bright Galaxy Sample or Faint Source Catalog, our sample includes a comparison sample of 12 galaxy mergers from the $Spitzer$ Interacting Galaxies Survey, mostly early-stage. We perform identical SED modeling of simulated mergers to validate our methods, and we supplement the SED data with mid-IR spectra of diagnostic lines obtained with $Spitzer$ InfraRed Spectrograph. The estimated AGN contributions to the IR luminosities vary from system to system from 0% up to 91% but are significantly greater in the later-stage, more luminous mergers, consistent with what is known about galaxy evolution and AGN triggering.Comment: 26 pages, 10 figure

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

The AGN contribution to the UV-FIR luminosities of interacting galaxies and its role in identifying the Main Sequence

Emission from active galactic nuclei (AGNs) is known to play an important role in the evolution of many galaxies including luminous and ultraluminous systems (U/LIRGs), as well as merging systems. However, the extent, duration, and exact effects of its influence are still imperfectly understood. To assess the impact of AGNs on interacting systems, we present a Spectral Energy Distribution (SED) analysis of a sample of 189 nearby galaxies. We gather and systematically re-reduce archival broad-band imaging mosaics from the ultraviolet to the far-infrared using data from GALEX, SDSS, 2MASS, IRAS, WISE, Spitzer and Herschel. We use spectroscopy from Spitzer/IRS to obtain fluxes from fine-structure lines that trace star formation and AGN activity. Utilizing the SED modelling and fitting tool CIGALE, we derive the physical conditions of the ISM, both in star-forming regions and in nuclear regions dominated by the AGN in these galaxies. We investigate how the star formation rates (SFRs) and the fractional AGN contributions ($f_{\rm{AGN}}$) depend on stellar mass, galaxy type, and merger stage. We find that luminous galaxies more massive than about $10^{10} \rm{M}_{*}$ are likely to deviate significantly from the conventional galaxy main-sequence relation. Interestingly, infrared AGN luminosity and stellar mass in this set of objects are much tighter than SFR and stellar mass. We find that buried AGNs may occupy a locus between bright starbursts and pure AGNs in the $f_{\rm{AGN}}$-[Ne V]/[Ne II] plane. We identify a modest correlation between $f_{\rm{AGN}}$ and mergers in their later stages.Comment: Accepted for publication in MNRAS; 24 pages, 15 figures, 3 tables (plus appendix

Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography

Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the ‘view’ (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks’ ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views

Hubble Space Telescope WFC3 Early Release Science: Emission-Line Galaxies from Infrared Grism Observations

We present grism spectra of emission-line galaxies (ELGs) from 0.6-1.6 microns from the Wide Field Camera 3 on the Hubble Space Telescope. These new infrared grism data augment previous optical Advanced Camera for Surveys G800L 0.6-0.95 micron grism data in GOODS-South from the PEARS program, extending the wavelength covereage well past the G800L red cutoff. The ERS grism field was observed at a depth of 2 orbits per grism, yielding spectra of hundreds of faint objects, a subset of which are presented here. ELGs are studied via the Ha, [OIII], and [OII] emission lines detected in the redshift ranges 0.2<z<1.4, 1.2<z<2.2 and 2.0<z<3.3 respectively in the G102 (0.8-1.1 microns; R~210) and G141 (1.1-1.6 microns; R~130) grisms. The higher spectral resolution afforded by the WFC3 grisms also reveals emission lines not detectable with the G800L grism (e.g., [SII] and [SIII] lines). From these relatively shallow observations, line luminosities, star-formation rates, and grism spectroscopic redshifts are determined for a total of 48 ELGs to m(AB)~25 mag. Seventeen GOODS-South galaxies that previously only had photometric redshifts now have new grism-spectroscopic redshifts, in some cases with large corrections to the photometric redshifts (Delta(z)~0.3-0.5). Additionally, one galaxy had no previously-measured redshift but now has a secure grism-spectroscopic redshift, for a total of 18 new GOODS-South spectroscopic redshifts. The faintest source in our sample has a magnitude m(AB)=26.9 mag. The ERS grism data also reflect the expected trend of lower specific star formation rates for the highest mass galaxies in the sample as a function of redshift, consistent with downsizing and discovered previously from large surveys. These results demonstrate the remarkable efficiency and capability of the WFC3 NIR grisms for measuring galaxy properties to faint magnitudes and redshifts to z>2.Comment: Accepted for publication in AJ. Updated to include referee comments. Updated sample using improved reduction contains 23 new galaxies (Table 1; Figures 2 & 3

Rotation of planet-harbouring stars

The rotation rate of a star has important implications for the detectability, characterisation and stability of any planets that may be orbiting it. This chapter gives a brief overview of stellar rotation before describing the methods used to measure the rotation periods of planet host stars, the factors affecting the evolution of a star's rotation rate, stellar age estimates based on rotation, and an overview of the observed trends in the rotation properties of stars with planets.Comment: 16 pages, 4 figures: Invited review to appear in 'Handbook of Exoplanets', Springer Reference Works, edited by Hans J. Deeg and Juan Antonio Belmont