Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network

Spiral galaxies can be classified into the {\it Grand-designs} and {\it Flocculents} based on the nature of their spiral arms. The {\it Grand-designs} exhibit almost continuous and high contrast spiral arms and are believed to be driven by density waves, while the {\it Flocculents} have patchy and low-contrast spiral features and are primarily stochastic in origin. We train a convolutional neural network (CNN) model to classify spirals into {\it Grand-designs} and {\it Flocculents}, with a testing accuracy of $\mathrm{97.2\%}$. We then use the above model for classifying $\mathrm{1,354}$ new spirals from the SDSS. Out of these, $\mathrm{721}$ were identified as {\it Flocculents}, and the rest as {\it Grand-designs}. We find the median asymptotic rotational velocities of our newly classified {\it Grand-designs} and {\it Flocculents} are $218 \pm 86$ and $145 \pm 67$ respectively, indicating that the {\it Grand-designs} are mostly the high-mass and the {\it Flocculents} the intermediate-mass spirals. This is further corroborated by the observation that the median morphological indices of the {\it Grand-designs} and {\it Flocculents} are $2.6 \pm 1.8$ and $4.7 \pm 1.9$ respectively, implying that the {\it Flocculents} primarily consist of a late-type galaxy population in contrast to the {\it Grand-designs}. Finally, an almost equal fraction of of bars $\sim$ 0.3 in both the classes of spiral galaxies reveals that the presence of a bar component does not regulate the type of spiral arm hosted by a galaxy. Our results may have important implications for formation and evolution of spiral arms in galaxies.Comment: 19 pages, 8 figures (Accepted for publication in the MNRAS

A Methodology for Successful University Graduate CubeSat Programs

The University of Colorado Smead Department of Aerospace Engineering has over a decade of success in designing, building, and operating student led CubeSat missions. The experience and lessons learned from building and operating the CSSWE, MinXSS-1, MinXSS-2, and QB50-Challenger missions have helped grow a knowledge base on the most effective and efficient ways to manage some of the “tall poles” when it comes to student run CubeSat missions. Among these “tall poles” we have seen student turnover, software, and documentation become some of the hardest to knock-down and we present our strategies for doing so. We use the MAXWELL mission (expected to launch in 2021) as a road-map to detail the methodology we have built over the last decade to ensure the greatest chance of mission success

Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using Convolutional Neural Networks

ABSTRACT Constructing dynamical models for interacting galaxies constrained by their observed structure and kinematics crucially depends on the correct choice of the values of their relative inclination (i) and viewing angle (θ) (the angle between the line of sight and the normal to the plane of their orbital motion). We construct Deep Convolutional Neural Network (DCNN) models to determine the i and θ of interacting galaxy pairs, using N-body + smoothed particle hydrodynamics (SPH) simulation data from the GalMer data base for training. GalMer simulates only a discrete set of i values (0°, 45°, 75°, and 90°) and almost all possible values of θ values in the range, [−90°, 90°]. Therefore, we have used classification for i parameter and regression for θ. In order to classify galaxy pairs based on their i values only, we first construct DCNN models for (i) 2-class (i  = 0 °, 45°) (ii) 3-class (i = 0°, 45°, 90°) classification, obtaining F1 scores of 99 per cent and 98 per cent respectively. Further, for a classification based on both i and θ values, we develop a DCNN model for a 9-class classification using different possible combinations of i and θ, and the F1 score was 97${{\ \rm per\ cent}}$. To estimate θ alone, we have used regression, and obtained a mean-squared error value of 0.12. Finally, we also tested our DCNN model on real data from Sloan Digital Sky Survey. Our DCNN models could be extended to determine additional dynamical parameters, currently determined by trial and error method.</jats:p

Two loop QCD amplitudes for di-pseudo scalar production in gluon fusion

Abstract We compute the radiative corrections to the four-point amplitude g+g → A+A in massless Quantum Chromodynamics (QCD) up to order $${\alpha}_s^4$$ α s 4 in perturbation theory. We used the effective field theory that describes the coupling of pseudo-scalars to gluons and quarks directly, in the large top quark mass limit. Due to the CP odd nature of the pseudo-scalar Higgs boson, the computation involves careful treatment of chiral quantities in dimensional regularisation. The ultraviolet finite results are shown to be consistent with the universal infrared structure of QCD amplitudes. The infrared finite part of these amplitudes constitutes the important component of any next to next to leading order corrections to observables involving pair of pseudo-scalars at the Large Hadron Collider.</jats:p