BVR photometry of a newly identified RS CVn binary star HD 61396

BVR photometry of a recently identified RS CVn binary star HD61396, carried out during 2001, is presented. The new photometry reveal significant evolution in the shape and amplitude of light curve when compared with those reported earlier by Padmakar etal (2000). The traditional two-starspot model has been used to obtain the spot parameters from the observed light curve. Changes in the spot area and their location on the stellar surface are discernible from the extracted parameters from the new photometry.Comment: 9 pages including 2 figures and 2 tables. New Astronomy in pres

Continuous Selections of the Inverse Numerical Range Map

For a complex $n$-by-$n$ matrix $A$, the numerical range $F(A)$ is the range of the map $f_A(x) = x^*A x$ acting on the unit sphere in \C^n. We ask whether the multivalued inverse numerical range map $f_A^{-1}$ has a continuous single-valued selection defined on all or part of $F(A)$. We show that for a large class of matrices, $f_A^{-1}$ does have a continuous selection on $F(A)$. For other matrices, $f_A^{-1}$ has a continuous selection defined everywhere on $F(A)$ except in the vicinity of a finite number of exceptional points on the boundary of $F(A)$

Impact of photometric variability on age and mass determination of Young Stellar Objects: A case study on Orion Nebula Cluster

In case of pre-main sequence objects, the only way to determine age and mass is by fitting theoretical isochrones on color-magnitude (alternatively luminosity-temperature) diagrams. Since young stellar objects exhibit photometric variability over wide range in magnitude and colors, the age and mass determined by fitting isochrones is expected to be inaccurate, if not erroneous. These in turn will badly affect any study carried out on age spread and process of star formation. Since we have carried out very extensive photometric observations of the Orion Nebula Cluster (ONC), we decided to use our multi-band data to explore the influence of variability in determining mass and age of cluster members. In this study, we get the amplitudes of the photometric variability in V, R, and I optical bands of a sample of 346 ONC members and use it to investigate how the variability affects the inferred masses and ages and if it alone can take account for the age spread among the ONC members reported by earlier studies. We find that members that show periodic and smooth photometric rotational modulation have their masses and ages unaffected by variability. On other hand, we found that members with periodic but very scattered photometric rotational modulation and members with irregular variability have their masses and ages significantly affected. Moreover, using Hertzsprung-Russell (HR) diagrams we find that the observed I band photometric variability can take account of only a fraction (about 50%) of the inferred age spread, whereas the V band photometric variability is large enough to mask any age spread.Comment: Accepted by MNRAS; 17 pages, 4 Tables, 15 Figure

Direct Feedback Alignment with Sparse Connections for Local Learning

Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neuron's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware. In this work, we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values. Using a sparse feedback matrix, we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms. Consequently, memory and compute can be partitioned and distributed whichever way produces the most efficient forward pass so long as a single error can be delivered to each neuron. Our results show orders of magnitude improvement in data movement and $2\times$ improvement in multiply-and-accumulate operations over backpropagation. Like previous work, we observe that any variant of feedback alignment suffers significant losses in classification accuracy on deep convolutional neural networks. By transferring trained convolutional layers and training the fully connected layers using direct feedback alignment, we demonstrate that direct feedback alignment can obtain results competitive with backpropagation. Furthermore, we observe that using an extremely sparse feedback matrix, rather than a dense one, results in a small accuracy drop while yielding hardware advantages. All the code and results are available under https://github.com/bcrafton/ssdfa.Comment: 15 pages, 8 figure

Observation of R-Band Variability of L Dwarfs

We report, for the first time, photometric variability of L dwarfs in $R$ band. Out of three L1 dwarfs (2MASS 1300+19, 2MASS 1439+19, and 2MASS 1658+70) observed, we have detected R band variability in 2MASS 1300+19 and 2MASS 1439+19. The objects exhibit variability of amplitude ranging from 0.01 mag to 0.02 mag. Object 2MASS 1658+70, turns out to be non-variable in both $R$ and $I$ band. However, more observations are needed to infer its variability. No periodic behaviour in the variability is found from the two L1 dwarfs that are variable. All the three L1 dwarfs have either negligible or no $H_{\alpha}$ activity. In the absence of any direct evidence for the presence of sufficiently strong magnetic field, the detection of polarization at the optical favors the presence of dust in the atmosphere of L dwarfs. We suggest that the observed $R$ band photometric variability is most likely due to atmospheric dust activity.Comment: 13 pages (latex, aastex style) including 3 eps figures. Accepted for publication in The Astrophysical Journal Letter

Development of a scalable generic platform for adaptive optics real time control

The main objective of the present project is to explore the viability of an adaptive optics control system based exclusively on Field Programmable Gate Arrays (FPGAs), making strong use of their parallel processing capability. In an Adaptive Optics (AO) system, the generation of the Deformable Mirror (DM) control voltages from the Wavefront Sensor (WFS) measurements is usually through the multiplication of the wavefront slopes with a predetermined reconstructor matrix. The ability to access several hundred hard multipliers and memories concurrently in an FPGA allows performance far beyond that of a modern CPU or GPU for tasks with a well defined structure such as Adaptive Optics control. The target of the current project is to generate a signal for a real time wavefront correction, from the signals coming from a Wavefront Sensor, wherein the system would be flexible to accommodate all the current Wavefront Sensing techniques and also the different methods which are used for wavefront compensation. The system should also accommodate for different data transmission protocols (like Ethernet, USB, IEEE 1394 etc.) for transmitting data to and from the FPGA device, thus providing a more flexible platform for Adaptive Optics control. Preliminary simulation results for the formulation of the platform, and a design of a fully scalable slope computer is presented.Comment: Paper presented as part of SPIE ICOP 2015 Conference Proceeding