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H. P. Singh

Publications and source records attributed to H. P. Singh.

21 records · Page 2Linked to original sources

Period-Color and Amplitude-Color Relations for RR Lyraes

We use published OGLE LMC/SMC data to present comprehensive Period-Color (PC) and Amplitude-Color (AC) relations for both fundamental and overtone stars. For fundamental mode stars, we confirm earlier work that the minimum light extinction corrected PC relation in V-I has a shallow slope but with considerable scatter (LMC: $[0.093 \pm 0.019]$ with a standard deviation about this line of 0.116, SMC: $[0.055\pm0.058]$ with a standard deviation about this line of 0.099). We note the high scatter about this line for both the LMC and SMC: either there is some source of uncertainty in extinction or some other physical parameter is responsible for this dispersion. We compare with previous results and discuss some possible causes for this scatter. In contrast, RRc overtone stars do not obey a flat PC relation at minimum light (LMC: $[0.604 \pm 0.041]$ with a standard deviation about this line of 0.109, SMC: $[0.472 \pm 0.265]$ with a standard deviation about this line of 0.091). The fact that fundamental mode RR Lyrae stars obey a flat relation at minimum light and overtone RR Lyrae stars do not is consistent with the interaction of the stellar photosphere and hydrogen ionization front. We compare these results with PC relations for fundamental and first overtone Cepheids. The fact that the PC relations change significantly as a function of phase indicates strongly that Cepheid and RR Lyrae relations can only be understood at mean light when their properties as a function of phase are determined.

astro-ph.GA↗

Empirical Study in Finite Correlation Coefficient in Two Phase Estimation

This paper proposes a class of estimators for population correlation coefficient when information about the population mean and population variance of one of the variables is not available but information about these parameters of another variable (auxiliary) is available, in two phase sampling and analyzes its properties. Optimum estimator in the class is identified with its variance formula. The estimators of the class involve unknown constants whose optimum values depend on unknown population parameters.

math.GM↗

An introduction to artificial neural networks

Artificial neural networks are algorithms which have been developed to tackle a range of computational problems. These range from modelling brain function to making predictions of time-dependent phenomena to solving hard (NP-complete) problems. In this introduction we describe a single, yet very important, type of network known as a feedforward network. This network is a mathematical model which can be trained to learn an arbitrarily complex relationship between a data and a parameter domain, so can be used to solve interpolation and classification problems. We discuss the structure, training and interpretation of these networks, and their implementation, taking the classification of stellar spectra as an example.

astro-ph↗