Integrating Temporal and Spectral Features of Astronomical Data Using Wavelet Analysis for Source Classification

Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features incorporate both temporal and spectral properties of the astronomical data. Classification is then performed on those abstract features. In order to demonstrate this technique, we have used gamma-ray burst (GRB) data from the NASA's Swift mission to classify GRBs into high- and low-redshift groups. Reliable selection of high-redshift GRBs is of considerable interest in astrophysics and cosmology.Comment: Accepted and Published in 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Imaging: Earth and Beyond (Washington DC, October 13-15, 2015) Conference Proceeding

Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here we introduce "machine-z", a redshift prediction algorithm and a "high-z" classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time our high-z classifier can achieve 80% recall of true high-redshift bursts, while incurring a false positive rate of 20%. With 40% false positive rate the classifier can achieve ~100% recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.Comment: Accepted to the Monthly Notices of the Royal Astronomical Society Journal (10 pages, 10 figures, and 3 Tables

Analytical Approach for the Determination of the Luminosity Distance in a Flat Universe with Dark Energy

Recent cosmological observations indicate that the present universe is flat and dark energy dominated. In such a universe, the calculation of the luminosity distance, d_L, involve repeated numerical calculations. In this paper, it is shown that a quite efficient approximate analytical expression, having very small uncertainties, can be obtained for d_L. The analytical calculation is shown to be exceedingly efficient, as compared to the traditional numerical methods and is potentially useful for Monte-Carlo simulations involving luminosity distances.Comment: 3 pages, 4 figures, Accepted for publication in MNRA

A Proposal to Localize Fermi GBM GRBs Through Coordinated Scanning of the GBM Error Circle via Optical Telescopes

We investigate the feasibility of implementing a system that will coordinate ground-based optical telescopes to cover the Fermi GBM Error Circle (EC). The aim of the system is to localize GBM detected GRBs and facilitate multi-wavelength follow-up from space and ground. This system will optimize the observing locations in the GBM EC based on individual telescope location, Field of View (FoV) and sensitivity. The proposed system will coordinate GBM EC scanning by professional as well as amateur astronomers around the world. The results of a Monte Carlo simulation to investigate the feasibility of the project are presented.Comment: 2011 Fermi Symposium proceedings - eConf C11050

Screening High-z GRBs with BAT Prompt Emission Properties

Detecting high-z GRBs is important for constraining the GRB formation rate, and tracing the history of re-ionization and metallicity of the universe. Based on the current sample of GRBs detected by Swift with known redshifts, we investigated the relationship between red-shift, and spectral and temporal characteristics, using the BAT event-by-event data. We found red-shift trends for the peak-flux-normalized temporal width T90, the light curve variance, the peak flux, and the photon index in simple power-law fit to the BAT event data. We have constructed criteria for screening GRBs with high red-shifts. This will enable us to provide a much faster alert to the GRB community of possible high-z bursts.Comment: 4 pages, 4 figures, to be published in the proceedings of ''Gamma Ray Bursts 2007'', Santa Fe, New Mexico, November 5-