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

Measurements of streaming motions of the Galactic bar with Red Clump Giants

We report a measurement of the streaming motion of the stars in the Galactic bar with the Red Clump Giants (RCGs) using the data of the Optical Gravitational Lensing Experiment II (OGLE-II). We measure the proper motion of 46,961 stars and divide RCGs into bright and faint sub-samples which on average will be closer to the near and far side of the bar, respectively. We find that the far-side RCGs (4,979 stars) have a proper motion of \Delta ~ 1.5 +- 0.11 mas yr^{-1} toward the negative l relative to the near-side RCGs (3,610 stars). This result can be explained by stars in the bar rotating around the Galactic center in the same direction as the Sun with v_b ~ 100 km s^{-1}. In the Disc Star (DS) and Red Giant (RG) samples, we do not find significant difference between bright and faint sub-samples. For those samples \Delta \~ 0.3 +- 0.14 mas yr^{-1} and ~ 0.03 +- 0.14 mas yr^{-1}, respectively. It is likely that the average proper motion of RG stars is the same as that of the Galactic center. The proper motion of DSs with respect to RGs is ~ 3.3 mas yr^{-1} toward positive l. This value is consistent with the expectations for a flat rotation curve and Solar motion with respect to local standard of rest. RGs have proper motion approzimately equal to the average of bright and faint RCGs, which implies that they are on average near the center of the bar. This pilot project demonstrates that OGLE-II data may be used to study streaming motions of stars in the Galactic bar. We intend to extend this work to all 49 OGLE-II fields in the Galactic bulge region.Comment: 7 pages, 9 figures, submitted to MNRA

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

The Menstrual Cycle and Performance Feedback Alter Gender Differences in Competitive Choices

Economic experiments have shown that in mixed gender groups women are more reluctant than men to choose tournaments when given the choice between piece rate and winner-take-all tournament style compensation. These gender difference experiments have all relied on a framework where subjects were not informed of their abilities relative to potential competitors. We replicate these findings with math and word tasks, and then show that feedback about relative performance moves high ability females towards more competitive compensation schemes, moves low ability men towards less competitive schemes such as piece rate and group pay, and removes the average gender difference in compensation choices. We also examine between and within-subjects differences in choices for females across the menstrual cycle. We find women's relative reluctance to choose tournaments comes mostly from women in the low hormone phase of their menstrual cycle. Women in the high hormone phase are substantially more willing to compete than women in the low phase, though still somewhat less willing to compete than men. There are no significant differences between the choices of any of these groups after they receive relative performance feedback.competition, tournaments, gender, hormones, menstruation, feedback