On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

Rainfall but not selective logging affect changes in abundance of tropical forest butterfly in Sabah, Borneo

We investigated the effects of rainfall on the distribution and abundance of the satyrine butterfly Ragadia makuta in selectively logged and unlogged forest on Borneo. In 1997-98, there was a severe El Nino-Southern Oscillation (ENSO) drought, and annual surveys over a 4-y period showed that abundance of R. makuta was greatly reduced during the drought, but that populations quickly recovered after it. Monthly surveys over a 12-mo period of typical rainfall showed that high rainfall in the month preceding surveys significantly reduced butterfly abundance. Butterfly abundance and distribution did not differ between selectively logged and unlogged areas in either monthly or annual surveys and there was no difference between selectively logged and unlogged areas in the pattern of post-drought recovery. These results indicate that the abundance of R. makuta was significantly reduced both after high rainfall and during severe drought, but that these impacts were short-lived and were not affected by habitat disturbance. ENSO droughts on Borneo naturally often lead to widespread forest fires and thus impacts of ENSO events for butterflies are more likely to be due to indirect effects of habitat loss, rather than direct effects of drought on butterfly population dynamics

Effect of Maple Sugaring on Leaf Litter Decomposition in Vermont Forests

The purpose of this study was to examine if tapping sugar maple trees alters the decomposition of their leaf litter. To do this, leaf litter collection baskets were placed in tapped and untapped stands of maple trees in Proctor Maple Research Center in Underhill, Vermont. Litter was allowed to collect in the baskets throughout the fall 2016 season, and then the leaves were dried, weighed, and run through a nutrient analyzer. The nutrient analysis yielded percent nitrogen by weight, percent carbon by weight, and carbon nitrogen ratios for each sample. It was found that the leaf litter of untapped samples had significantly more nitrogen and significantly lower carbon nitrogen ratios than the leaf litter collected in the tapped stand. This indicates a likely change in the decomposition of the leaves in each stand, because nutrient ratios have been shown to alter decomposition rates for leaves. One of the implications of slowed decomposition is retarded nutrient cycling, which could lead to a reduction in available nitrogen, a limiting nutrient for sugar maples, in the forest’s soil. More research should be done to determine the origin of the difference in nutrients. Additionally, a longer-term study is necessary to monitor the decomposition rates in this forest

Modelling the spreading of wilding conifers

In many parts of the Canterbury high country, conifer seeds are spreading on the wind from exisiting plantations and shelterbelts, leading to a serious weed problem. Environment Canterbury set the task at MISG to model this spread, and thus provide a basis for prioritising control operations on a limited budget. The study group provided increased understanding of topographic and climatic factors involved in seed dispersal, and of the distribution of the resulting seed rain. In addition a simulation framework was developed for comparing the effectiveness of different control strategies

Element Abundances in a Gas-rich Galaxy at z = 5: Clues to the Early Chemical Enrichment of Galaxies

Element abundances in high-redshift quasar absorbers offer excellent probes of the chemical enrichment of distant galaxies, and can constrain models for population III and early population II stars. Recent observations indicate that the sub-damped Lyman-alpha (sub-DLA) absorbers are more metal-rich than DLA absorbers at redshifts 0$<$$z$$<$3. It has also been suggested that the DLA metallicity drops suddenly at $z$$>$4.7. However, only 3 DLAs at $z$$>$4.5 and none at $z$$>$3.5 have "dust-free" metallicity measurements of undepleted elements. We report the first quasar sub-DLA metallicity measurement at $z$$>$3.5, from detections of undepleted elements in high-resolution data for a sub-DLA at $z$=5.0. We obtain fairly robust abundances of C, O, Si, and Fe, using lines outside the Lyman-alpha forest. This absorber is metal-poor, with O/H]=-2.00$\pm$0.12, which is $\gtrsim$4$\sigma$ below the level expected from extrapolation of the trend for $z$$<$3.5 sub-DLAs. The C/O ratio is 1.8$^{+0.4}_{-0.3}$ times lower than in the Sun. More strikingly, Si/O is 3.2$^{+0.6}_{-0.5}$ times lower than in the Sun, while Si/Fe is nearly (1.2$^{+0.4}_{-0.3}$ times) solar. This absorber does not display a clear alpha/Fe enhancement. Dust depletion may have removed more Si from the gas phase than is common in the Milky Way interstellar medium, which may be expected if high-redshift supernovae form more silicate-rich dust. C/O and Si/O vary substantially between different velocity components, indicating spatial variations in dust depletion and/or early stellar nucleosynethesis (e.g., population III star initial mass function). The higher velocity gas may trace an outflow enriched by early stars.Comment: 42 pages including 9 figures, accepted for publication in Ap

Sex-ratio of Miridae (Hemiptera) taken via UV light-traps in Arkansas, USA.

We determined the sex-ratio of 1,095 plant bugs (Hemiptera: Miridae) taken from 60 individual UV light-trap samples in Clark County, Arkansas over a two year period. We found that of the 21 taxa in which a sex-ratio determination could be made, 61.9% of them (13 of 21) contained a majority (over 50%) of males. Three taxa were exclusively represented by males, while two taxa were exclusively represented by females. Although taxa dependent, our data indicate that male mirids are, in general, more frequently encountered in UV light-traps. However, contrary to the notion that sparked this study (see herein) light-trap content was not represented vastly to exclusively by male individuals as the sex-ratio of the cumulative data was 62.47% males (684) and 37.53% females (411)