arXiv subjects

James P. Long

Publications and source records attributed to James P. Long.

22 records · Page 2Linked to original sources

arXiv2014

Kernel Density Estimation with Berkson Error

Given a sample $\{X_i\}_{i=1}^n$ from $f_X$, we construct kernel density estimators for $f_Y$, the convolution of $f_X$ with a known error density $f_ε$. This problem is known as density estimation with Berkson error and has applications in epidemiology and astronomy. Little is understood about bandwidth selection for Berkson density estimation. We compare three approaches to selecting the bandwidth both asymptotically, using large sample approximations to the MISE, and at finite samples, using simulations. Our results highlight the relationship between the structure of the error $f_ε$ and the optimal bandwidth. In particular, the results demonstrate the importance of smoothing when the error term $f_ε$ is concentrated near 0. We propose a data--driven bandwidth estimator and test its performance on NO$_2$ exposure data.

stat.ME↗

arXiv2013

Electronic Hybridization of Large-Area Stacked Graphene Films

Direct, tunable coupling between individually assembled graphene layers is a next step towards designer two-dimensional (2D) crystal systems, with relevance for fundamental studies and technological applications. Here we describe the fabrication and characterization of large-area (> cm^2), coupled bilayer graphene on SiO2/Si substrates. Stacking two graphene films leads to direct electronic interactions between layers, where the resulting film properties are determined by the local twist angle. Polycrystalline bilayer films have a "stained-glass window" appearance explained by the emergence of a narrow absorption band in the visible spectrum that depends on twist angle. Direct measurement of layer orientation via electron diffraction, together with Raman and optical spectroscopy, confirms the persistence of clean interfaces over large areas. Finally, we demonstrate that interlayer coupling can be reversibly turned off through chemical modification, enabling optical-based chemical detection schemes. Together, these results suggest that individual 2D crystals can be individually assembled to form electronically coupled systems suitable for large-scale applications.

cond-mat.mtrl-sci↗

arXiv2012

Optimizing Automated Classification of Periodic Variable Stars in New Synoptic Surveys

Efficient and automated classification of periodic variable stars is becoming increasingly important as the scale of astronomical surveys grows. Several recent papers have used methods from machine learning and statistics to construct classifiers on databases of labeled, multi--epoch sources with the intention of using these classifiers to automatically infer the classes of unlabeled sources from new surveys. However, the same source observed with two different synoptic surveys will generally yield different derived metrics (features) from the light curve. Since such features are used in classifiers, this survey-dependent mismatch in feature space will typically lead to degraded classifier performance. In this paper we show how and why feature distributions change using OGLE and \textit{Hipparcos} light curves. To overcome survey systematics, we apply a method, \textit{noisification}, which attempts to empirically match distributions of features between the labeled sources used to construct the classifier and the unlabeled sources we wish to classify. Results from simulated and real--world light curves show that noisification can significantly improve classifier performance. In a three--class problem using light curves from \textit{Hipparcos} and OGLE, noisification reduces the classifier error rate from 27.0% to 7.0%. We recommend that noisification be used for upcoming surveys such as Gaia and LSST and describe some of the promises and challenges of applying noisification to these surveys.

astro-ph.IM↗

arXiv2011

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because a) standard assumptions for machine-learned model selection procedures break down and b) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting (IW), co-training (CT), and active learning (AL). We argue that AL---where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up---is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and OGLE, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply active learning to classify variable stars in the ASAS survey, finding dramatic improvement in our agreement with the ACVS catalog, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

astro-ph.IM↗