14,532 research outputs found
Methods for Detection and Correction of Sudden Pixel Sensitivity Drops
PDC 8.0 includes implementation of a new algorithm to detect and correct step discontinuities appearing in roughly one of every twenty stellar light curves during a given quarter. An example of such a discontinuity in an actual light curve is shown in fig. 1. The majority of such discontinuities are believed to result from high-energy particles (either cosmic or solar in origin) striking the photometer and causing permanent local changes (typically -0.5% in summed apertures) in quantum efficiency, though a partial exponential recovery is often observed. Since these features, dubbed sudden pixel sensitivity dropouts (SPSDs), are uncorrelated across targets they cannot be properly accounted for by the current detrending algorithm. PDC de-trending is based on the assumption that features in flux time series are due either to intrinsic stellar phenomena or to systematic errors and that systematics will exhibit measurable correlations across targets. SPSD events violate these assumptions and their successful removal not only rectifies the flux values of affected targets, but demonstrably improves the overall performance of PDC de-trending
Six Noise Type Military Sound Classifier
Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives
Detection and Removal of Artifacts in Astronomical Images
Astronomical images from optical photometric surveys are typically
contaminated with transient artifacts such as cosmic rays, satellite trails and
scattered light. We have developed and tested an algorithm that removes these
artifacts using a deep, artifact free, static sky coadd image built up through
the median combination of point spread function (PSF) homogenized, overlapping
single epoch images. Transient artifacts are detected and masked in each single
epoch image through comparison with an artifact free, PSF-matched simulated
image that is constructed using the PSF-corrected, model fitting catalog from
the artifact free coadd image together with the position variable PSF model of
the single epoch image. This approach works well not only for cleaning single
epoch images with worse seeing than the PSF homogenized coadd, but also the
traditionally much more challenging problem of cleaning single epoch images
with better seeing. In addition to masking transient artifacts, we have
developed an interpolation approach that uses the local PSF and performs well
in removing artifacts whose widths are smaller than the PSF full width at half
maximum, including cosmic rays, the peaks of saturated stars and bleed trails.
We have tested this algorithm on Dark Energy Survey Science Verification data
and present performance metrics. More generally, our algorithm can be applied
to any survey which images the same part of the sky multiple times.Comment: 17 pages, 6 figures. Accepted for publication in Astronomy and
Computin
Iterative Random Forests to detect predictive and stable high-order interactions
Genomics has revolutionized biology, enabling the interrogation of whole
transcriptomes, genome-wide binding sites for proteins, and many other
molecular processes. However, individual genomic assays measure elements that
interact in vivo as components of larger molecular machines. Understanding how
these high-order interactions drive gene expression presents a substantial
statistical challenge. Building on Random Forests (RF), Random Intersection
Trees (RITs), and through extensive, biologically inspired simulations, we
developed the iterative Random Forest algorithm (iRF). iRF trains a
feature-weighted ensemble of decision trees to detect stable, high-order
interactions with same order of computational cost as RF. We demonstrate the
utility of iRF for high-order interaction discovery in two prediction problems:
enhancer activity in the early Drosophila embryo and alternative splicing of
primary transcripts in human derived cell lines. In Drosophila, among the 20
pairwise transcription factor interactions iRF identifies as stable (returned
in more than half of bootstrap replicates), 80% have been previously reported
as physical interactions. Moreover, novel third-order interactions, e.g.
between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order
relationships that are candidates for follow-up experiments. In human-derived
cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated
splicing regulation, and identified novel 5th and 6th order interactions,
indicative of multi-valent nucleosomes with specific roles in splicing
regulation. By decoupling the order of interactions from the computational cost
of identification, iRF opens new avenues of inquiry into the molecular
mechanisms underlying genome biology
PASTIS: Bayesian extrasolar planet validation. I. General framework, models, and performance
A large fraction of the smallest transiting planet candidates discovered by
the Kepler and CoRoT space missions cannot be confirmed by a dynamical
measurement of the mass using currently available observing facilities. To
establish their planetary nature, the concept of planet validation has been
advanced. This technique compares the probability of the planetary hypothesis
against that of all reasonably conceivable alternative false-positive (FP)
hypotheses. The candidate is considered as validated if the posterior
probability of the planetary hypothesis is sufficiently larger than the sum of
the probabilities of all FP scenarios. In this paper, we present PASTIS, the
Planet Analysis and Small Transit Investigation Software, a tool designed to
perform a rigorous model comparison of the hypotheses involved in the problem
of planet validation, and to fully exploit the information available in the
candidate light curves. PASTIS self-consistently models the transit light
curves and follow-up observations. Its object-oriented structure offers a large
flexibility for defining the scenarios to be compared. The performance is
explored using artificial transit light curves of planets and FPs with a
realistic error distribution obtained from a Kepler light curve. We find that
data support for the correct hypothesis is strong only when the signal is high
enough (transit signal-to-noise ratio above 50 for the planet case) and remains
inconclusive otherwise. PLATO shall provide transits with high enough
signal-to-noise ratio, but to establish the true nature of the vast majority of
Kepler and CoRoT transit candidates additional data or strong reliance on
hypotheses priors is needed.Comment: Accepted for publication in MNRAS; 23 pages, 11 figure
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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