2,512 research outputs found
The Classification of Periodic Light Curves from non-survey optimized observational data through Automated Extraction of Phase-based Visual Features
We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies but achieve only 19.82% accuracy on a 30% test set, 5.56% above a random model. Noise and sampling defects present in these light curves poison these features primarily by reducing our Periodogram period match rate to fewer than 5%. We propose using an automated visual feature extraction technique by transforming the phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. We produced a set of scaled images with pixels turned either on or off based on a threshold of data points in each pixel defined as at minimum one fifth of those of the most populated pixel for each light curve. Training on the same feedforward network, we achieve 29.13% accuracy, a 13.16% improvement over a random model and we also show this technique scales with an improvement to 33.51% accuracy by increasing the number of hidden layer neurons. We concede that this improvement is not yet sufficient to allow these light curves to be used for automated classification and in conclusion we discuss a new pipeline currently being developed that simultaneously incorporates period estimation and classification. This method is inspired by approximating the manual methods employed by astronomers
Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
The unprecedented volume and rate of transient events that will be discovered
by the Large Synoptic Survey Telescope (LSST) demands that the astronomical
community update its followup paradigm. Alert-brokers -- automated software
system to sift through, characterize, annotate and prioritize events for
followup -- will be critical tools for managing alert streams in the LSST era.
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is
one such broker. In this work, we develop a machine learning pipeline to
characterize and classify variable and transient sources only using the
available multiband optical photometry. We describe three illustrative stages
of the pipeline, serving the three goals of early, intermediate and
retrospective classification of alerts. The first takes the form of variable vs
transient categorization, the second, a multi-class typing of the combined
variable and transient dataset, and the third, a purity-driven subtyping of a
transient class. While several similar algorithms have proven themselves in
simulations, we validate their performance on real observations for the first
time. We quantitatively evaluate our pipeline on sparse, unevenly sampled,
heteroskedastic data from various existing observational campaigns, and
demonstrate very competitive classification performance. We describe our
progress towards adapting the pipeline developed in this work into a real-time
broker working on live alert streams from time-domain surveys.Comment: 33 pages, 14 figures, submitted to ApJ
Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
We propose a robust principal component analysis (PCA) framework for the
exploitation of multi-band photometric measurements in large surveys. Period
search results are improved using the time series of the first principal
component due to its optimized signal-to-noise ratio.The presence of correlated
excess variations in the multivariate time series enables the detection of
weaker variability. Furthermore, the direction of the largest variance differs
for certain types of variable stars. This can be used as an efficient attribute
for classification. The application of the method to a subsample of Sloan
Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti
variables. We found also 129 new RR Lyrae variables, complementary to the
catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR
Lyrae stars towards the Galactic bulge. The sample comprises also 25
multiperiodic or Blazhko RR Lyrae stars.Comment: 23 pages, 17 figure
A Gas Giant Circumbinary Planet Transiting the F Star Primary of the Eclipsing Binary Star KIC 4862625 and the Independent Discovery and Characterization of the two transiting planets in the Kepler-47 System
We report the discovery of a transiting, gas giant circumbinary planet
orbiting the eclipsing binary KIC 4862625 and describe our independent
discovery of the two transiting planets orbiting Kepler-47 (Orosz et al. 2012).
We describe a simple and semi-automated procedure for identifying individual
transits in light curves and present our follow-up measurements of the two
circumbinary systems. For the KIC 4862625 system, the 0.52+/-0.018 RJup radius
planet revolves every ~138 days and occults the 1.47+/-0.08 MSun, 1.7 +/-0.06
RSun F8 IV primary star producing aperiodic transits of variable durations
commensurate with the configuration of the eclipsing binary star. Our best-fit
model indicates the orbit has a semi-major axis of 0.64 AU and is slightly
eccentric, e=0.1. For the Kepler-47 system, we confirm the results of Orosz et
al. (2012). Modulations in the radial velocity of KIC 4862625A are measured
both spectroscopically and photometrically, i.e. via Doppler boosting, and
produce similar results.Comment: 40 pages, 17 figure
From Data to Software to Science with the Rubin Observatory LSST
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset
will dramatically alter our understanding of the Universe, from the origins of
the Solar System to the nature of dark matter and dark energy. Much of this
research will depend on the existence of robust, tested, and scalable
algorithms, software, and services. Identifying and developing such tools ahead
of time has the potential to significantly accelerate the delivery of early
science from LSST. Developing these collaboratively, and making them broadly
available, can enable more inclusive and equitable collaboration on LSST
science.
To facilitate such opportunities, a community workshop entitled "From Data to
Software to Science with the Rubin Observatory LSST" was organized by the LSST
Interdisciplinary Network for Collaboration and Computing (LINCC) and partners,
and held at the Flatiron Institute in New York, March 28-30th 2022. The
workshop included over 50 in-person attendees invited from over 300
applications. It identified seven key software areas of need: (i) scalable
cross-matching and distributed joining of catalogs, (ii) robust photometric
redshift determination, (iii) software for determination of selection
functions, (iv) frameworks for scalable time-series analyses, (v) services for
image access and reprocessing at scale, (vi) object image access (cutouts) and
analysis at scale, and (vii) scalable job execution systems.
This white paper summarizes the discussions of this workshop. It considers
the motivating science use cases, identified cross-cutting algorithms,
software, and services, their high-level technical specifications, and the
principles of inclusive collaborations needed to develop them. We provide it as
a useful roadmap of needs, as well as to spur action and collaboration between
groups and individuals looking to develop reusable software for early LSST
science.Comment: White paper from "From Data to Software to Science with the Rubin
Observatory LSST" worksho
Search for high-amplitude δ Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
We propose a robust principal component analysis framework for the exploitation of multiband photometric measurements in large surveys. Period search results are improved using the time-series of the first principal component due to its optimized signal-to-noise ratio. The presence of correlated excess variations in the multivariate time-series enables the detection of weaker variability. Furthermore, the direction of the largest variance differs for certain types of variable stars. This can be used as an efficient attribute for classification. The application of the method to a subsample of Sloan Digital Sky Survey Stripe 82 data yielded 132 high-amplitude δ Scuti variables. We also found 129 new RR Lyrae variables, complementary to the catalogue of Sesar et al., extending the halo area mapped by Stripe 82 RR Lyrae stars towards the Galactic bulge. The sample also comprises 25 multiperiodic or Blazhko RR Lyrae star
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