4,633 research outputs found
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints
Accurate estimation of parameters is paramount in developing high-fidelity
models for complex dynamical systems. Model-based optimal experiment design
(OED) approaches enable systematic design of dynamic experiments to generate
input-output data sets with high information content for parameter estimation.
Standard OED approaches however face two challenges: (i) experiment design
under incomplete system information due to unknown true parameters, which
usually requires many iterations of OED; (ii) incapability of systematically
accounting for the inherent uncertainties of complex systems, which can lead to
diminished effectiveness of the designed optimal excitation signal as well as
violation of system constraints. This paper presents a robust OED approach for
nonlinear systems with arbitrarily-shaped time-invariant probabilistic
uncertainties. Polynomial chaos is used for efficient uncertainty propagation.
The distinct feature of the robust OED approach is the inclusion of chance
constraints to ensure constraint satisfaction in a stochastic setting. The
presented approach is demonstrated by optimal experimental design for the
JAK-STAT5 signaling pathway that regulates various cellular processes in a
biological cell.Comment: Submitted to ADCHEM 201
The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory
We describe the near real-time transient-source discovery engine for the
intermediate Palomar Transient Factory (iPTF), currently in operations at the
Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system
the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for
PSF-matching, image subtraction, detection, photometry, and machine-learned
(ML) vetting of extracted transient candidates. We also review the performance
of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively
unconfused regions, "bogus" candidates from processing artifacts and imperfect
image subtractions outnumber real transients by ~ 10:1. This can be
considerably higher for image data with inaccurate astrometric and/or
PSF-matching solutions. Despite this occasionally high contamination rate, the
ML classifier is able to identify real transients with an efficiency (or
completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when
classifying raw candidates. All subtraction-image metrics, source features, ML
probability-based real-bogus scores, contextual metadata from other surveys,
and possible associations with known Solar System objects are stored in a
relational database for retrieval by the various science working groups. We
review our efforts in mitigating false-positives and our experience in
optimizing the overall system in response to the multitude of science projects
underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS
Rejoinder: Classifier Technology and the Illusion of Progress
Rejoinder: Classifier Technology and the Illusion of Progress
[math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000079 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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