2,448 research outputs found
PhotoRaptor - Photometric Research Application To Redshifts
Due to the necessity to evaluate photo-z for a variety of huge sky survey
data sets, it seemed important to provide the astronomical community with an
instrument able to fill this gap. Besides the problem of moving massive data
sets over the network, another critical point is that a great part of
astronomical data is stored in private archives that are not fully accessible
on line. So, in order to evaluate photo-z it is needed a desktop application
that can be downloaded and used by everyone locally, i.e. on his own personal
computer or more in general within the local intranet hosted by a data center.
The name chosen for the application is PhotoRApToR, i.e. Photometric Research
Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a
machine learning algorithm and special tools dedicated to preand
post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron
trained by the Quasi Newton Algorithm), which has been revealed particularly
powerful for the photo-z calculation on the base of a spectroscopic sample
(Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013).
The PhotoRApToR program package is available, for different platforms, at the
official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).Comment: User Manual of the PhotoRaptor tool, 54 pages. arXiv admin note:
substantial text overlap with arXiv:1501.0650
GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
We present first results from the third GRavitational lEnsing Accuracy
Testing (GREAT3) challenge, the third in a sequence of challenges for testing
methods of inferring weak gravitational lensing shear distortions from
simulated galaxy images. GREAT3 was divided into experiments to test three
specific questions, and included simulated space- and ground-based data with
constant or cosmologically-varying shear fields. The simplest (control)
experiment included parametric galaxies with a realistic distribution of
signal-to-noise, size, and ellipticity, and a complex point spread function
(PSF). The other experiments tested the additional impact of realistic galaxy
morphology, multiple exposure imaging, and the uncertainty about a
spatially-varying PSF; the last two questions will be explored in Paper II. The
24 participating teams competed to estimate lensing shears to within systematic
error tolerances for upcoming Stage-IV dark energy surveys, making 1525
submissions overall. GREAT3 saw considerable variety and innovation in the
types of methods applied. Several teams now meet or exceed the targets in many
of the tests conducted (to within the statistical errors). We conclude that the
presence of realistic galaxy morphology in simulations changes shear
calibration biases by per cent for a wide range of methods. Other
effects such as truncation biases due to finite galaxy postage stamps, and the
impact of galaxy type as measured by the S\'{e}rsic index, are quantified for
the first time. Our results generalize previous studies regarding sensitivities
to galaxy size and signal-to-noise, and to PSF properties such as seeing and
defocus. Almost all methods' results support the simple model in which additive
shear biases depend linearly on PSF ellipticity.Comment: 32 pages + 15 pages of technical appendices; 28 figures; submitted to
MNRAS; latest version has minor updates in presentation of 4 figures, no
changes in content or conclusion
Sampling trade-offs in duty-cycled systems for air quality low-cost sensors
The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.This work is supported by the National Spanish funding PID2019-107910RB-I00, by regional project 2017 SGR-990, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu.Peer ReviewedPostprint (published version
Transient engine model for calibration using two-stage regression approach
Engine mapping is the process of empirically modelling engine behaviour
as a function of adjustable engine parameters, predicting the
output of the engine. The aim is to calibrate the electronic engine
controller to meet decreasing emission requirements and increasing
fuel economy demands. Modern engines have an increasing number
of control parameters that are having a dramatic impact on time and
e ort required to obtain optimal engine calibrations. These are further
complicated due to transient engine operating mode.
A new model-based transient calibration method has been built on the
application of hierarchical statistical modelling methods, and analysis
of repeated experiments for the application of engine mapping. The
methodology is based on two-stage regression approach, which organise
the engine data for the mapping process in sweeps. The introduction
of time-dependent covariates in the hierarchy of the modelling led
to the development of a new approach for the problem of transient
engine calibration.
This new approach for transient engine modelling is analysed using
a small designed data set for a throttle body inferred air
ow phenomenon.
The data collection for the model was performed on a
transient engine test bed as a part of this work, with sophisticated
software and hardware installed on it. Models and their associated
experimental design protocols have been identi ed that permits the
models capable of accurately predicting the desired response features
over the whole region of operability. Further, during the course of the work, the utility of multi-layer perceptron
(MLP) neural network based model for the multi-covariate
case has been demonstrated. The MLP neural network performs
slightly better than the radial basis function (RBF) model. The basis
of this comparison is made on assessing relevant model selection criteria,
as well as internal and external validation ts.
Finally, the general ability of the model was demonstrated through the
implementation of this methodology for use in the calibration process,
for populating the electronic engine control module lookup tables
Design and development of GrainNet - universal Internet enabled software for operation and standardization of near-infrared spectrometers
A current trend in modern near-infrared spectroscopy is the incorporation of sophisticated mathematical algorithms into the computer instrumentation used to extract information from raw spectral data by applying complex multivariate models. To address some of the problems that near-infrared spectroscopy faces, the GrainNet software model that connects a MATLABRTM computing and development environment, NIR spectrometers, and MS Server data-storage for spectral data and calibration models, was developed.;GrainNet is a client-server based Internet enabled communication and analyzing model for Near-Infrared (NIR) instruments. FOSS Infratec, Perten, and Bruins Instruments are currently three brands of the NIR instruments that have been included in the project. The performance of the implemented calibration models was evaluated. Three calibration models are implemented in the GrainNet: (1) Partial Least Squares Regression; (2) Artificial Neural Network; (3) Locally Weighted Regression.;The Piecewise Direct Standardization (PDS), Direct Standardization (DS), Finite Impulse Response (FIR) and Multiplicative Scatter Corrections (MSC) models were developed in the MATLABRTM environment and tested for standardization transfer of the Bruins Instruments and Foss Infratec grain analyzers. A new calibration model for corn that uses feed-forward back-propagation neural networks with wavelets signal decomposition used as an input was developed
Statistical data mining algorithms for optimising analysis of spectroscopic data from on-line NIR mill systems
Justin Sexton investigated techniques to identify atypical sugarcane from spectral data. He found that identifying atypical samples could help remove bias in estimates of CCS. His results can be used to track occurrences of atypical cane or improve quality estimates providing benefits at various stages along the industry value chain
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z
Photometric redshift estimation algorithms are often based on representative
data from observational campaigns. Data-driven methods of this type are subject
to a number of potential deficiencies, such as sample bias and incompleteness.
Motivated by these considerations, we propose using physically motivated
synthetic spectral energy distributions in redshift estimation. In addition,
the synthetic data would have to span a domain in colour-redshift space
concordant with that of the targeted observational surveys. With a matched
distribution and realistically modelled synthetic data in hand, a suitable
regression algorithm can be appropriately trained; we use a mixture density
network for this purpose. We also perform a zero-point re-calibration to reduce
the systematic differences between noise-free synthetic data and the
(unavoidably) noisy observational data sets. This new redshift estimation
framework, SYTH-Z, demonstrates superior accuracy over a wide range of
redshifts compared to baseline models trained on observational data alone.
Approaches using realistic synthetic data sets can therefore greatly mitigate
the reliance on expensive spectroscopic follow-up for the next generation of
photometric surveys.Comment: 14 pages, 8 figure
The application of near infrared spectroscopy to the characterisation and quality control of pharmaceutical materials of natural origin.
Natural products are of increasing interest as sources of novel pharmaceuticals and there are frequent questions as to their authenticity and purity. The use of Near Infrared (NIR) spectroscopy provides a method for rapid analysis of such materials and requires no sample preparation. Although there are many references detailing the use of NIR spectroscopy in the pharmaceutical industry, the majority of these are concerned with the analysis of pharmaceutically active drugs or pharmaceutical excipients. The aims of this PhD were to demonstrate the potential of NIR spectroscopy as a tool in the quality control of a variety of natural materials. The emphasis, therefore, was on studying a range of different products in both a qualitative and quantitative manner. Qualitative methods included the identification and qualification of natural products by the use of constructed spectral libraries. Statistical discrimination methods such as Correlation in Wavelength Space (CWS) and Maximum Distance in Wavelength Space (MDWS) were employed to create Library methods that allowed an unknown spectrum to be identified with, or distinguished from, spectra used to construct the Library. Quantitative methods included the quantification of certain analytes at different concentrations in certain materials by developing calibration equations using the Multiple Linear Regression (MLR) method. Methods of sampling, preparation and measurement were developed to carry out these investigations in the most appropriate manner and to overcome problems encountered with data acquisition and analysis. British and European Pharmacopoeia assays for the quantification of certain chemical constituents in essential oils such as Eucalyptus and Lemon oils have been used as standard reference methods and compared with NIR methods developed as part of this research and it has been shown that the NIR method is similar in precision and accuracy to the conventional assay methods. In addition, it has been demonstrated that NIR spectroscopy can be used to identify and distinguish between different essential oils and that the water content of Agar samples can be determined. Initial studies have also been carried out into the detection of adulteration of Rosemary oil with Eucalyptus oil and the contamination of Fennel with Hemlock. To summarise, the potential of NIR spectroscopy for the quality control of natural pharmaceuticals has been demonstrated and it is possible that this technique could replace more complicated and expensive traditional methods of analysis in the future
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