168 research outputs found
3D Reconstruction of the Density Field: An SVD Approach to Weak Lensing Tomography
We present a new method for constructing three-dimensional mass maps from
gravitational lensing shear data. We solve the lensing inversion problem using
truncation of singular values (within the context of generalized least squares
estimation) without a priori assumptions about the statistical nature of the
signal. This singular value framework allows a quantitative comparison between
different filtering methods: we evaluate our method beside the previously
explored Wiener filter approaches. Our method yields near-optimal angular
resolution of the lensing reconstruction and allows cluster sized halos to be
de-blended robustly. It allows for mass reconstructions which are 2-3
orders-of-magnitude faster than the Wiener filter approach; in particular, we
estimate that an all-sky reconstruction with arcminute resolution could be
performed on a time-scale of hours. We find however that linear, non-parametric
reconstructions have a fundamental limitation in the resolution achieved in the
redshift direction.Comment: 11 pages, 6 figures. Accepted for publication in Ap
Interpolating Masked Weak Lensing Signal with Karhunen-Loeve Analysis
We explore the utility of Karhunen Loeve (KL) analysis in solving practical
problems in the analysis of gravitational shear surveys. Shear catalogs from
large-field weak lensing surveys will be subject to many systematic
limitations, notably incomplete coverage and pixel-level masking due to
foreground sources. We develop a method to use two dimensional KL eigenmodes of
shear to interpolate noisy shear measurements across masked regions. We explore
the results of this method with simulated shear catalogs, using statistics of
high-convergence regions in the resulting map. We find that the KL procedure
not only minimizes the bias due to masked regions in the field, it also reduces
spurious peak counts from shape noise by a factor of ~ 3 in the cosmologically
sensitive regime. This indicates that KL reconstructions of masked shear are
not only useful for creating robust convergence maps from masked shear
catalogs, but also offer promise of improved parameter constraints within
studies of shear peak statistics.Comment: 13 pages, 9 figures; submitted to Ap
Using Open Source Libraries in the Development of Control Systems Based on Machine Vision
The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented. © 2020, IFIP International Federation for Information Processing.Foundation for Assistance to Small Innovative Enterprises in Science and Technology, FASIEFunding. The work was performed under state contract 3170ΓC1/48564, grant from the FASIE
Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)
The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks
First-Year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Constraints on Non-Standard Cosmological Models
We use the new SNe Ia discovered by the SDSS-II Supernova Survey together
with additional supernova datasets as well as observations of the cosmic
microwave background and baryon acoustic oscillations to constrain cosmological
models. This complements the analysis presented by Kessler et al. in that we
discuss and rank a number of the most popular non-standard cosmology scenarios.
When this combined data-set is analyzed using the MLCS2k2 light-curve fitter,
we find that more exotic models for cosmic acceleration provide a better fit to
the data than the Lambda-CDM model. For example, the flat DGP model is ranked
higher by our information criteria tests than the standard model. When the
dataset is instead analyzed using the SALT-II light-curve fitter, the standard
cosmological constant model fares best. Our investigation also includes
inhomogeneous Lemaitre-Tolman-Bondi (LTB) models. While our LTB models can be
made to fit the supernova data as well as any other model, the extra parameters
they require are not supported by our information criteria analysis.Comment: ApJ in press, updated reference
First-year Sloan Digital Sky Survey-II (SDSS-II) supernova results: consistency and constraints with other intermediate-redshift datasets
We present an analysis of the luminosity distances of Type Ia Supernovae from
the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey in conjunction with
other intermediate redshift (z<0.4) cosmological measurements including
redshift-space distortions from the Two-degree Field Galaxy Redshift Survey
(2dFGRS), the Integrated Sachs-Wolfe (ISW) effect seen by the SDSS, and the
latest Baryon Acoustic Oscillation (BAO) distance scale from both the SDSS and
2dFGRS. We have analysed the SDSS-II SN data alone using a variety of
"model-independent" methods and find evidence for an accelerating universe at
>97% level from this single dataset. We find good agreement between the
supernova and BAO distance measurements, both consistent with a
Lambda-dominated CDM cosmology, as demonstrated through an analysis of the
distance duality relationship between the luminosity (d_L) and angular diameter
(d_A) distance measures. We then use these data to estimate w within this
restricted redshift range (z<0.4). Our most stringent result comes from the
combination of all our intermediate-redshift data (SDSS-II SNe, BAO, ISW and
redshift-space distortions), giving w = -0.81 +0.16 -0.18(stat) +/- 0.15(sys)
and Omega_M=0.22 +0.09 -0.08 assuming a flat universe. This value of w, and
associated errors, only change slightly if curvature is allowed to vary,
consistent with constraints from the Cosmic Microwave Background. We also
consider more limited combinations of the geometrical (SN, BAO) and dynamical
(ISW, redshift-space distortions) probes.Comment: 13 pages, 7 figures, accepted for publication in MNRA
Estimating Level of Engagement from Ocular Landmarks
E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders
The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package
The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project
LSSGalPy: Interactive Visualization of the Large-scale Environment Around Galaxies
New tools are needed to handle the growth of data in astrophysics delivered
by recent and upcoming surveys. We aim to build open-source, light, flexible,
and interactive software designed to visualize extensive three-dimensional (3D)
tabular data. Entirely written in the Python language, we have developed
interactive tools to browse and visualize the positions of galaxies in the
universe and their positions with respect to its large-scale structures (LSS).
Motivated by a previous study, we created two codes using Mollweide projection
and wedge diagram visualizations, where survey galaxies can be overplotted on
the LSS of the universe. These are interactive representations where the
visualizations can be controlled by widgets. We have released these open-source
codes that have been designed to be easily re-used and customized by the
scientific community to fulfill their needs. The codes are adaptable to other
kinds of 3D tabular data and are robust enough to handle several millions of
objects.Comment: 7 pages, 2 figures; accepted for publication in PASP Special Focus
Issue: Techniques and Methods for Astrophysical Data Visualizatio
First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters
We present measurements of the Hubble diagram for 103 Type Ia supernovae
(SNe) with redshifts 0.04 < z < 0.42, discovered during the first season (Fall
2005) of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. These data
fill in the redshift "desert" between low- and high-redshift SN Ia surveys. We
combine the SDSS-II measurements with new distance estimates for published SN
data from the ESSENCE survey, the Supernova Legacy Survey, the Hubble Space
Telescope, and a compilation of nearby SN Ia measurements. Combining the SN
Hubble diagram with measurements of Baryon Acoustic Oscillations from the SDSS
Luminous Red Galaxy sample and with CMB temperature anisotropy measurements
from WMAP, we estimate the cosmological parameters w and Omega_M, assuming a
spatially flat cosmological model (FwCDM) with constant dark energy equation of
state parameter, w. For the FwCDM model and the combined sample of 288 SNe Ia,
we find w = -0.76 +- 0.07(stat) +- 0.11(syst), Omega_M = 0.306 +- 0.019(stat)
+- 0.023(syst) using MLCS2k2 and w = -0.96 +- 0.06(stat) +- 0.12(syst), Omega_M
= 0.265 +- 0.016(stat) +- 0.025(syst) using the SALT-II fitter. We trace the
discrepancy between these results to a difference in the rest-frame UV model
combined with a different luminosity correction from color variations; these
differences mostly affect the distance estimates for the SNLS and HST
supernovae. We present detailed discussions of systematic errors for both
light-curve methods and find that they both show data-model discrepancies in
rest-frame -band. For the SALT-II approach, we also see strong evidence for
redshift-dependence of the color-luminosity parameter (beta). Restricting the
analysis to the 136 SNe Ia in the Nearby+SDSS-II samples, we find much better
agreement between the two analysis methods but with larger uncertainties.Comment: Accepted for publication by ApJ
- …