272 research outputs found
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
Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
The advent of large scale multi-epoch surveys raises the need for automated
light curve (LC) processing. This is particularly true for eclipsing binaries
(EBs), which form one of the most populated types of variable objects. The Gaia
mission, launched at the end of 2013, is expected to detect of the order of few
million EBs over a 5-year mission.
We present an automated procedure to characterize EBs based on the geometric
morphology of their LCs with two aims: first to study an ensemble of EBs on a
statistical ground without the need to model the binary system, and second to
enable the automated identification of EBs that display atypical LCs. We model
the folded LC geometry of EBs using up to two Gaussian functions for the
eclipses and a cosine function for any ellipsoidal-like variability that may be
present between the eclipses. The procedure is applied to the OGLE-III data set
of EBs in the Large Magellanic Cloud (LMC) as a proof of concept. The bayesian
information criterion is used to select the best model among models containing
various combinations of those components, as well as to estimate the
significance of the components.
Based on the two-Gaussian models, EBs with atypical LC geometries are
successfully identified in two diagrams, using the Abbe values of the original
and residual folded LCs, and the reduced . Cleaning the data set from
the atypical cases and further filtering out LCs that contain non-significant
eclipse candidates, the ensemble of EBs can be studied on a statistical ground
using the two-Gaussian model parameters. For illustration purposes, we present
the distribution of projected eccentricities as a function of orbital period
for the OGLE-III set of EBs in the LMC, as well as the distribution of their
primary versus secondary eclipse widths.Comment: 20 pages, 29 figures. Submitted to A&
RESULTS OF POSTSLAUGHTER EVALUATION OF CROSSBRED FATTENERS (ZŁOTNICKA SPOTTED X DUROC) AND PUREBRED FATTENERS (ZŁOTNICKA SPOTTED)
Experimental material consisted of 112 carcasses of crossbred fatteners (złp x dur) and 16 purebred animals (złp). The following traits were analyzed in postslaughter evaluation: carcass weight (kg), mean backfat thickness (mm), height of the longissimus dorsi muscle (mm) and lean meat percentage in the carcass (%). Based on the determined carcass weight and measurements of carcass leanness the carcasses were classified in the SEUROP system. Results of postslaughter evaluation indicate relatively low carcass leanness. In only 10% carcasses leanness exceeded 50 %, while 75% carcasses fell within the range from R to O in the EUROP classification. The breed of the sire had a highly significant effect on meatiness of fatteners. Pigs sired by Duroc boars were characterized by a significantly higher meatiness, irrespectively of sex, piggery, supplier and year of birth. Backfat thickness was significantly higher in the group of crosses sired by Złotnicka Spotted boars
Palmitoylethanolamide reduces granuloma-induced hyperalgesia by modulation of mast cell activation in rats
The aim of this study was to obtain evidences of a possible analgesic role for palmitoylethanolamide (PEA) in chronic granulomatous inflammation sustained by mast cell (MC) activation in rats at 96 hours. PEA (200-400-800 μg/mL), locally administered at time 0, reduced in a concentration-dependent manner the expression and release of NGF in comparison with saline-treated controls. PEA prevented nerve formation and sprouting, as shown by histological analysis, reduced mechanical allodynia, evaluated by Von Frey filaments, and inhibited dorsal root ganglia activation. These results were supported by the evidence that MCs in granuloma were mainly degranulated and closely localized near nerve fibres and PEA significantly reduced MC degranulation and nerves fibre formation. These findings are the first evidence that PEA, by the modulation of MC activation, controls pain perception in an animal model of chronic inflammation, suggesting its potential use for the treatment of all those painful conditions in which MC activation is an initial key step
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
Gaia Data Release 3. The first Gaia catalogue of eclipsing binary candidates
We present the first Gaia catalogue of eclipsing binary candidates released
in Gaia DR3, describe its content, provide tips for its usage, estimate its
quality, and show illustrative samples. The catalogue contains 2,184,477
sources with G magnitudes up to 20 mag. Candidate selection is based on the
results of variable object classification performed within the Gaia Data
Processing and Analysis Consortium, further filtered using eclipsing
binary-tailored criteria based on the G light curves. To find the orbital
period, a large ensemble of trial periods is first acquired using three
distinct period search methods applied to the cleaned G light curve. The G
light curve is then modelled with up-to two Gaussians and a cosine for each
trial period. The best combination of orbital period and geometric model is
finally selected using Bayesian model comparison based on the BIC. A global
ranking metric is provided to rank the quality of the chosen model between
sources. The catalogue is restricted to orbital periods larger than 0.2 days.
About 530,000 of the candidates are classified as eclipsing binaries in the
literature as well, out of ~600,000 available crossmatches, and 93% of them
have published periods compatible with the Gaia periods. Catalogue completeness
is estimated to be between 25% and 50%, depending on the sky region, relative
to the OGLE4 catalogues of eclipsing binaries towards the Galactic Bulge and
the Magellanic Clouds. The analysis of an illustrative sample of ~400,000
candidates with significant parallaxes shows properties in the observational HR
diagram as expected for eclipsing binaries. The subsequent analysis of a
sub-sample of detached bright candidates provides further hints for the
exploitation of the catalogue. The orbital periods, light curve model
parameters, and global rankings are all published in the catalogue with their
related uncertainties where applicable.Comment: Submitted to A&A. Main text: 23 pages, 35 figures. Four appendices
(17 pages) with 38 figure
Random forest automated supervised classification of Hipparcos periodic variable stars
We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V−I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency. Random forests and a multi-stage scheme involving Bayesian network and Gaussian mixture methods lead to statistically equivalent results. In standard 10-fold cross-validation (CV) experiments, the rate of correct classification is between 90 and 100 per cent, depending on the variability type. The main mis-classification cases, up to a rate of about 10 per cent, arise due to confusion between SPB and ACV blue variables and between eclipsing binaries, ellipsoidal variables and other variability types. Our training set and the predicted types for the other Hipparcos periodic stars are available onlin
Hipparcos Variable Star Detection and Classification Efficiency
A complete periodic star extraction and classification scheme is set up and
tested with the Hipparcos catalogue. The efficiency of each step is derived by
comparing the results with prior knowledge coming from the catalogue or from
the literature. A combination of two variability criteria is applied in the
first step to select 17 006 variability candidates from a complete sample of
115 152 stars. Our candidate sample turns out to include 10 406 known variables
(i.e., 90% of the total of 11 597) and 6600 contaminating constant stars. A
random forest classification is used in the second step to extract 1881 (82%)
of the known periodic objects while removing entirely constant stars from the
sample and limiting the contamination of non-periodic variables to 152 stars
(7.5%). The confusion introduced by these 152 non-periodic variables is
evaluated in the third step using the results of the Hipparcos periodic star
classification presented in a previous study (Dubath et al. [1]).Comment: 8 pages, 7 figure
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