LSSTC AGN Data Challenge 2021

Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

Astronomy and Astrophysics ◽

10.1051/0004-6361/201731106 ◽

2018 ◽

Vol 611 ◽

pp. A97 ◽

Cited By ~ 11

Author(s):

J. Pasquet-Itam ◽

J. Pasquet

Keyword(s):

Random Forest ◽

Random Forest Classifier ◽

Sloan Digital Sky Survey ◽

Light Curves ◽

Support Vector ◽

Learning Approach ◽

Photometric Redshifts ◽

K Nearest Neighbors ◽

Sky Survey ◽

Extraction Step

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope.

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Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

Molecular Biology and Evolution ◽

10.1093/molbev/msab259 ◽

2021 ◽

Author(s):

Martin Kapun ◽

Joaquin C B Nunez ◽

María Bogaerts-Márquez ◽

Jesús Murga-Moreno ◽

Margot Paris ◽

...

Keyword(s):

Evolutionary Dynamics ◽

Population Genomics ◽

Natural Populations ◽

Easy Access ◽

Data Repository ◽

Bioinformatics Pipeline ◽

Space And Time ◽

Sequencing Technologies ◽

Spatio Temporal ◽

Demographic Inference

Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in > 20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

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Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

10.1101/2021.02.01.428994 ◽

2021 ◽

Author(s):

Martin Kapun ◽

Joaquin C. B. Nunez ◽

María Bogaerts-Márquez ◽

Jesús Murga-Moreno ◽

Margot Paris ◽

...

Keyword(s):

Evolutionary Dynamics ◽

Population Genomics ◽

Natural Populations ◽

Easy Access ◽

Data Repository ◽

Bioinformatics Pipeline ◽

Space And Time ◽

Sequencing Technologies ◽

Different Seasons ◽

Spatio Temporal

Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last 20 years. A major challenge is the integration of these disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution and population structure of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 population samples from over 100 locations in >20 countries on four continents based on a combination of 121 unpublished and 150 previously published genomic datasets. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

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Distance to SN 1987A and the LMC

Symposium - International Astronomical Union ◽

10.1017/s0074180900118959 ◽

1999 ◽

Vol 190 ◽

pp. 549-554

Author(s):

Nino Panagia

Keyword(s):

Angular Size ◽

Large Magellanic Cloud ◽

Magellanic Cloud ◽

Light Curves ◽

Distance Modulus ◽

Absolute Size ◽

Sn 1987A

Using the new reductions of the IUE light curves by Sonneborn et al. (1997) and an extensive set of HST images of SN 1987A we have repeated and improved Panagia et al. (1991) analysis to obtain a better determination of the distance to the supernova. In this way we have derived an absolute size of the ringRabs= (6.23 ± 0.08) x 1017cm and an angular sizeR″ = 808 ± 17 mas, which give a distance to the supernovad(SN1987A) = 51.4 ± 1.2 kpc and a distance modulusm–M(SN1987A) = 18.55 ± 0.05. Allowing for a displacement of SN 1987A position relative to the LMC center, the distance to the barycenter of the Large Magellanic Cloud is also estimated to bed(LMC) = 52.0±1.3 kpc, which corresponds to a distance modulus ofm–M(LMC) = 18.58±0.05.

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Quasi-Periodic Oscillations in Dwarf Novae

International Astronomical Union Colloquium ◽

10.1017/s0252921100073310 ◽

1979 ◽

Vol 46 ◽

pp. 77-88

Author(s):

Edward L. Robinson

Keyword(s):

Binary Systems ◽

Outer Edge ◽

Light Curves ◽

Close Binary ◽

Bright Spot ◽

Dwarf Novae ◽

Periodic Oscillations ◽

High Frequencies ◽

Short Period ◽

Typical Time

Three distinct kinds of rapid variations have been detected in the light curves of dwarf novae: rapid flickering, short period coherent oscillations, and quasi-periodic oscillations. The rapid flickering is seen in the light curves of most, if not all, dwarf novae, and is especially apparent during minimum light between eruptions. The flickering has a typical time scale of a few minutes or less and a typical amplitude of about .1 mag. The flickering is completely random and unpredictable; the power spectrum of flickering shows only a slow decrease from low to high frequencies. The observations of U Gem by Warner and Nather (1971) showed conclusively that most of the flickering is produced by variations in the luminosity of the bright spot near the outer edge of the accretion disk around the white dwarf in these close binary systems.

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The Eclipsing Binary WX Eridani

International Astronomical Union Colloquium ◽

10.1017/s0252921100073656 ◽

1979 ◽

Vol 46 ◽

pp. 385

Author(s):

M.B.K. Sarma ◽

K.D. Abhankar

Keyword(s):

Light Curve ◽

Spectral Type ◽

Orbital Period ◽

Primary Component ◽

Light Curves ◽

Absorption Feature ◽

Primary Star ◽

Eclipsing Binary ◽

The Times ◽

Phase Angles

AbstractThe Algol-type eclipsing binary WX Eridani was observed on 21 nights on the 48-inch telescope of the Japal-Rangapur Observatory during 1973-75 in B and V colours. An improved period of P = 0.82327038 days was obtained from the analysis of the times of five primary minima. An absorption feature between phase angles 50-80, 100-130, 230-260 and 280-310 was present in the light curves. The analysis of the light curves indicated the eclipses to be grazing with primary to be transit and secondary, an occultation. Elements derived from the solution of the light curve using Russel-Merrill method are given. From comparison of the fractional radii with Roche lobes, it is concluded that none of the components have filled their respective lobes but the primary star seems to be evolving. The spectral type of the primary component was estimated to be F3 and is found to be pulsating with two periods equal to one-fifth and one-sixth of the orbital period.

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