1,360 research outputs found
Photometric redshifts for Quasars in multi band Surveys
MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it
is a machine learning method which can be used to cope with regression and
classification problems on complex and massive data sets. In this paper we give
the formal description of the method and present the results of its application
to the evaluation of photometric redshifts for quasars. The data set used for
the experiment was obtained by merging four different surveys (SDSS, GALEX,
UKIDSS and WISE), thus covering a wide range of wavelengths from the UV to the
mid-infrared. The method is able i) to achieve a very high accuracy; ii) to
drastically reduce the number of outliers and catastrophic objects; iii) to
discriminate among parameters (or features) on the basis of their significance,
so that the number of features used for training and analysis can be optimized
in order to reduce both the computational demands and the effects of
degeneracy. The best experiment, which makes use of a selected combination of
parameters drawn from the four surveys, leads, in terms of DeltaZnorm (i.e.
(zspec-zphot)/(1+zspec)), to an average of DeltaZnorm = 0.004, a standard
deviation sigma = 0.069 and a Median Absolute Deviation MAD = 0.02 over the
whole redshift range (i.e. zspec <= 3.6), defined by the 4-survey cross-matched
spectroscopic sample. The fraction of catastrophic outliers, i.e. of objects
with photo-z deviating more than 2sigma from the spectroscopic value is < 3%,
leading to a sigma = 0.035 after their removal, over the same redshift range.
The method is made available to the community through the DAMEWARE web
application.Comment: 38 pages, Submitted to ApJ in February 2013; Accepted by ApJ in May
201
Catalog of quasars from the Kilo-Degree Survey Data Release 3
We present a catalog of quasars selected from broad-band photometric ugri
data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are
identified by the random forest (RF) supervised machine learning model, trained
on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from
entries with excessively noisy, missing or otherwise problematic measurements.
Applying a feature importance analysis, we then tune the algorithm and identify
in the KiDS multiband catalog the 17 most useful features for the
classification, namely magnitudes, colors, magnitude ratios, and the stellarity
index. We used the t-SNE algorithm to map the multi-dimensional photometric
data onto 2D planes and compare the coverage of the training and inference
sets. We limited the inference set to r<22 to avoid extrapolation beyond the
feature space covered by training, as the SDSS spectroscopic sample is
considerably shallower than KiDS. This gives 3.4 million objects in the final
inference sample, from which the random forest identified 190,000 quasar
candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived
from a test set extracted from SDSS and not used in the training, are confirmed
by comparison with external spectroscopic and photometric QSO catalogs
overlapping with the KiDS footprint. The robustness of our results is
strengthened by number counts of the quasar candidates in the r band, as well
as by their mid-infrared colors available from WISE. An analysis of parallaxes
and proper motions of our QSO candidates found also in Gaia DR2 suggests that a
probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is
preferable for better completeness. Our study presents the first comprehensive
quasar selection from deep high-quality KiDS data and will serve as the basis
for versatile studies of the QSO population detected by this survey.Comment: Data available from the KiDS website at
http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php and the source code from
https://github.com/snakoneczny/kids-quasar
Shapley Supercluster Survey: Construction of the photometric catalogues and i-band data release
The Shapley Supercluster Survey is a multi-wavelength survey covering an area of âŒ23 degÂČ (âŒ260 MpcÂČ at z = 0.048) around the supercluster core, including nine Abell and two poor clusters, having redshifts in the range 0.045â0.050. The survey aims to investigate the role of the cluster-scale mass assembly on the evolution of galaxies, mapping the effects of the environment from the cores of the clusters to their outskirts and along the filaments. The optical (ugri) imaging acquired with OmegaCAM on the VLT Survey Telescope is essential to achieve the project goals providing accurate multi-band photometry for the galaxy population down to mâ + 6. We describe the methodology adopted to construct the optical catalogues and to separate extended and point-like sources. The catalogues reach average 5Ï limiting magnitudes within a 3 arcsec diameter aperture of ugri = [24.4,24.6,24.1,23.3] and are 93 per cent complete down to ugri = [23.8,23.8,23.5,22.0] mag, corresponding to âŒmâ r + 8.5. The data are highly uniform in terms of observing conditions and all acquired with seeing less than 1.1 arcsec full width at half-maximum. The median seeing in r band is 0.6 arcsec, corresponding to 0.56 kpc hâ»Âč 70 at z = 0.048. While the observations in the u, g and r bands are still ongoing, the i-band observations have been completed, and we present the i-band catalogue over the whole survey area. The latter is released and it will be regularly updated, through the use of the Virtual Observatory tools. This includes 734 319 sources down to i = 22.0 mag and it is the first optical homogeneous catalogue at such a depth, covering the central region of the Shapley supercluster
Inside Catalogs: A Comparison of Source Extraction Software
The scope of this article is to compare the catalog extraction performances obtained using the new combination of SExtractor with PSFEx against the more traditional and diffuse application of DAOPHOT with ALLSTAR; therefore, the paper may provide a guide for the selection of the most suitable catalog extraction software. Both software packages were tested on two kinds of simulated images, having a uniform spatial distribution of sources and an overdensity in the center, respectively. In both cases, SExtractor is able to generate a deeper catalog than DAOPHOT. Moreover, the use of neural networks for object classification plus the novel SPREAD_MODEL parameter push down to the limiting magnitude the possibility of star/galaxy separation. DAOPHOT and ALLSTAR provide an optimal solution for point-source photometry in stellar fields and very accurate and reliable PSF photometry, with robust star/galaxy separation. However, they are not useful for galaxy characterization and do not generate catalogs that are very complete for faint sources. On the other hand, SExtractor, along with the new capability to derive PSF photometry, turns out to be competitive and returns accurate photometry for galaxies also. We can report that the new version of SExtractor, used in conjunction with PSFEx, represents a very powerful software package for source extraction with performances comparable to those of DAOPHOT. Finally, by comparing the results obtained in the cases of a uniform and of an overdense spatial distribution of stars, we notice for both software packages a decline for the latter case in the quality of the results produced in terms of magnitudes and centroids
Steps towards a map of the nearby universe
We present a new analysis of the Sloan Digital Sky Survey data aimed at
producing a detailed map of the nearby (z < 0.5) universe. Using neural
networks trained on the available spectroscopic base of knowledge we derived
distance estimates for about 30 million galaxies distributed over ca. 8,000 sq.
deg. We also used unsupervised clustering tools developed in the framework of
the VO-Tech project, to investigate the possibility to understand the nature of
each object present in the field and, in particular, to produce a list of
candidate AGNs and QSOs.Comment: 3 pages, 1 figure. To appear in Nucl Phys. B, in the proceedings of
the NOW-2006 (Neutrino Oscillation Workshop - 2006), R. Fogli et al. ed
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
Astronomy has entered the big data era and Machine Learning based methods
have found widespread use in a large variety of astronomical applications. This
is demonstrated by the recent huge increase in the number of publications
making use of this new approach. The usage of machine learning methods, however
is still far from trivial and many problems still need to be solved. Using the
evaluation of photometric redshifts as a case study, we outline the main
problems and some ongoing efforts to solve them.Comment: 13 pages, 3 figures, Springer's Communications in Computer and
Information Science (CCIS), Vol. 82
A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies
Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields
Efficacy and safety of GLP-1 receptor agonists as add-on to SGLT2 inhibitors in type 2 diabetes mellitus: A meta-analysis
GLP-1 receptor agonists (GLP-1RA) and SGLT2 inhibitors (SGLT2i) have been associated with improved glycemic control, body weight loss and favorable changes in cardiovascular risk factors and outcomes. We conducted a systematic review and meta-analysis to evaluate the effects of the addition of GLP-1RA to SGLT2i in patients with type 2 diabetes mellitus and inadequate glycemic control. Six databases were searched until March 2019. Randomized controlled trials (RCT) with a follow-up of at least 24 weeks reporting on HbA1c, body weight, systolic blood pressure, lipids, achievement of HbA1c < 7%, requirement of rescue therapy due to hyperglycemia and hypoglycemic events were selected. Four RCTs were included. Compared to SGLT2i, the GLP-1RA/SGLT2i combination was associated with greater reduction in HbA1c (â0.74%), body weight (â1.61 kg), and systolic blood pressure (â3.32 mmHg). A higher number of patients achieved HbA1c < 7% (RR = 2.15), with a lower requirement of rescue therapy (RR = 0.37) and similar incidence of hypoglycemia. Reductions in total and LDL cholesterol were found. The present review supports treatment intensification with GLP-1RA in uncontrolled type 2 diabetes on SGLT2i. This drug regimen could provide improved HbA1c control, together with enhanced weight loss and blood pressure and lipids control
The use of neural networks to probe the structure of the nearby universe
In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs
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