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Register a new userSearch for carbon stars and DZ white dwarfs in SDSS spectra survey through machine learning
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Carbon stars and DZ white dwarfs are two types of rare objects in the Galaxy. In this paper, we have applied the label propagation algorithm to search for these two types of stars from Data Release Eight (DR8) of the Sloan Digital Sky Survey (SDSS), which is verified to be efficient by calculating precision and recall. From nearly two million spectra including stars, galaxies and QSOs, we have found 260 new carbon stars in which 96 stars have been identified as dwarfs and 7 identified as giants, and 11 composition spectrum systems (each of them consists of a white dwarf and a carbon star). Similarly, using the label propagation method, we have obtained 29 new DZ white dwarfs from SDSS DR8. Compared with PCA reconstructed spectra, the 29 findings are typical DZ white dwarfs. We have also investigated their proper motions by comparing them with proper motion distribution of 9,374 white dwarfs, and found that they satisfy the current observed white dwarfs by SDSS generally have large proper motions. In addition, we have estimated their effective temperatures by fitting the polynomial relationship between effective temperature and g-r color of known DZ white dwarfs, and found 12 of the 29 new DZ white dwarfs are cool, in which nine are between 6000K and 6600K, and three are below 6000K.
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Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected systematic errors limit the practical applicability of this approach to high-amplitude variability and well-behaving data sets. Searching for a new variability detection technique that would be applicable to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties, we propose to consider variability detection as a classification problem that can be approached with machine learning. We compare several classification algorithms: Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects identified) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, 13 low-amplitude variables are new discoveries. We find that the considered machine learning classifiers are more efficient (they find more variables and less false candidates) compared to traditional techniques that consider individual variability indices or their linear combination. The NN, SGB, SVM and RF show a higher efficiency compared to LR and kNN.
We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as naive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.
A number of so-called ultra-cool white dwarfs have been detected in different surveys so far. However, based on anecdotal evidence it is believed that most or all of these ultra-cool white dwarfs are low-mass products of binary evolution and thus not representative for the oldest white dwarfs. Their low mass causes relatively high luminosity making them the first cool white dwarfs detected in relatively shallow surveys. Deeper observations are needed for the oldest, high mass white dwarfs with the longest cooling times. We report results of an ongoing project that combines deep IR and optical data. This combination plus proper motion information will allow an unambiguous identification of very cool white dwarfs, since the spectral energy distributions are very different from other types of stellar objects. The atmospheric parameters that can be derived from the spectral energy distributions together with the proper motions inferred from the IR data can be used to construct the white dwarf luminosity functions for the thick disc and halo populations. From these we will be able to test the early star formation history and initial mass function of the first stellar populations.
White dwarfs with metal lines in their spectra act as signposts for post-main sequence planetary systems. Searching the Sloan Digital Sky Survey (SDSS) data release 12, we have identified 231 cool (<9000 K) DZ white dwarfs with strong metal absorption, extending the DZ cooling sequence to both higher metal abundances, lower temperatures, and hence longer cooler ages. Of these 231 systems, 104 are previously unknown white dwarfs. Compared with previous work, our spectral fitting uses improved model atmospheres with updated line profiles and line-lists, which we use to derive effective temperatures and abundances for up to 8 elements. We also determine spectroscopic distances to our sample, identifying two halo-members with tangential space-velocities >300 kms-1. The implications of our results on remnant planetary systems are to be discussed in a separate paper.
Little is known about the incidence of magnetic fields among the coolest white dwarfs. Their spectra usually do not exhibit any absorption lines as the bound-bound opacities of hydrogen and helium are vanishingly small. Probing these stars for the presence of magnetic fields is therefore extremely challenging. However, external pollution of a cool white dwarf by, e.g., planetary debris, leads to the appearance of metal lines in its spectral energy distribution. These lines provide a unique tool to identify and measure magnetism in the coolest and oldest white dwarfs in the Galaxy. We report the identification of 7 strongly metal polluted, cool (T_eff < 8000 K) white dwarfs with magnetic field strengths ranging from 1.9 to 9.6 MG. An analysis of our larger magnitude-limited sample of cool DZ yields a lower limit on the magnetic incidence of 13+/-4 percent, noticeably much higher than among hot DA white dwarfs.
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