Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhotometric brown-dwarf classification
103
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We have developed a method photo-type to identify and accurately classify L and T dwarfs, onto the standard system, from photometry alone. We combine SDSS, UKIDSS and WISE data and classify point sources by comparing the izYJHKW1W2 colours against template colours for quasars, stars, and brown dwarfs. In a sample of $6.5times10^6$ bright point sources, J$<$17.5, from 3150 deg$^2$, we identify and type 898 L and T dwarfs, making this the largest homogeneously selected sample of brown dwarfs to date. The sample includes 713 (125) new (previously known) L dwarfs and 21 (39) T dwarfs. For the previously-known sources, the scatter in the plot of photo-type vs spectral type indicates that our photo-types are accurate to 1.5 (1.0) sub-types rms for L (T) dwarfs. Peculiar objects and candidate unresolved binaries are identified.
rate research
Read More
Aims. We present a method, named photo-type, to identify and accurately classify L and T dwarfs onto the standard spectral classification system using photometry alone. This enables the creation of large and deep homogeneous samples of these objects efficiently, without the need for spectroscopy. Methods. We created a catalogue of point sources with photometry in 8 bands, ranging from 0.75 to 4.6 microns, selected from an area of 3344 deg^2, by combining SDSS, UKIDSS LAS, and WISE data. Sources with 13.0 < J < 17.5, and Y - J > 0.8, were then classified by comparison against template colours of quasars, stars, and brown dwarfs. The L and T templates, spectral types L0 to T8, were created by identifying previously known sources with spectroscopic classifications, and fitting polynomial relations between colour and spectral type. Results. Of the 192 known L and T dwarfs with reliable photometry in the surveyed area and magnitude range, 189 are recovered by our selection and classification method. We have quantified the accuracy of the classification method both externally, with spectroscopy, and internally, by creating synthetic catalogues and accounting for the uncertainties. We find that, brighter than J = 17.5, photo-type classifications are accurate to one spectral sub-type, and are therefore competitive with spectroscopic classifications. The resultant catalogue of 1157 L and T dwarfs will be presented in a companion paper.
We present a homogeneous sample of 1361 L and T dwarfs brighter than J = 17.5 (of which 998 are new), from an effective area of 3070 deg2, classified by the photo-type method to an accuracy of one spectral sub-type using izYJHKW1W2 photometry from SDSS+UKIDSS+WISE. Other than a small bias in the early L types, the sample is shown to be effectively complete to the magnitude limit, for all spectral types L0 to T8. The nature of the bias is an incompleteness estimated at 3% because peculiar blue L dwarfs of type L4 and earlier are classified late M. There is a corresponding overcompleteness because peculiar red (likely young) late M dwarfs are classified early L. Contamination of the sample is confirmed to be small: so far spectroscopy has been obtained for 19 sources in the catalogue and all are confirmed to be ultracool dwarfs. We provide coordinates and izYJHKW1W2 photometry of all sources. We identify an apparent discontinuity, $Delta$m $sim$ 0.4 mag., in the Y-K colour between spectral types L7 and L8. We present near-infrared spectra of nine sources identified by photo-type as peculiar, including a new low-gravity source ULAS J005505.68+013436.0, with spectroscopic classification L2{$gamma$}. We provide revised izYJHKW1W2 template colours for late M dwarfs, types M7 to M9.
High contrast imaging at optical wavelengths is limited by the modest correction of conventional near-IR optimized AO systems.We take advantage of new fast and low-readout-noise detectors to explore the potential of fast imaging coupled to post-processing techniques to detect faint companions to stars at small separations. We have focused on I-band direct imaging of the previously detected brown dwarf binary HD130948BC,attempting to spatially resolve the L2+L2 benchmark system. We used the Lucky-Imaging instrument FastCam at the 2.5-m Nordic Telescope to obtain quasi diffraction-limited images of HD130948 with ~0.1 resolution.In order to improve the detectability of the faint binary in the vicinity of a bright (I=5.19 pm 0.03) solar-type star,we implemented a post-processing technique based on wavelet transform filtering of the image which allows us to strongly enhance the presence of point-like sources in regions where the primary halo dominates. We detect for the first time the BD binary HD130948BC in the optical band I with a SNR~9 at 2.561pm 0.007 (46.5 AU) from HD130948A and confirm in two independent dataset that the object is real,as opposed to time-varying residual speckles.We do not resolve the binary, which can be explained by astrometric results posterior to our observations that predict a separation below the NOT resolution.We reach at this distance a contrast of dI = 11.30 pm 0.11, and estimate a combined magnitude for this binary to I = 16.49 pm 0.11 and a I-J colour 3.29 pm 0.13. At 1, we reach a detectability 10.5 mag fainter than the primary after image post-processing. We obtain on-sky validation of a technique based on speckle imaging and wavelet-transform processing,which improves the high contrast capabilities of speckle imaging.The I-J colour measured for the BD companion is slightly bluer, but still consistent with what typically found for L2 dwarfs(~3.4-3.6).
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques fitting parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieves an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.
It is well-known that stars with giant planets are on average more metal-rich than stars without giant planets, whereas stars with detected low-mass planets do not need to be metal-rich. With the aim of studying the weak boundary that separates giant planets and brown dwarfs (BDs) and their formation mechanism, we analyze the spectra of a sample of stars with already confirmed BD companions both by radial velocity and astrometry. We employ standard and automatic tools to perform an EW-based analysis and to derive chemical abundances from CORALIE spectra of stars with BD companions. We compare these abundances with those of stars without detected planets and with low-mass and giant-mass planets. We find that stars with BDs do not have metallicities and chemical abundances similar to those of giant-planet hosts but they resemble the composition of stars with low-mass planets. The distribution of mean abundances of $alpha$-elements and iron peak elements of stars with BDs exhibit a peak at about solar abundance whereas for stars with low-mass and high-mass planets the [X$_alpha$/H] and [X$_{rm Fe}$/H] peak abundances remain at $sim -0.1$~dex and $sim +0.15$~dex, respectively. We display these element abundances for stars with low-mass and high-mass planets, and BDs versus the minimum mass, $m_C sin i$, of the most-massive substellar companion in each system, and we find a maximum in $alpha$-element as well as Fe-peak abundances at $m_C sin i sim 1.35pm 0.20$ jupiter masses. We discuss the implication of these results in the context of the formation scenario of BDs in comparison with that of giant planets.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
