scholarly journals Discovery of CWISE J052306.42−015355.4, an Extreme T Subdwarf Candidate

2022 ◽  
Vol 163 (2) ◽  
pp. 47
Author(s):  
Hunter Brooks ◽  
J. Davy Kirkpatrick ◽  
Dan Caselden ◽  
Adam C. Schneider ◽  
Aaron M. Meisner ◽  
...  

Abstract We present the discovery of CWISE J052306.42−015355.4, which was found as a faint, significant proper-motion object (0.″52 ± 0.″08 yr−1) using machine-learning tools on the unWISE re-processing of time series images from the Wide-field Infrared Survey Explorer. Using the CatWISE2020 W1 and W2 magnitudes along with a J-band detection from the VISTA Hemisphere Survey, the location of CWISE J052306.42−015355.4 on the W1 − W2 versus J − W2 diagram best matches that of other known, or suspected, extreme T subdwarfs. As there is currently very little knowledge concerning extreme T subdwarfs we estimate a rough distance of ≤68 pc, which results in a tangential velocity of ≤167 km s−1, both of which are tentative. A measured parallax is greatly needed to test these values. We also estimate a metallicity of −1.5 < [M/H] < −0.5 using theoretical predictions.

2019 ◽  
Vol 488 (3) ◽  
pp. 4033-4041 ◽  
Author(s):  
I Gezer ◽  
H Van Winckel ◽  
R Manick ◽  
D Kamath

ABSTRACT We performed a photometric and spectroscopic analysis of two RV Tauri stars: GK Car and GZ Nor. Both objects are surrounded by hot circumstellar dust. Their pulsation periods, derived from ASAS (All Sky Automated Survey) photometric time-series, have been used to derive their luminosities and distances via the period–luminosity–distance (PLC) relation. In addition, for both objects, Gaia distances are available. The Gaia distances and luminosities are consistent with the values obtained from the PLC relationship. GK Car is at distance of 4.5 ± 1.3 kpc and has a luminosity of 1520 ± 840 L⊙, while GZ Nor is at distance of 8.4 ± 2.3 kpc and has a luminosity of 1240 ± 690 L⊙. Our abundance analysis reveals that both stars show depletion of refractory elements with [Fe/H] = −1.3 and [Zn/Ti] = +1.2 for GK Car and [Fe/H] = −2.0 and [Zn/Ti] = +0.8 for GZ Nor. In the WISE(Wide-Field Infrared Survey Explorer) colour–colour diagram, GK Car is located in the RV Tauri box as originally defined by Lloyd Evans and updated by Gezer et al., while GZ Nor is not. Despite this, we argue that both objects are surrounded by a gravitationally bound disc. As depletion is observed in binaries, we postulate that both stars are binaries as well. RV Tauri stars are generally acknowledged to be post-asymptotic giant branch (post-AGB) stars. Recent studies show that they might be either indeed post-AGB or post-red giant branch (post-RGB) objects depending on their luminosity. For both objects, the derived luminosities are relatively low for post-AGB objects, however, the uncertainties are quite large. We conclude that they could be either post-RGB or post-AGB objects.


2018 ◽  
Vol 10 (10) ◽  
pp. 1560 ◽  
Author(s):  
Wei Wu ◽  
Luoqi Ge ◽  
Jiancheng Luo ◽  
Ruohong Huan ◽  
Yingpin Yang

Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images.


Author(s):  
Dipankar Majumdar ◽  
Arup Kumar Bhattacharjee ◽  
Soumen Mukherjee

Investment in the right fund at the right time happens to be the key to success in the stock trading business. Therefore, for strategic investment, the selection of the right opportunity has to be executed crucially so as to reap the maximum returns from the market. Predicting the stock market has always been known to be very critical and needs years of experience as it involves lots of interleaving parameters and constraints. Intelligent investment in mutual funds (MF) can be done when various machine learning tools are used to predict future fund value using the past fund value. In this chapter, an elaborate discussion is presented on the different types of mutual funds and how these data can be used in prediction by machine learning in different literature. In this work, the NAV of a total of 17 different mutual funds have been extracted from the website of AMFI, and thereafter, ANFIS is used to forecast the time series of the NAV of the MF. They have been trained using ANFIS and thereafter tested for prediction with satisfactory results.


2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.


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