The regression of effective temperatures in APOGEE and LAMOST |
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| Institution: | 1. Department of Mathematics, Sri Aurobindo College, University of Delhi, New Delhi, Delhi 110017, India;2. Department of Mathematics, Deshbandhu college, University of Delhi, New Delhi, Delhi 110019, India;3. Department of Commerce, Sri Aurobindo College, University of Delhi, New Delhi, Delhi 110017, India;4. Department of Physics, Deshbandhu college, University of Delhi, New Delhi, Delhi 110019, India;1. Maharaja Agrasen College, Department of Mathematics, University of Delhi, Delhi -110096, India;2. Shivaji College, Department of Mathematics, University of Delhi, Delhi 110027, India;3. Daulat Ram College, Department of Mathematics, University of Delhi, Delhi 110007, India;1. Department of Mathematics, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002;2. Department of Mathematics, Graphic Era (deemed to be) University, Dehradun, Uttarakhand, India 248002;3. Indraprastha Institute of Management and Technology, Saharanpur, Uttar Pradesh, India 247551;4. Former Professor of Mathematics, Indian Institute of Technology, Roorkee, Uttarakhand, India 247671;5. Emeritus Professor of Mathematics, Ambala College of Engineering and Applied Research, Devasthali, Ambala Cantt, Haryana, India 133101 |
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| Abstract: | We use the random forest to regress the surface effective temperatures of stars in APOGEE from SDSS DR16 and LAMOST DR6. When the NUV-u, u-g, g-r, r-i, i-J, J-H, H-K, K-WISE_4_5 magnitudes are used as machine learning features, the coefficient of determination of regression are 94.91% in APOGEE and 90.46% in LAMOST. The standard deviation of the prediction and pipeline temperatures are 93.89K in APOGEE and 113.10K in LAMOST. When the NUV-J, J-H, H-K, K-WISE_4_5 magnitudes are used as features, the coefficient of determination of regression are 94.37% in APOGEE and 88.89% in LAMOST. The standard deviation is 96.59K in APOGEE and 119.92K in LAMOST. The J-H magnitudes are the most important feature to predict the effective temperatures, and the NUV-J magnitudes are the second important feature. The NUV-J, J-H, H-K, K-WISE_4_5 magnitudes are from the all-sky survey and can be employed widely to regress the effective temperatures of stars. |
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