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Start Over Author "Mucesh, S." Topic methods: statistical
2 results on '"Mucesh, S."'

1. A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest.

Author

Mucesh, S, Hartley, W G, Palmese, A, Lahav, O, Whiteway, L, Bluck, A F L, Alarcon, A, Amon, A, Bechtol, K, Bernstein, G M, Carnero Rosell, A, Carrasco Kind, M, Choi, A, Eckert, K, Everett, S, Gruen, D, Gruendl, R A, Harrison, I, Huff, E M, and Kuropatkin, N

Subjects
*DISTRIBUTION (Probability theory), *RANDOM forest algorithms, *MACHINE learning, *STELLAR mass, *GALACTIC evolution, *REDSHIFT
Abstract

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes , our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro   1 , a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies. [ABSTRACT FROM AUTHOR]

Published
2021
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2. A machine learning approach to galaxy properties: Joint redshift-stellar mass probability distributions with Random Forest

Author

A. A. Plazas, L. N. Da Costa, W. G. Hartley, Maria E. S. Pereira, Brian Yanny, Marcos Lima, Alex Drlica-Wagner, J. Carretero, Antonella Palmese, E. M. Huff, Juan Garcia-Bellido, Ramon Miquel, M. A. G. Maia, Michel Aguena, V. Scarpine, A. Choi, Martin Crocce, F. J. Castander, G. Tarle, R. D. Wilkinson, Ian Harrison, S. Mucesh, K. Honscheid, Sunayana Bhargava, A. Alarcon, J. De Vicente, David J. James, Huan Lin, Pablo Fosalba, M. Carrasco Kind, Chun-Hao To, Alexandra Amon, E. J. Sanchez, F. Paz-Chinchón, Keith Bechtol, E. Suchyta, August E. Evrard, M. Costanzi, M. Smith, Felipe Menanteau, Josh Frieman, D. L. Hollowood, S. Allam, Robert A. Gruendl, S. Serrano, Ofer Lahav, Daniel Gruen, Samuel Hinton, Peter Melchior, Christopher J. Conselice, Erin Sheldon, B. Flaugher, E. Bertin, G. Gutierrez, David J. Brooks, S. Desai, Enrique Gaztanaga, Robert Morgan, J. Gschwend, S. Everett, D. W. Gerdes, Gary Bernstein, I. Ferrero, H. T. Diehl, David Bacon, I. Sevilla-Noarbe, N. Kuropatkin, K. D. Eckert, T. N. Varga, Asa F. L. Bluck, Kyler Kuehn, Michael Schubnell, Daniel Thomas, L. Whiteway, A. Carnero Rosell, National Science Foundation (US), Ministerio de Ciencia, Innovación y Universidades (España), Generalitat de Catalunya, European Commission, Instituto Nacional de Ciência e Tecnologia (Brasil), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, Mucesh, S., Hartley, W. G., Palmese, A., Lahav, O., Whiteway, L., Amon, A., Bechtol, K., Bernstein, G. M., Carnero Rosell, A., Carrasco Kind, M., Choi, A., Eckert, K., Everett, S., Gruen, D., Gruendl, R. A., Harrison, I., Huff, E. M., Kuropatkin, N., Sevilla-Noarbe, I., Sheldon, E., Yanny, B., Aguena, M., Allam, S., Bacon, D., Bertin, E., Bhargava, S., Brooks, D., Carretero, J., Castander, F. J., Conselice, C., Costanzi, M., Crocce, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Diehl, H. T., Drlica-Wagner, A., Evrard, A. E., Ferrero, I., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gschwend, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lima, M., Lin, H., Maia, M. A. G., Melchior, P., Menanteau, F., Miquel, R., Morgan, R., Paz-Chinchón, F., Plazas, A. A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Smith, M., Suchyta, E., Tarle, G., Thomas, D., To, C., Varga, T. N., and Wilkinson, R. D.

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, statistical [Methods], software: data analysis, computer.software_genre, 01 natural sciences, data analysi [software], Copula (probability theory), Machine Learning (cs.LG), Astrophysics - Cosmology and Nongalactic Astrophysic, data analysis [Methods], 010303 astronomy & astrophysics, Physics, fundamental parameter [galaxies], galaxies: fundamental parameters, Random forest, Software: public realese, fundamental parameters [Galaxies], Probability distribution, galaxies: evolution, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, public realese [Software], Cosmology and Nongalactic Astrophysics (astro-ph.CO), methods: data analysis, methods: statistical, software: public release, Astrophysics - Astrophysics of Galaxies, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Machine learning, Astrophysics - Astrophysics of Galaxie, 0103 physical sciences, Galaxy formation and evolution, [INFO]Computer Science [cs], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Probability integral transform, Instrumentation and Methods for Astrophysics (astro-ph.IM), 010308 nuclear & particles physics, business.industry, Univariate, Astronomy and Astrophysics, evolution [Galaxies], public release [software], Redshift, Galaxy, Space and Planetary Science, data analysis [Software], Astrophysics of Galaxies (astro-ph.GA), data analysi [methods], Artificial intelligence, Astrophysics - Instrumentation and Methods for Astrophysic, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], business, computer
Abstract

We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the $griz$ bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for $10,699$ test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed GALPRO, a highly intuitive and efficient Python package to rapidly generate multivariate PDFs on-the-fly. GALPRO is documented and available for researchers to use in their cosmology and galaxy evolution studies., Comment: 18 pages, 8 figures, Accepted by MNRAS

Published
2021
Full Text
View/download PDF

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