Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning

Autor: Mucesh, Sunil, Hartley, William G., Gilligan-Lee, Ciarán M., Lahav, Ofer

Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenome

Externí odkaz: http://arxiv.org/abs/2412.02439

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

Autor: Mucesh, Sunil, Hartley, W. G., Palmese, Antonella, Lahav, Ofer, Whiteway, L., Bluck, A. F. L., Alarcon, A., Amon, A., Bechtol, K., Bernstein, G. M., Carnero Rosell, A., Carrasco Kind, Matias, Choi, A., Eckert, K., Everett, S., Gruen, Daniel, Gruendl, Robert A., Harrison, Ian, Huff, Eric M., Kuropatkin, N., Sevilla-Noarbe, I., Sheldon, E., Yanny, B., Aguena, M., Allam, S., Bacon, D., Bertin, Emmanuel, Bhargava, S., Brooks, D., Carretero, J., Castander, F. J., Conselice, C., Costanzi, M., Crocce, M., da Costa, L. N., Pereira, Maria Elidaiana da Silva, De Vicente, J., Desai, S., Diehl, H. T., Drlica-Wagner, A., Evrard, August E., Ferrero, Ismael, Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, Juan, Gaztanaga, E., Gerdes, David W., Gschwend, J., Gutierrez, G., Hinton, Samuel R., Hollowood, Devon L., Honscheid, Klaus, James, D. J., Kuehn, K., Lima, M., Lin, H., Maia, M. A. G., Melchior, P., Menanteau, F., Miquel, R., Morgan, R., Paz-Chinchón, Francisco, Plazas, Andrés A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Smith, Mathew, Suchyta, E., Tarle, G., Thomas, D., To, Chun-Hao, Varga, T. N., Wilkinson, R. D.

Publikováno v: Mucesh, Sunil Hartley, W. G. Palmese, Antonella Lahav, Ofer Whiteway, L. Bluck, A. F. L. Alarcon, A. Amon, A. Bechtol, K. Bernstein, G. M. Carnero Rosell, A. Carrasco Kind, Matias Choi, A. Eckert, K. Everett, S. Gruen, Daniel Gruendl, Robert A. Harrison, Ian Huff, Eric M. Kuropatkin, N. Sevilla-Noarbe, I. Sheldon, E. Yanny, B. Aguena, M. Allam, S. Bacon, D. Bertin, Emmanuel Bhargava, S. Brooks, D. Carretero, J. Castander, F. J. Conselice, C. Costanzi, M. Crocce, M. da Costa, L. N. Pereira, Maria Elidaiana da Silva De Vicente, J. Desai, S. Diehl, H. T. Drlica-Wagner, A. Evrard, August E. Ferrero, Ismael Flaugher, B. Fosalba, P. Frieman, J. García-Bellido, Juan Gaztanaga, E. Gerdes, David W. Gschwend, J. Gutierrez, G. Hinton, Samuel R. Hollowood, Devon L. Honscheid, Klaus James, D. J. Kuehn, K. Lima, M. Lin, H. Maia, M. A. G. Melchior, P. Menanteau, F. Miquel, R. Morgan, R. Paz-Chinchón, Francisco Plazas, Andrés A. Sanchez, E. Scarpine, V. Schubnell, M. Serrano, S. Smith, Mathew Suchyta, E. Tarle, G. Thomas, D. To, Chun-Hao Varga, T. N. Wilkinson, R. D. . A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest. Monthly notices of the Royal Astronomical Society. 2021, 502(2), 2770-2786
Monthly notices of the Royal Astronomical Society

Externí odkaz: http://hdl.handle.net/10852/89328
https://www.duo.uio.no/bitstream/handle/10852/89328/1/stab164.pdf