Deep Learning Identifies High-$$z$$ Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
Abstract
We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution— pre-BN, BN, and post-BN—and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ~0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at $$z$$ = 1–3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2–10.3 $$M_⊙$$ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.
- Authors:
-
[2];
[4];
[6];
[7];
- Sorbonne Univ., Paris (France); Paris Diderot Univ. Paris (France); Inst. Univ. de France (France)
- Univ. of California, Santa Cruz, CA (United States). Physics Dept.
- Univ. of California, Santa Cruz, CA (United States). Physics Dept.; Hebrew Univ. of Jerusalem (Israel). Racah Inst. of Physics
- Univ. of California, Santa Cruz, CA (United States). Dept. of Astronomy and Astrophysics
- Hebrew Univ. of Jerusalem (Israel). Racah Inst. of Physics
- Heidelberg Univ. (Germany). Inst. for Theoretische Astrophysik
- Johns Hopkins Univ., Baltimore, MD (United States)
- Space Telescope Science Inst., Baltimore, MD (United States)
- Univ. of Pennsylvania, Philadelphia, PA (United States). Dept. of Physics and Astronomy
- Shanghai Normal Univ. (China). Shanghai Key Lab for Astrophysics
- Sorbonne Univ., Paris (France); Univ. of California, Santa Cruz, CA (United States). Physics Dept.
- Sorbonne Univ., Paris (France)
- Sorbonne Univ., Paris (France); PSL Research University Centre for Mathematical Morphology, Fontainebleau (France)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1544060
- Resource Type:
- Accepted Manuscript
- Journal Name:
- The Astrophysical Journal (Online)
- Additional Journal Information:
- Journal Name: The Astrophysical Journal (Online); Journal Volume: 858; Journal Issue: 2; Journal ID: ISSN 1538-4357
- Publisher:
- Institute of Physics (IOP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; Astronomy & Astrophysics
Citation Formats
Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domínguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., and Tuccillo, D. Deep Learning Identifies High-$z$ Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range. United States: N. p., 2018.
Web. doi:10.3847/1538-4357/aabfed.
Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domínguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., & Tuccillo, D. Deep Learning Identifies High-$z$ Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range. United States. https://doi.org/10.3847/1538-4357/aabfed
Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domínguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., and Tuccillo, D. Thu .
"Deep Learning Identifies High-$z$ Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range". United States. https://doi.org/10.3847/1538-4357/aabfed. https://www.osti.gov/servlets/purl/1544060.
@article{osti_1544060,
title = {Deep Learning Identifies High-$z$ Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range},
author = {Huertas-Company, M. and Primack, J. R. and Dekel, A. and Koo, D. C. and Lapiner, S. and Ceverino, D. and Simons, R. C. and Snyder, G. F. and Bernardi, M. and Chen, Z. and Domínguez-Sánchez, H. and Lee, C. T. and Margalef-Bentabol, B. and Tuccillo, D.},
abstractNote = {We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution— pre-BN, BN, and post-BN—and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ~0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at $z$ = 1–3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2–10.3 $M_⊙$ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.},
doi = {10.3847/1538-4357/aabfed},
journal = {The Astrophysical Journal (Online)},
number = 2,
volume = 858,
place = {United States},
year = {Thu May 10 00:00:00 EDT 2018},
month = {Thu May 10 00:00:00 EDT 2018}
}
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Figures / Tables:
Table 1: Explanation of the 19 camera orientations used to generate mock 2D images from the simulations.
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