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Chemical evolution model to derive metallicity distributions for each stellar population

Published online by Cambridge University Press:  30 October 2019

Hidetomo Homma Open the ORCID record for Hidetomo Homma [Opens in a new window]
Hidetomo Homma*
Affiliation:
National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, JAPAN email: [email protected]
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Abstract

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We have constructed a chemical evolution model in order to reproduce the both metallicity distribution functions (MDFs) of red giant branch stars (RGBs) and RR Lyrae stars (RRLs) of a dwarf galaxy, simultaneously. The detailed chemical abundances of RGBs of the Local Group dwarf galaxies have been measured by spectroscopic observations. Moreover, the metallicity of RRLs of a dwarf galaxy are estimated by using the theoretical period-luminosity relations in the previous study and it is found that the mean metallicity of RRLs are lower than that of RGBs. In order to investigate the MDFs of RGBs and RRLs, we combine our chemical evolution model with the stellar evolutionally isochrones and calculate the metallicity of RGBs and RRLs, respectively. As a result, our chemical evolution model reproduces the peak metallicity of both MDFs of RGBs and RRLs of Sculptor and Fornax dwarf spheroidal galaxies (dSphs), simultaneously. Therefore, it is found that the difference of the mean metallicity between RGBs and RRLs are caused by the effects of stellar evolution. Moreover, by using the theoretical period-luminosity-metallicity relation of the RRLs, our chemical evolution model determines that the distance modulus of Sculptor and Fornax dSphs are 19.68 ± 0.09 and ${20.81^{+0.13}_{-0.11}}$, respectively. However, our model underestimates the number of metal-rich RRLs ([Fe/H] > −1.5) of Fornax dSph. This result suggests that the mass-loss rate of metal-rich RGBs would be larger than that of metal-poor RGBs.

Keywords

galaxies: abundancesgalaxies: dwarfgalaxies: evolutiongalaxies: individual (Sculptor, Fornax)
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S344: Dwarf Galaxies: From the Deep Universe to the Present , August 2018 , pp. 204 - 207
Copyright
© International Astronomical Union 2019 

References

Bressan, A., Marigo, P., Girardi, L., Salasnich, B., Dal, C. C., Rubele, S., & Nanni, A. 2012, MNRAS, 427, 127 CrossRefGoogle Scholar
de Boer, T. J. L., Tolstoy, E., Hill, V., Saha, A., Olsen, K., Starkenburg, E., Lemasle, B., Irwin, M. J., & Battaglia, G. 2012, A&A, 539, 103 Google Scholar
de Boer, T. J. L., Tolstoy, E., Hill, V., Saha, A., Olszewski, E. W., Mateo, M., Starkenburg, E., Battaglia, G., & Walker, M. G. 2012, A&A, 544, 73 Google Scholar
Homma, H., Murayama, T., Kobayashi, M. A. R., & Taniguchi, Y. 2015, ApJ, 799, 230 CrossRefGoogle Scholar
Karczmarek, P., Pietrzyński, G., Górski, M., Gieren, W., & Bersier, D. 2017, AJ, 154, 263 CrossRefGoogle Scholar
Kirby, E. N., Guhathakurta, P., Simon, J. D., Geha, M. C., Rockosi, C. M., Sneden, C., Cohen, J. G., Sohn, S. T., Majewski, S. R., & Siegel, M. 2010, ApJS, 191, 352 10.1088/0067-0049/191/2/352CrossRefGoogle Scholar
Marconi, M., Coppola, G., Bono, G., Braga, V., Pietrinferni, A., Buonanno, R., Castellani, M., Musella, I., Ripepi, V., & Stellingwerf, R. F. 2015, ApJ, 808, 50 CrossRefGoogle Scholar
Martínez-Vázquez, C. E., Monelli, M., Gallart, C., Bono, G., Bernard, E. J., Stetson, P. B., Ferraro, I., Walker, A. R., Dall’Ora, M., Fiorentino, G., & Iannicola, G. 2016, MNRAS, 461, L41 CrossRefGoogle Scholar
Martínez-Vázquez, C. E., Stetson, P. B., Monelli, M., Bernard, E. J., Fiorentino, G., Gallart, C., Bono, G., Cassisi, S., Dall’Ora, M., Ferraro, I., Iannicola, G., & Walker, A. R. 2016, MNRAS, 462, 4349 10.1093/mnras/stw1895CrossRefGoogle Scholar
Miglio, A., Brogaard, K., Stello, D., Chaplin, W. J., D’Antona, F., Montalbán, J., Basu, S., Bressan, A., Grundahl, F., Pinsonneault, M., Serenelli, A. M., Elsworth, Y., Hekker, S., Kallinger, T., Mosser, B., Ventura, P., Bonanno, A., Noels, A., Silva, A. V., Szabo, R., Li, J., McCauliff, S., Middour, C. K., & Kjeldsen, H. 2012, MNRAS, 419, 2077 10.1111/j.1365-2966.2011.19859.xCrossRefGoogle Scholar
Pietrzyński, G., Gieren, W., Szewczyk, O., Walker, A., Rizzi, L., Bresolin, F., Kudritzki, R.-P., Nalewajko, K., Storm, J., Dall’Ora, M., & Ivanov, V. 2008, AJ, 135, 1993 10.1088/0004-6256/135/6/1993CrossRefGoogle Scholar
Rizzi, L. 2002 PhD Thesis, Padova Univ. Google Scholar
Salaris, M., de Boer, T., Tolstoy, E., Fiorentino, G., & Cassisi, S. 2013, A&A, 559, 57 Google Scholar