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Calibrating TP-AGB stellar models and chemical yields through resolved stellar populations in the Small Magellanic Cloud

Published online by Cambridge University Press:  30 December 2019

Giada Pastorelli Open the ORCID record for Giada Pastorelli [Opens in a new window] ,
Paola Marigo ,
Léo Girardi  and
the STARKEY project team
Giada Pastorelli
Affiliation:
Dipartimento di Fisica e Astronomia Galileo Galilei, Università di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy – email: giada.pastorelli@studenti.unipd.it
Paola Marigo
Affiliation:
Dipartimento di Fisica e Astronomia Galileo Galilei, Università di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy – email: giada.pastorelli@studenti.unipd.it
Léo Girardi
Affiliation:
Dipartimento di Fisica e Astronomia Galileo Galilei, Università di Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy – email: giada.pastorelli@studenti.unipd.it Osservatorio Astronomico di Padova – INAF, Vicolo dell’Osservatorio 5, 35122 Padova, Italy
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Abstract

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Most of the physical processes driving the TP-AGB evolution are not yet fully understood and they need to be modelled with parameterised descriptions. We present the results of the on-going calibration of the TP-AGB phase based on a complete sample of AGB stars in the Small Magellanic Cloud (SAGE-SMC survey). We computed large grids of TP-AGB models with several combinations of third dredge-up and mass-loss prescriptions with the COLIBRI code. The SMC AGB population is modelled with the population synthesis code TRILEGAL according to the space-resolved star formation history derived with the deep photometry from the VISTA survey of the Magellanic Clouds. We put quantitative constraints on the efficiencies of the third dredge-up and mass loss by requiring the models to reproduce the star counts and the luminosity functions of the observed Oxygen-, Carbon-rich and extreme-AGB stars and we investigate the impact of the best-fitting prescriptions on the chemical yields.

Keywords

stars: AGB and post-AGBstars: mass lossgalaxies: Magellanic Clouds
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S343: Why Galaxies Care About AGB Stars: A Continuing Challenge through Cosmic Time , August 2018 , pp. 269 - 272
Copyright
© International Astronomical Union 2019 

References

Blöcker, T. 1995, A&A, 297, 727 Google Scholar
Boyer, M. L., McDonald, I., Srinivasan, S., Zijlstra, A., van Loon, J. T., Olsen, K. A. G., & Sonneborn, G. 2015, ApJ, 810, 116 CrossRefGoogle Scholar
Boyer, M. L., Srinivasan, S., van Loon, J. T., McDonald, I., Meixner, M., Zaritsky, D., Gordon, K. D., Kemper, F., Babler, B., Block, M., Bracker, S., Engelbracht, C. W., Hora, J., Indebetouw, R., Meade, M., Misselt, K. 2011, AJ, 142, 103.10.1088/0004-6256/142/4/103CrossRefGoogle Scholar
Cioni, M.-R. L., Clementini, G., Girardi, L., Guandalini, R., Gullieuszik, M., Miszalski, B., Moretti, M.-I., Ripepi, V., Rubele, S., Bagheri, G., Bekki, K., Cross, N., de Blok, W. J. G., de Grijs, R., Emerson, J. P., Evans, C. J., Gibson, B., Gonzales-Solares, E., Groenewegen, M. A. T., Irwin, M., Ivanov, V. D., Lewis, J., Marconi, M., Marquette, J.-B., Mastropietro, C., Moore, B., Napiwotzki, R., Naylor, T., Oliveira, J. M., Read, M., Sutorius, E., van Loon, J. T., Wilkinson, M. I., & Wood, P. R. 2011, A&A, 527, A116 Google Scholar
Cranmer, S. R., & Saar, S. H. 2011, ApJ, 741, 54 CrossRefGoogle Scholar
Cummings, J. D. and Kalirai, J. S. and Tremblay, P.-E. and Ramirez-Ruiz, E. & Choi, J. arXiv:1809.01673Google Scholar
El-Badry, K., Rix, H.-W., & Weisz, D. R. 2018, ApJ (Letters), 860, L17.Google Scholar
Eriksson, K., Nowotny, W., Höfner, S., Aringer, B., & Wachter, A. 2014, A&A, 566, A95.Google Scholar
Girardi, L., Groenewegen, M. A. T., Hatziminaoglou, E., & da Costa, L. 2005, A&A, 436, 895 Google Scholar
Karakas, A. I., Lattanzio, J. C., & Pols, O. R. 2002, PASA, 19, 515 CrossRefGoogle Scholar
Marigo, P., Bressan, A., Nanni, A., Girardi, L., & Pumo, M. L. 2013, MNRAS, 434, 488 CrossRefGoogle Scholar
Marigo, P., Girardi, L., Bressan, A., Rosenfield, P., Aringer, B., Chen, Y., Dussin, M., Nanni, A., Pastorelli, G., Rodrigues, T. S., Trabucchi, M., Bladh, S., Dalcanton, J., Groenewegen, M. A. T., Montalbán, J., & Wood, P. R. 2017, ApJ, 835, 77 CrossRefGoogle Scholar
Mattsson, L., Wahlin, R., & Höfner, S. 2010, A&A, 509, A14.Google Scholar
Rosenfield, P., Marigo, P., Girardi, L., Dalcanton, J. J., Bressan, A., Gullieuszik, M., Weisz, D., Williams, B. F., Dolphin, A., & Aringer, B. 2014, ApJ, 790, 22.CrossRefGoogle Scholar
Rosenfield, P., Marigo, P., Girardi, L., Dalcanton, J. J., Bressan, A., Williams, B. F., & Dolphin, A. 2016, ApJ, 822, 73.CrossRefGoogle Scholar
Rubele, S., Pastorelli, G., Girardi, L., Cioni, M.-R. L., Zaggia, S., Marigo, P., Bekki, K., Bressan, A., Clementini, G., de Grijs, R., Emerson, J., Groenewegen, M. A. T., Ivanov, V. D., Muraveva, T., Nanni, A., Oliveira, J. M., Ripepi, V., Sun, N.-C., & van Loon, J. T. 2018, MNRAS, 478, 5017 CrossRefGoogle Scholar
Ruffle, P. M. E., Kemper, F., Jones, O. C., Sloan, G. C., Kraemer, K. E., Woods, P. M., Boyer, M. L., Srinivasan, S., Antoniou, V., Lagadec, E., Matsuura, M., McDonald, I., Oliveira, J. M., Sargent, B. A., Sewiéło, M., Szczerba, R., van Loon, J. T., Volk, K., & Zijlstra, A. A. 2015, MNRAS, 451, 3504 CrossRefGoogle Scholar
Schröder, K.-P. & Cuntz, M. 2005, ApJ (Letters), 630, L73 Google Scholar
Srinivasan, S., Boyer, M. L., Kemper, F., Meixner, M., Sargent, B. A., & Riebel, D. 2016, MNRAS, 457, 2814 CrossRefGoogle Scholar
Vassiliadis, E., & Wood, P. R. 1993, ApJ, 413, 641 CrossRefGoogle Scholar