Cited By
View all- Hu JSong YOmee SWei LDong RGianey S(2023)Evolutionary Machine Learning in Science and EngineeringHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_18(535-561)Online publication date: 2-Nov-2023
Evolving stellar models to find the origins of our galaxy
Pages 1129 - 1137
Abstract
After the Big Bang, it took about 200 million years before the very first stars would form - now more than 13 billion years ago. Unfortunately, we will not be able to observe these stars directly. Instead, we can observe the 'fossil' records that these stars have left behind, preserved in the oldest stars of our own galaxy. When the first stars exploded as supernovae, their ashes were dispersed and the next generation of stars formed, incorporating some of the debris. We can now measure the chemical abundances in those old stars, which is similar to a genetic fingerprint that allows us to identify the parents.
In this paper, we develop a Genetic Algorithm (GA) for identifying the 'parents' of these old stars in our galaxy. The objective is to study the now extinct first stars in the universe - what their properties were, how they lived and died, how many they were, and even how different or alike they were. The GA is evaluated on its effectiveness in finding the right combination of ashes from theoretical models. The solutions found by the GA are compared to observational data. The aim is to find out which theoretical data, i.e., abundances of chemical elements, best matches the current observations.
References
[1]
Benjamin P Abbott, Rich Abbott, TD Abbott, Fausto Acernese, Kendall Ackley, Carl Adams, Thomas Adams, Paolo Addesso, RX Adhikari, VB Adya, and others. 2017. GW170817: observation of gravitational waves from a binary neutron star inspiral. Physical Review Letters 119, 16 (2017), 161101.
[2]
B. P. Abbott, R. Abbott, T. D. Abbott, M. R. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. X. Adhikari, and et al. 2016. Observation of Gravitational Waves from a Binary Black Hole Merger. Physical Review Letters 116, 6 (Feb. 2016), 061102.
[3]
Aldeida Aleti and Lars Grunske. 2015. Test data generation with a Kalman filter-based adaptive genetic algorithm. Journal of Systems and Software 103 (2015), 343 -- 352. Special Issue.
[4]
Aldeida Aleti and Irene Moser. 2011. Predictive parameter control. In Genetic and Evolutionary Computation Conference, Proceedings. 561--568.
[5]
Aldeida Aleti and Irene Moser. 2013. Entropy-based Adaptive Range Parameter Control for Evolutionary Algorithms. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. ACM, 1501--1508.
[6]
Aldeida Aleti, Irene Moser, Indika Meedeniya, and Lars Grunske. 2014. Choosing the appropriate forecasting model for predictive parameter control. Evolutionary computation 22, 2 (2014), 319--349.
[7]
Aldeida Aleti, Irene Moser, and Sanaz Mostaghim. 2012. Adaptive Range Parameter Control. In IEEE Congress on Evolutionary Computation. IEEE, 1--8.
[8]
W. Aoki, N. Tominaga, T. C. Beers, S. Honda, and Y. S. Lee. 2014. A chemical signature of first-generation very massive stars. Science 345 (Aug. 2014), 912--915.
[9]
E. Caffau, P. Bonifacio, P. François, L. Sbordone, L. Monaco, M. Spite, F. Spite, H.-G. Ludwig, R. Cayrel, S. Zaggia, F. Hammer, S. Randich, P. Molaro, and V. Hill. 2011. An extremely primitive star in the Galactic halo. Nature 477 (Sept. 2011), 67--69.
[10]
C. L. Carilli and S. Rawlings. 2004. Motivation, key science projects, standards and assumptions. New Astronomy Reviews 48 (Dec. 2004), 979--984.
[11]
Roger Cayrel, E Depagne, M Spite, V Hill, F Spite, P François, B Plez, T Beers, F Primas, J Andersen, and others. 2004. First stars V-Abundance patterns from C to Zn and supernova yields in the early Galaxy. Astronomy & Astrophysics 416, 3 (2004), 1117--1138.
[12]
N. Christlieb, M. S. Bessell, T. C. Beers, B. Gustafsson, A. Korn, P. S. Barklem, T. Karlsson, M. Mizuno-Wiedner, and S. Rossi. 2002. A stellar relic from the early Milky Way. Nature 419 (Oct. 2002), 904--906.
[13]
N. Christlieb, B. Gustafsson, A. J. Korn, P. S. Barklem, T. C. Beers, M. S. Bessell, T. Karlsson, and M. Mizuno-Wiedner. 2004. HE 0107-5240, a Chemically Ancient Star. I. A Detailed Abundance Analysis. The Astrophysical Journal 603 (March 2004), 708--728.
[14]
Gert De Geyter, Maarten Baes, Jacopo Fritz, and Peter Camps. 2013. FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code. Astronomy & Astrophysics 550 (2013), A74.
[15]
E. Depagne, V. Hill, M. Spite, F. Spite, B. Plez, T. C. Beers, B. Barbuy, R. Cayrel, J. Andersen, P. Bonifacio, P. Franois, B. Nordstrm, and F. Primas. 2002. First Stars. II. Elemental abundances in the extremely metal-poor star CS 22949--037. A diagnostic of early massive supernovae. Astronomy and Astrophysics 390 (July 2002), 187--198.
[16]
A. Frebel, W. Aoki, N. Christlieb, H. Ando, M. Asplund, P. S. Barklem, T. C. Beers, K. Eriksson, C. Fechner, M. Y. Fujimoto, S. Honda, T. Kajino, T. Minezaki, K. Nomoto, J. E. Norris, S. G. Ryan, M. Takada-Hidai, S. Tsangarides, and Y. Yoshii. 2005. Nucleosynthetic signatures of the first stars. Nature 434 (April 2005), 871--873.
[17]
A. Heger and S. E. Woosley. 2010. Nucleosynthesis and Evolution of Massive Metal-free Stars. The Astrophysical Journal 724 (Nov. 2010), 341--373.
[18]
Annibal Hetem and Jane Gregorio-Hetem. 2007. The use of genetic algorithms to model protoplanetary discs. Monthly Notices of the Royal Astronomical Society 382, 4 (2007), 1707--1718.
[19]
S. C. Keller, M. S. Bessell, A. Frebel, A. R. Casey, M. Asplund, H. R. Jacobson, K. Lind, J. E. Norris, D. Yong, A. Heger, Z. Magic, G. S. Da Costa, B. P. Schmidt, and P. Tisserand. 2014. A single low-energy, iron-poor supernova as the source of metals in the star SMSS J031300.362670839.3. Nature 506 (2014), 463--466.
[20]
David K. Lai, Michael Bolte, Jennifer A. Johnson, Sara Lucatello, Alexander Heger, and S. E. Woosley. 2008. Detailed Abundances for 28 Metal-poor Stars: Stellar Relics in the Milky Way. The Astrophysical Journal 681 (July 2008), 1524--1556.
[21]
J. E. Norris, N. Christlieb, A. J. Korn, K. Eriksson, M. S. Bessell, T. C. Beers, L. Wisotzki, and D. Reimers. 2007. HE 0557-4840: Ultra-Metal-Poor and Carbon-Rich. The Astrophysical Journal 670 (Nov. 2007), 774--788.
[22]
Carlos Oliveira, Aldeida Aleti, Lars Grunske, and Kate Smith-Miles. 2018. Mapping the Effectiveness of Automated Test Suite Generation Techniques. IEEE Transactions on Reliability 99 (2018), 1--15.
[23]
Diego Ordóñez, Carlos Dafonte, Minia Manteiga, and Bernardino Arcay. 2011. Genetic Algorithms Applied to Spectral Index Extraction. Computational Intelligence 343 (2011), 195--207.
[24]
A. N. Pettitt and M. A. Stephens. 1977. The Kolmogorov-Smirnov Goodness-of-Fit Statistic with Discrete and Grouped Data. Technometrics 19, 2 (1977), 205--210.
[25]
Michael JD Powell. 2002. UOBYQA: unconstrained optimization by quadratic approximation. Mathematical Programming 92, 3 (2002), 555--582.
[26]
Jing Ren and Norbert Christlieb. 2011. private communication Dec. 2. N/A N/A (2011), N/A.
[27]
Jing Ren and et al. 2014. in preparation. N/A N/A (2014), N/A.
[28]
Reuven Y Rubinstein and Dirk P Kroese. 2004. The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning. Springer, Berlin, Heidelberg.
[29]
Hideyuki Umeda and Ken'ichi Nomoto. 2003. First-generation black-hole-forming supernovae and the metal abundance pattern of a very iron-poor star. Nature 422, 6934 (2003), 871--873.
Index Terms
- Evolving stellar models to find the origins of our galaxy
Recommendations
Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS
In this work, the authors employ Evolution Strategies (ES) to automatically extract a set of physical parameters, corresponding to stellar population synthesis, from a sample of galaxy spectra taken from the Sloan Digital Sky Survey (SDSS). This ...
Comments
Information & Contributors
Information
Published In
July 2019
1545 pages
ISBN:9781450361118
DOI:10.1145/3321707
- Editor:
- Manuel López-Ibáñez,
- General Chairs:
- Anne Auger,
- Thomas Stützle
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 July 2019
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- ARC Future Fellowship
- Australian Government Research TrainingProgram (RTP) Scholarship
- Laureate Fellowship
- ARC Discovery Project
Conference
GECCO '19
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 118Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)1
Reflects downloads up to 15 Oct 2024
Other Metrics
Citations
Cited By
View all- Hu JSong YOmee SWei LDong RGianey S(2023)Evolutionary Machine Learning in Science and EngineeringHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_18(535-561)Online publication date: 2-Nov-2023
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in