Supernova Cosmology for the 21st Century

How I Learnt to Stop Worrying About Likelihoods and Train a Neural Network

Konstantin Karchev (Autor)

Buch | Hardcover

XII, 199 Seiten

2026
Springer International Publishing (Verlag)
978-3-032-15071-4 (ISBN)

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This thesis breaks new ground in supernova type Ia cosmology, developing novel and powerful machine-learning methods scalable to the next generation of astronomical surveys. It demonstrates the feasibility of a fully simulation-based approach to inference, which overcomes the limitations of current methods while increasing the efficiency (and speed) of cosmological inference by orders of magnitude from upcoming large samples of objects. Combining advances in machine learning, numerical modelling, and physical insight, this work provides a much-needed bridge between cosmology and data science. On top of its exceptional methodological impact, the thesis itself is an outstanding product: it is written to the highest scientific and editorial standard, with exceptional quality of figures and graphs, and demonstrating superb command of statistics, machine learning, astrophysics, and cosmology. It is a precious resource for anybody interested in learning, in a concise and accessible yet rigorous manner, the state-of-the-art in supernova type Ia cosmology and modern inference methodologies in general.

Konstantin Karchev obtained a Bachelor's degree in Bath, UK and a Master's in gravitation and astroparticle physics at the University of Amsterdam before pursuing a doctoral degree at SISSA, Trieste under the supervision of prof. Roberto Trotta on the development of cutting-edge machine-learning methods for supernova cosmology. He has also authored several open-source scientific packages and contributed to research in strong gravitational lensing and the study of exoplanets, addressing the challenges of big and detailed astronomical data sets. Finally, Konstantin has been involved in several outreach and teaching activities, and shows a strong affinity for scientific visualisation and graphical design.

Preface.- Bayesian inference.- Neural simulation-based inference.- Neural simulation-based model selection.- Developments in hierarchical SBI.- Supernova cosmology for philosophers.- Supernova cosmology for Nobel laureates. - Supernova cosmology for data scientists.- Supernova cosmology for statisticians.- Clipppy: probabilistic programming.- torch: accelerating physics.- SLiCsim: light curves for the ML era.- SIDE-real.- SimSIMS.- SICRET.- RESSET.- CIGaRS.- Epilogue.- Appendices: Simulation-based hierarchical truncated inference.

Erscheint lt. Verlag	17.4.2026
Reihe/Serie	Springer Theses
Vorwort	Roberto Trotta
Zusatzinfo	XII, 199 p. 50 illus., 44 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	210 x 279 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Statistik
	Naturwissenschaften ► Physik / Astronomie ► Astronomie / Astrophysik
Schlagworte	Bayesian hierarchical modelling • Bayesian inference • Bayesian model comparison • Cosmology • machine learning • neural network • neural ratio estimation • SBI • Simulation-based inference • SN Ia • standard candle • Type Ia supernova
ISBN-10	3-032-15071-X / 303215071X
ISBN-13	978-3-032-15071-4 / 9783032150714
Zustand	Neuware