Imperial College London
Portrait Picture

ProfessorRobertoTrotta

Faculty of Natural Sciences, Department of Physics

Visiting Professor
 
 
 
//

Contact

 

+44 (0)20 7594 7793r.trotta Website CV

 
 
//

Assistant

 

Mrs Sheila Ekudo +44 (0)20 7594 2086

 
//

Location

 

1009Blackett LaboratorySouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Karchev:2023:mnras/stac3785,
author = {Karchev, K and Trotta, R and Weniger, C},
doi = {mnras/stac3785},
journal = {Monthly Notices of the Royal Astronomical Society},
pages = {1056--1072},
title = {SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation},
url = {http://dx.doi.org/10.1093/mnras/stac3785},
volume = {520},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Type Ia supernovae (SNe Ia), standardizable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large data sets, are limited to simplified probabilistic descriptions, and must explicitly sample a high-dimensional latent posterior to infer the few parameters of interest, which makes them inefficient. Marginal likelihood-free inference, on the other hand, is based on forward simulations of data, and thus can fully account for complicated redshift uncertainties, contamination from non-SN Ia sources, selection effects, and a realistic instrumental model. All latent parameters, including instrumental and survey-related ones, per object and population-level properties, are implicitly marginalized, while the cosmological parameters of interest are inferred directly. As a proof of concept, we apply truncated marginal neural ratio estimation (TMNRE), a form of marginal likelihood-free inference, to BAHAMAS, a Bayesian hierarchical model for SALT parameters. We verify that TMNRE produces unbiased and precise posteriors for cosmological parameters from up to 100 000 SNe Ia. With minimal additional effort, we train a network to infer simultaneously the
AU  - Karchev,K
AU  - Trotta,R
AU  - Weniger,C
DO  - mnras/stac3785
EP  - 1072
PY  - 2023///
SN  - 0035-8711
SP  - 1056
TI  - SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation
T2  - Monthly Notices of the Royal Astronomical Society
UR  - http://dx.doi.org/10.1093/mnras/stac3785
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000926956600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://academic.oup.com/mnras/article/520/1/1056/6965837
UR  - http://hdl.handle.net/10044/1/109396
VL  - 520
ER  -
Download