REVIEW 1 cited by
First measurement of $\sigma_8$ using supernova magnitudes only
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
A method was recently proposed which allows the conversion of the weak-lensing effects in the supernova Hubble diagram from noise into signal. Such signal is sensitive to the growth of structure in the universe, and in particular can be used as a measurement of $\sigma_8$ which is independent from more traditional methods such as those based on the CMB, cosmic shear or cluster abundance. We extend here that analysis to allow for intrinsic non-Gaussianities in the supernova PDF, and discuss how this can be best modelled using the Bayes Factor. Although it was shown that a precise measurement of $\sigma_8$ requires ~$10^5$ supernovae, current data already allows an important proof of principle. In particular we make use of the 732 supernovae with z < 1 of the recent JLA catalog and show that a simple treatment of intrinsic non-Gaussianities with a couple of nuisance parameters is enough for our method to yield the values $\sigma_8 = 0.84^{+0.28}_{-0.65}$ or $\sigma_8 < 1.45$ at a $2\sigma$ confidence level. This result is consistent with mock simulations and it is also in agreement with independent measurements and presents the first ever measurement of $\sigma_8$ using supernova magnitudes alone.
Forward citations
Cited by 1 Pith paper
-
On the cosmological performance of photometrically classified supernovae with machine learning
Machine-learning classification of simulated photometric supernovae retains up to 75 percent of cosmological information with SALT2 features and roughly one third with Newling or wavelet features.
Discussion (0). Continue with ORCID to comment.