Journal
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
Volume -, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1475-7516/2021/01/009
Keywords
galaxy clustering; neutrino masses from cosmology
Funding
- INFN INDARK PD51 grant
- ASI-INAF [2017-14-H.0]
- WFIRST program [NNG26PJ30C, NNN12AA01C]
- Department of Energy [DE-SC0009946]
Ask authors/readers for more resources
This study investigates the cosmological properties of massive neutrinos using N-body simulations and examines the impact of neutrino-induced scale-dependent growth on the position of the linear point (LP). The findings suggest that LP remains a reliable standard ruler for measuring cosmic distances even in the presence of massive neutrinos, providing a potential tool for constraining neutrino masses.
The linear point (LP), defined as the mid-point between the dip and the peak of the two-point clustering correlation function (TPCF), has been shown to be an excellent standard ruler for cosmology. In fact, it is nearly redshift-independent, being weakly sensitive to non-linearities, scale-dependent halo bias and redshift-space distortions. So far, these findings were tested assuming that neutrinos are massless; in this paper we extend the analysis to massive-neutrino cosmologies. In particular, we examine if the scale-dependent growth induced by neutrinos affects the LP position and if it is possible to detect the neutrino masses using the shift of the LP compared to the massless-neutrino case. For our purposes, we employ two sets of state-of-the-art N-body simulations with massive neutrinos. For each of them we measure the TPCF of cold dark matter (CDM) and halos and, to estimate the LP, fit the TPCF with a model-independent parametric fit in the range of scales of the Baryon Acoustic Oscillations (BAO). Overall, we find that the LP retains its features as a standard ruler even when neutrinos are massive. The cosmic distances measured with the LP can therefore be employed to constrain the neutrino mass.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available