Progenitor-age dependence of type Ia supernova luminosity and its cosmological implication

Empirically standardised peak luminosity of Type Ia supernovae(SNe Ia)as a standard candle has become one of the most powerful probes of the expansion history of the late universe.Although the existence of such a consistent peak luminosity could be interpreted as a consequence of the fixed critical Chandrasekhar mass at which a carbon-oxygen white dwarf explodes,there is growing evidence for a more complex environmental dependence to the SN Ia luminosity beyond the current understanding of the SN Ia physics.

Can non-standard recombination resolve the Hubble tension?

The inconsistent Hubble constant values derived from cosmic microwave background(CMB) observations and from local distance-ladder measurements may suggest new physics beyond the standard ΛCDM paradigm. It has been found in earlier studies that, at least phenomenologically, non-standard recombination histories can reduce the ≥4σ Hubble tension to ~ 2σ.Following this path, we vary physical and phenomenological parameters in RECFAST, the standard code to compute ionization history of the universe, to explore possible physics beyond standard recombination. We find that the CMB constraint on the Hubble constant is sensitive to the hydrogen ionization energy and 2 s → 1 s two-photon decay rate, both of which are atomic constants, and is insensitive to other details of recombination. Thus, the Hubble tension is very robust against perturbations of recombination history, unless exotic physics modifies the atomic constants during the recombination epoch.

Cosmological parameter estimation from large-scale structure deep learning

We propose a light-weight deep convolutional neural network(CNN)to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.The training set is based on 465 realizations of a cubic box with a side length of 256 h-1 Mpc,sampled with 1283 particles interpolated over a cubic grid of 1283 voxels.These volumes have cosmological parameters varying within the flatΛCDM parameter space of 0.16≤?m≤0.46 and 2.0≤109 As≤2.3.The neural network takes as an input cubes with 32^3 oxels and has three convolution layers,three dense layers,together with some batch normalization and pooling layers.In the final predictions from the network we find a 2.5%bias on the primordial amplitudeσ8 that cannot easily be resolved by continued training.We correct this bias to obtain unprecedented accuracy in the cosmological parameter estimation with statistical uncertainties ofδ?m=0.0015 andδσ8=0.0029,which are several times better than the results of previous CNN works.Compared with a 2-point analysis method using the clustering region of 0-130 and 10-130 h-1 Mpc,the CNN constraints are several times and an order of magnitude more precise,respectively.Finally,we conduct preliminary checks of the error-tolerance abilities of the neural network,and find that it exhibits robustness against smoothing,masking,random noise,global variation,rotation,reflection,and simulation resolution.Those effects are well understood in typical clustering analysis,but had not been tested before for the CNN approach.Our work shows that CNN can be more promising than people expected in deriving tight cosmological constraints from the cosmic large scale structure.