Quantification of High-dimensional Non-Gaussianities and Its Implication to Fisher Analysis in Cosmology.

It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can constrain the value of the cosmological parameters. However, these calculations typically rely on the assumption that the sampling distribution of the considered statistic follows a multivariate Gaussian distribution. In this work, we follow Sellentin & Heavens and use two different statistical tests to identify non-Gaussianities in different statistics such as the power spectrum, bispectrum, marked power spectrum, and wavelet scattering transform (WST). We remove the non-Gaussian components of the different statistics and perform Fisher matrix calculations with the Gaussianized statistics using Quijote simulations. We show that constraints on the parameters can change by a factor of ∼2 in some cases. We show with simple examples how statistics that do not follow a multivariate Gaussian distribution can achieve artificially tight bounds on the cosmological parameters when using the Fisher matrix formalism. We think that the non-Gaussian tests used in this work represent a powerful tool to quantify the robustness of Fisher matrix calculations and their underlying assumptions. We release the code used to compute the power spectra, bispectra, and WST that can be run on both CPUs and GPUs.

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation.

We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green's function expansion that relates the initial conditions of the simulations to its outcome at later times in the deeply nonlinear regime. We test the accuracy of this approximation by assessing its performance on well-understood simple cases that have either known exact solutions or well-understood expansions. These scenarios include spherical configurations, isolated plane waves, and two interacting plane waves: initial conditions that are very different from the Gaussian random fields used for training. We find our model generalizes well to these well-understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data. These tests also provide a useful diagnostic for finding the model's strengths and weaknesses, and identifying strategies for model improvement. We also test the model on initial conditions that contain only transverse modes, a family of modes that differ not only in their phases but also in their evolution from the longitudinal growing modes used in the training set. When the network encounters these initial conditions that are orthogonal to the training set, the model fails completely. In addition to these simple configurations, we evaluate the model's predictions for the density, displacement, and momentum power spectra with standard initial conditions for N-body simulations. We compare these summary statistics against N-body results and an approximate, fast simulation method called COLA (COmoving Lagrangian Acceleration). Our model achieves percent level accuracy at nonlinear scales of k ∼ 1 Mpc - 1 h , representing a significant improvement over COLA.

Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev-Zeldovich flux-mass scatter.

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux-cluster mass relation (YSZ - M), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines YSZ and concentration of ionized gas (cgas): M ∝ Yconc3/5 ≡ YSZ3/5(1 - A cgas). Yconc reduces the scatter in the predicted M by ∼20 - 30% for large clusters (M ≳ 1014 h-1 M⊙), as compared to using just YSZ. We show that the dependence on cgas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Yconc on clusters from CAMELS simulations and show that Yconc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

Searching for the Radiative Decay of the Cosmic Neutrino Background with Line-Intensity Mapping.

We study the possibility to use line-intensity mapping (LIM) to seek photons from the radiative decay of neutrinos in the cosmic neutrino background. The Standard Model prediction for the rate for these decays is extremely small, but it can be enhanced if new physics increases the neutrino electromagnetic moments. The decay photons will appear as an interloper of astrophysical spectral lines. We propose that the neutrino-decay line can be identified with anisotropies in LIM clustering and also with the voxel intensity distribution. Ongoing and future LIM experiments will have-depending on the neutrino hierarchy, transition, and experiment considered-a sensitivity to an effective electromagnetic transition moment ∼10^{-12}-10^{-8}(m_{i}c^{2}/0.1 eV)^{3/2}µ_{B}, where m_{i} is the mass of the decaying neutrino and µ_{B} is the Bohr magneton. This will be significantly more sensitive than cosmic microwave background spectral distortions, and it will be competitive with stellar cooling studies. As a by-product, we also report an analytic form of the one-point probability distribution function for neutrino-density fluctuations, obtained from the quijote simulations using symbolic regression.

Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure.

Cosmological neutrinos have their greatest influence in voids: These are the regions with the highest neutrino to dark matter density ratios. The marked power spectrum can be used to emphasize low-density regions over high-density regions and, therefore, is potentially much more sensitive than the power spectrum to the effects of neutrino masses. Using 22 000 N-body simulations from the Quijote suite, we quantify the information content in the marked power spectrum of the matter field and show that it outperforms the standard power spectrum by setting constraints improved by a factor larger than 2 on all cosmological parameters. The combination of marked and standard power spectra allows us to place a 4.3σ constraint on the minimum sum of the neutrino masses with a volume equal to 1 (Gpc h^{-1})^{3} and without cosmic microwave background priors. Combinations of different marked power spectra yield a 6σ constraint within the same conditions.

First Detection of Scale-Dependent Linear Halo Bias in N-Body Simulations with Massive Neutrinos.

Using N-body simulations with massive neutrino density perturbations, we detect the scale-dependent linear halo bias with high significance. This is the first time that this effect is detected in simulations containing neutrino density perturbations on all scales, confirming the same finding from separate universe simulations. The scale dependence is the result of the additional scale in the system, i.e., the massive neutrino free-streaming length, and it persists even if the bias is defined with respect to the cold dark matter plus baryon (instead of total matter) power spectrum. The separate universe approach provides a good model for the scale-dependent linear bias, and the effect is approximately 0.25f_{ν} and 0.43f_{ν} for halos with bias of 1.7 and 3.5, respectively. While the size of the effect is small, it is not insignificant in terms of f_{ν} and should therefore be included to accurately constrain neutrino mass from clustering statistics of biased tracers. More importantly, this feature is a distinct signature of free-streaming particles and cannot be mimicked by other components of the standard cosmological model.

Primordial Non-Gaussianities and Zero-Bias Tracers of the Large-Scale Structure.

We develop a new method to constrain primordial non-Gaussianities of the local kind using unclustered tracers of the large scale structure. We show that, in the limit of low noise, zero bias tracers yield large improvement over standard methods, mostly due to vanishing sampling variance. We propose a simple technique to construct such a tracer, using environmental information obtained from the original sample and validate our method with N-body simulations. Our results indicate that σ_{f_{NL}^{loc}}≃1 can be reached using only information on a single tracer of sufficiently high number density.