cosmology

First author: Bonny Y. Wang Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES dataset, where every catalog contains an average of 11,000 voids from a volume of $1~(h^{-1}{\rm Gpc})^3$.

First author: Davide Racco The era of Gravitational-Wave (GW) astronomy will grant the detection of the astrophysical GW background from unresolved mergers of binary black holes, and the prospect of probing the presence of primordial GW backgrounds. In particular, the low-frequency tail of the GW spectrum for causally-generated primordial signals (like a phase transition) offers an excellent opportunity to measure unambiguously cosmological parameters as the equation of state of the universe, or free-streaming particles at epochs well before recombination.

First author: M. Dai Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low-$z$ or poorly calibrated $U$-band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data.

First author: L. Balkenhol We present a sample-variance-limited measurement of the temperature power spectrum ($TT$) of the cosmic microwave background (CMB) using observations of a $\sim! 1500 ,\mathrm{deg}^2$ field made by SPT-3G in 2018. We report multifrequency power spectrum measurements at 95, 150, and 220GHz covering the angular multipole range $750 \leq \ell < 3000$. We combine this $TT$ measurement with the published polarization power spectrum measurements from the 2018 observing season and update their associated covariance matrix to complete the SPT-3G 2018 $TT/TE/EE$ data set.

First author: Bartjan van Tent Bouncing models of cosmology, as they arise e.g. in loop quantum cosmology, can generate close-to-scale-invariant fluctuation spectra as observed in the Cosmic Microwave Background (CMB). However, they are typically not Gaussian and also generate a bispectrum. It was proposed that these models can help to mitigate the large-scale anomalies of the CMB by considering large non-Gaussianities on very large scales, which decay exponentially on sub-horizon scales.

First author: Nikita Lovyagin The James Webb Space Telescope (JWST), which has recently become operational, is capable of detecting objects at record-breaking redshifts, $z \gtrsim 15$. This is a crucial advance for observational cosmology, as at these redshifts the differences between alternative cosmological models manifest themselves in the most obvious way. In recent years, some observational hints have emerged indicating that the Standard Cosmological Model could require correcting. One of these hints is related to the discovery of remote galaxies whose redshifts correspond to the very young Universe (less than one billion years after the Big Bang) but which are similar to nearby galaxies.

First author: Divij Sharma Double source lensing provides a dimensionless ratio of distance ratios, a “remote viewing” of cosmology through distances relative to the gravitational lens, beyond the observer. We use this to test the cosmological framework, particularly with respect to spatial curvature and the distance duality relation. We derive a consistency equation for constant spatial curvature, allowing not only the investigation of flat vs curved but of the Friedmann-Lema^itre-Robertson-Walker framework itself.

First author: Dheeraj R. Pasham A black hole can launch a powerful relativistic jet after it tidally disrupts a star. If this jet fortuitously aligns with our line of sight, the overall brightness is Doppler boosted by several orders of magnitude. Consequently, such on-axis relativistic tidal disruption events (TDEs) have the potential to unveil cosmological (redshift $z>$1) quiescent black holes and are ideal test beds to understand the radiative mechanisms operating in super-Eddington jets.

First author: Adam Rouhiainen Fields in cosmology, such as the matter distribution, are observed by experiments up to experimental noise. The first step in cosmological data analysis is usually to de-noise the observed field using an analytic or simulation driven prior. On large enough scales, such fields are Gaussian, and the de-noising step is known as Wiener filtering. However, on smaller scales probed by upcoming experiments, a Gaussian prior is substantially sub-optimal because the true field distribution is very non-Gaussian.

First author: Sotiris Anagnostidis In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. These surveys have so far mostly been analysed with two-point statistics, such as power spectra and correlation functions. The usage of these summary statistics is best justified on large scales, where the density field is linear and Gaussian.