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This document introduces the exciting and fundamentally new science and astronomy that the European New Gravitational Wave Observatory (NGO) mission (derived from the previous LISA proposal) will deliver. The mission (which we will refer to by its informal name eLISA) will survey for the first time the low-frequency gravitational wave band (about 0.1 mHz to 1 Hz), with sufficient sensitivity to detect interesting individual astrophysical sources out to z = 15. The eLISA mission will discover and study a variety of cosmic events and systems with high sensitivity: coalescences of massive black holes binaries, brought together by galaxy mergers; mergers of earlier, less-massive black holes during the epoch of hierarchical galaxy and black-hole growth; stellar-mass black holes and compact stars in orbits just skimming the horizons of massive black holes in galactic nuclei of the present era; extremely compact white dwarf binaries in our Galaxy, a rich source of information about binary evolution and about future Type Ia supernovae; and possibly most interesting of all, the uncertain and unpredicted sources, for example relics of inflation and of the symmetry-breaking epoch directly after the Big Bang. eLISAs measurements will allow detailed studies of these signals with high signal-to-noise ratio, addressing most of the key scientific questions raised by ESAs Cosmic Vision programme in the areas of astrophysics and cosmology. They will also provide stringent tests of general relativity in the strong-field dynamical regime, which cannot be probed in any other way. This document not only describes the science but also gives an overview on the mission design and orbits.
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Line-Intensity Mapping is an emerging technique which promises new insights into the evolution of the Universe, from star formation at low redshifts to the epoch of reionization and cosmic dawn. It measures the integrated emission of atomic and molecular spectral lines from galaxies and the intergalactic medium over a broad range of frequencies, using instruments with aperture requirements that are greatly relaxed relative to surveys for single objects. A coordinated, comprehensive, multi-line intensity-mapping experimental effort can efficiently probe over 80% of the volume of the observable Universe - a feat beyond the reach of other methods. Line-intensity mapping will uniquely address a wide array of pressing mysteries in galaxy evolution, cosmology, and fundamental physics. Among them are the cosmic history of star formation and galaxy evolution, the compositions of the interstellar and intergalactic media, the physical processes that take place during the epoch of reionization, cosmological inflation, the validity of Einsteins gravity theory on the largest scales, the nature of dark energy and the origin of dark matter.
Some recent developments concerning the role of strange quark matter for astrophysical systems and the QCD phase transition in the early universe are addressed. Causality constraints of the soft nuclear equation of state as extracted from subthreshold kaon production in heavy-ion collisions are used to derive an upper mass limit for compact stars. The interplay between the viscosity of strange quark matter and the gravitational wave emission from rotation-powered pulsars are outlined. The flux of strange quark matter nuggets in cosmic rays is put in perspective with a detailed numerical investigation of the merger of two strange stars. Finally, we discuss a novel scenario for the QCD phase transition in the early universe, which allows for a small inflationary period due to a pronounced first order phase transition at large baryochemical potential.
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Omega_m$, $sigma_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.
We are learning much about how structure forms, in particular how clusters as nodes in the cosmic web evolve and accrete matter, and about the physical processes within these objects. In the next decade, the study of clusters will enable us to tackle important questions regarding the nature of Dark Matter and Dark Energy, how clusters co-evolve with super-massive black holes at their centers, and to advance our knowledge about fundamental plasma astrophysics. This science white paper outlines the key questions and research opportunities in cluster astrophysics that are emerging in the coming decade and beyond, and serves as an overview to other cluster related white papers.
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNN), to simultaneously estimate the astrophysical and cosmological parameters from 21cm maps from semi-numerical simulations. We further convert the simulated 21cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (f$_{rm esc}$), the ionizing emissivity power dependence on halo mass ($C_{rm ion}$) and the ionizing emissivity redshift evolution index ($D_{rm ion}$), and three cosmological parameters, namely the matter density parameter ($Omega_{m}$), the dimensionless Hubble constant ($h$), and the matter fluctuation amplitude ($sigma_{8}$), from 21cm maps at several redshifts. With the presence of noise from SKA, our designed CNNs are still able to recover these astrophysical and cosmological parameters with great accuracy ($R^{2} > 92%$), improving to $R^{2} > 99%$ towards low redshift and low neutral fraction values. Our results show that future 21cm observations can play a key role to break degeneracy between models and tightly constrain the astrophysical and cosmological parameters, using only few frequency channels.
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