Non-linear galaxy clustering in modified gravity cosmologies

We present \textsc{MG-GLAM}, a code developed for the very fast production of full $N$-body cosmological simulations in modified gravity (MG) models. We describe the implementation, numerical tests, and first results of a large suite of cosmological simulations for a wide range of viable MG models. The code is highly optimised, with a tremendous speedup of a factor of more than a hundred compared with earlier $N$-body codes, while still giving accurate predictions of the matter power spectrum and dark matter halo abundance. \textsc{MG-GLAM} is ideal for the generation of large numbers of MG simulations that can be used in the construction of mock galaxy catalogues and the production of accurate emulators for ongoing and future galaxy surveys. The coming generation of galaxy surveys will provide measurements of galaxy clustering with unprecedented accuracy and data size, which will allow us to test cosmological models at a much higher precision than previously achievable. This means that we must have more accurate theoretical predictions to compare with future observational data. As a first step toward more accurate modelling of the redshift space distortions (RSD) of small-scale galaxy clustering in modified gravity cosmologies, we investigate the validity of the so-called Skew-T (ST) probability distribution function (PDF) of halo pairwise peculiar velocities in these models. We show that combined with the streaming model, the ST PDF substantially improves the small-scale predictions by incorporating skewness and kurtosis, for both $\Lambda$ cold dark matter ($\Lambda$CDM) and two leading MG models: $f(R)$ gravity and the DGP braneworld model. The ST model reproduces the velocity PDF and redshift-space halo clustering measured from MG $N$-body simulations down to highly non-linear scales. By performing a simple Fisher analysis, we find a significant increase in constraining power to detect modifications of General Relativity by introducing small-scale information in the RSD analyses. We introduce an emulator-based halo model approach for non-linear clustering of galaxies in modified gravity cosmologies. We construct accurate emulators, i.e. simulation-based theoretical templates, using neural networks for basic halo properties than can be calculated robustly from $N$-body simulations. The dark matter halo emulators can be combined with a halo-galaxy connection model to predict the galaxy clustering statistics down to non-linear scales through the halo model

Sesame: A power spectrum emulator pipeline for beyond-Λ\LambdaCDM models

The mysterious nature of the dark sector of the $\Lambda$CDM model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. We present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond $\Lambda$CDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, $B(k,z)=P_{\text{beyond}-\Lambda \rm CDM}(k,z) / P_{\Lambda \rm CDM}(k, z)$, and then rely on existing high-quality emulators made for $\Lambda$CDM to isolate $P_{\text{beyond}-\Lambda \rm CDM}(k,z)$. Since both the $\Lambda$CDM and beyond-$\Lambda$CDM models are simulated in our approach, some of the lack of power on small scales is factored out, allowing us to extend the emulator to $k \sim 3-5\,h\,\mathrm{Mpc}^{-1}$ and still maintain good accuracy. In addition, errors from the simulation and emulation process can be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied $f(R)$ model with massive neutrinos, using approximately 3000 CPU hours of computation time, as opposed to millions of CPU hours for many existing $\Lambda$CDM emulators. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice.Comment: 11 pages, 8 figure

Inverse Geometry Design of Radiative Enclosures Using Particle Swarm Optimization Algorithms

Three different Particle Swarm Optimization (PSO) algorithms—standard PSO, stochastic PSO (SPSO) and differential evolution PSO (DEPSO)—are applied to solve the inverse geometry design problems of radiative enclosures. The design purpose is to satisfy a uniform distribution of radiative heat flux on the designed surface. The design surface is discretized into a series of control points, the PSO algorithms are used to optimize the locations of these points and the Akima cubic interpolation is utilized to approximate the changing boundary shape. The retrieval results show that PSO algorithms can be successfully applied to solve inverse geometry design problems and SPSO achieves the best performance on computational time. The influences of the number of control points and the radiative properties of the media on the retrieval geometry design results are also investigated

fkPT: constraining scale-dependent modified gravity with the full-shape galaxy power spectrum

Modified gravity models with scale-dependent linear growth typically exhibit an enhancement in the power spectrum beyond a certain scale. The conventional methods for extracting cosmological information usually involve inferring modified gravity effects via Redshift Space Distortions (RSD), particularly through the time evolution of fσ 8. However, classical galaxy RSD clustering analyses encounter difficulties in accurately capturing the spectrum's enhanced power, which is better obtained from the broad-band power spectrum. In this sense, full-shape analyses aim to consider survey data using comprehensive and precise models of the whole power spectrum. Yet, a major challenge in this approach is the slow computation of non-linear loop integrals for scale-dependent modified gravity, precluding the estimation of cosmological parameters using Markov Chain Monte Carlo methods. Based on recent studies, in this work we develop a perturbation theory tailored for Modified Gravity, or analogous scenarios introducing additional scales, such as in the presence of massive neutrinos. Our approach only needs the calculation of the scale-dependent growth rate f(k,t) and the limit of the perturbative kernels at large scales. We called this approximate technique as fk-Perturbation Theory and implemented it into the code fkpt, capable of computing the redshift space galaxy power spectrum in a fraction of a second. We validate our modeling and code with the f(R) theory MG-GLAM and General Relativity NSeries sets of simulations. The code is available at https://github.com/alejandroaviles/fkpt

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure

The SISAL database: a global resource to document oxygen and carbon isotope records from speleothems

Stable isotope records from speleothems provide information on past climate changes, most particularly information that can be used to reconstruct past changes in precipitation and atmospheric circulation. These records are increasingly being used to provide “out-of-sample” evaluations of isotope-enabled climate models. SISAL (Speleothem Isotope Synthesis and Analysis) is an international working group of the Past Global Changes (PAGES) project. The working group aims to provide a comprehensive compilation of speleothem isotope records for climate reconstruction and model evaluation. The SISAL database contains data for individual speleothems, grouped by cave system. Stable isotopes of oxygen and carbon (δ 18O, δ 13C) measurements are referenced by distance from the top or bottom of the speleothem. Additional tables provide information on dating, including information on the dates used to construct the original age model and sufficient information to assess the quality of each data set and to erect a standardized chronology across different speleothems. The metadata table provides location information, information on the full range of measurements carried out on each speleothem and information on the cave system that is relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at https://doi.org/10.17864/1947.147

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure