scholarly journals Neural physical engines for inferring the halo mass distribution function

2020 ◽  
Vol 494 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Tom Charnock ◽  
Guilhem Lavaux ◽  
Benjamin D Wandelt ◽  
Supranta Sarma Boruah ◽  
Jens Jasche ◽  
...  
Keyword(s):  
Dark Matter ◽  
Distribution Function ◽  
Mass Distribution ◽  
Large Scale ◽  
Initial Conditions ◽  
Matter Density ◽  
Practical Reasons ◽  
Dark Matter Density ◽  
Bias Model ◽  
Mass Distribution Function

ABSTRACT An ambitious goal in cosmology is to forward model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ∼Mpc scales and the distribution of dark matter haloes (used as a proxy for galaxies) in the same region. Here, we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field while inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded borg (Bayesian Origin Reconstruction from Galaxies) inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. In preliminary results, we notice there could potentially be orders of magnitude improvement in modelling compared to classical biasing techniques.

scholarly journals LyMAS: PREDICTING LARGE-SCALE Lyα FOREST STATISTICS FROM THE DARK MATTER DENSITY FIELD

2014 ◽  
Vol 784 (1) ◽  
pp. 11 ◽  
Author(s):  
Sébastien Peirani ◽  
David H. Weinberg ◽  
Stéphane Colombi ◽  
Jérémy Blaizot ◽  
Yohan Dubois ◽  
...  
Keyword(s):  
Dark Matter ◽  
Large Scale ◽  
Matter Density ◽  
Density Field ◽  
Dark Matter Density ◽  
Forest Statistics

scholarly journals Gamma and Beta Model of Growing and Rotating Planck Ball

2019 ◽  
Author(s):  
Satya Seshavatharam U.V ◽  
S. Lakshminarayana
Keyword(s):  
Dark Matter ◽  
Large Scale ◽  
Density Ratio ◽  
Planck Scale ◽  
Matter Density ◽  
Cosmic Acceleration ◽  
Expansion Velocity ◽  
Dark Matter Density ◽  
Density Ratios ◽  
Baby Universe

With reference to Planck scale, Mach’s relation, increasing support for large scale cosmic anisotropy and preferred directions and by introducing two new parameters Gamma and Beta, right from the beginning of Planck scale, we make an attempt to estimate ordinary matter density ratio, dark matter density ratio, mass, radius, temperature, age and expansion velocity (from and about the baby universe in all directions). We would like suggest that, from the beginning of Planck scale, 1) Dark matter can be considered as a kind of cosmic foam responsible for formation of galaxies. 2) Cosmic angular velocity is directly proportional to squared cosmic temperature. 3) Ratio of critical temperature to actual temperature plays a heuristic role in understanding ordinary and dark matter density ratios. 4) Cosmic expansion velocity increases with decreasing total matter density ratio. 5) There is no need to consider dark energy for understanding cosmic acceleration.

scholarly journals Low-energy probes of no-scale SU(5) super-GUTs

2021 ◽  
Vol 81 (2) ◽  
Author(s):  
John Ellis ◽  
Jason L. Evans ◽  
Natsumi Nagata ◽  
Keith A. Olive ◽  
L. Velasco-Sevilla
Keyword(s):  
Dark Matter ◽  
Supersymmetry Breaking ◽  
Higgs Mass ◽  
Cold Dark Matter ◽  
Initial Conditions ◽  
Branching Ratio ◽  
Proton Decay ◽  
Matter Density ◽  
Dark Matter Density ◽  
Soft Supersymmetry Breaking

AbstractWe explore the possible values of the $$\mu \rightarrow e \gamma $$ μ → e γ branching ratio, $$\text {BR}(\mu \rightarrow e\gamma )$$ BR ( μ → e γ ) , and the electron dipole moment (eEDM), $$d_e$$ d e , in no-scale SU(5) super-GUT models with the boundary conditions that soft supersymmetry-breaking matter scalar masses vanish at some high input scale, $$M_\mathrm{in}$$ M in , above the GUT scale, $$M_{\mathrm{GUT}}$$ M GUT . We take into account the constraints from the cosmological cold dark matter density, $$\Omega _{CDM} h^2$$ Ω CDM h 2 , the Higgs mass, $$M_h$$ M h , and the experimental lower limit on the lifetime for $$p \rightarrow K^+ \bar{\nu }$$ p → K + ν ¯ , the dominant proton decay mode in these super-GUT models. Reconciling this limit with $$\Omega _{CDM} h^2$$ Ω CDM h 2 and $$M_h$$ M h requires the Higgs field responsible for the charge-2/3 quark masses to be twisted, and possibly also that responsible for the charge-1/3 and charged-lepton masses, with model-dependent soft supersymmetry-breaking masses. We consider six possible models for the super-GUT initial conditions, and two possible choices for quark flavor mixing, contrasting their predictions for proton decay with versions of the models in which mixing effects are neglected. We find that $$\tau \left( p\rightarrow K^+ \bar{\nu }\right) $$ τ p → K + ν ¯ may be accessible to the upcoming Hyper-Kamiokande experiment, whereas all the models predict $$\text {BR}(\mu \rightarrow e\gamma )$$ BR ( μ → e γ ) and $$d_e$$ d e below the current and prospective future experimental sensitivities or both flavor choices, when the dark matter density, Higgs mass and current proton decay constraints are taken into account. However, there are limited regions with one of the flavor choices in two of the models where $$\mu \rightarrow e$$ μ → e conversion on a heavy nucleus may be observable in the future. Our results indicate that there is no supersymmetric flavor problem in the class of no-scale models we consider.

scholarly journals One simulation to have them all: performance of the Bias Assignment Method against N-body simulations

2019 ◽  
Author(s):  
A Balaguera-Antolínez ◽  
Francisco-Shu Kitaura ◽  
M Pellejero-Ibáñez ◽  
Martha Lippich ◽  
Cheng Zhao ◽  
...  
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Initial Conditions ◽  
Real Space ◽  
Matter Density ◽  
High Mass ◽  
Parameter Uncertainties ◽  
Dark Matter Density ◽  
Assignment Method ◽  
Reference Simulation

Abstract In this paper we demonstrate that the information encoded in one single (sufficiently large) N-body simulation can be used to reproduce arbitrary numbers of halo catalogues, using approximated realisations of dark matter density fields with different initial conditions. To this end we use as a reference one realisation (from an ensemble of 300) of the Minerva N-body simulations and the recently published Bias Assignment Method to extract the local and non-local bias linking the halo to the dark matter distribution. We use an approximate (and fast) gravity solver to generate 300 dark matter density fields from the down-sampled initial conditions of the reference simulation and sample each of these fields using the halo-bias and a kernel, both calibrated from the arbitrarily chosen realisation of the reference simulation. We show that the power spectrum, its variance and the three-point statistics are reproduced within $\sim 2\%$ (up to k ∼ 1.0 h Mpc−1), $\sim 5-10\%$ and $\sim 10\%$, respectively. Using a model for the real space power spectrum (with three free bias parameters), we show that the covariance matrices obtained from our procedure lead to parameter uncertainties that are compatible within $\sim 10\%$ with respect to those derived from the reference covariance matrix, and motivate approaches that can help to reduce these differences to $\sim 1\%$. Our method has the potential to learn from one simulation with moderate volumes and high-mass resolution and extrapolate the information of the bias and the kernel to larger volumes, making it ideal for the construction of mock catalogues for present and forthcoming observational campaigns such as Euclid or DESI.

scholarly journals The AGN–galaxy–halo connection: The distribution of AGN host halo masses to z = 2.5

2021 ◽  
Author(s):  
James Aird ◽  
Alison L Coil
Keyword(s):  
Dark Matter ◽  
Distribution Function ◽  
Mass Distribution ◽  
Accretion Rate ◽  
Cosmic Time ◽  
Stellar Mass ◽  
Dark Matter Halo ◽  
Galaxy Halo ◽  
Halo Masses ◽  
Mass Distribution Function

Abstract It is widely reported, based on clustering measurements of observed active galactic nuclei (AGN) samples, that AGN reside in similar mass host dark matter halos across the bulk of cosmic time, with log $\mathcal {M}/\mathcal {M}_{\odot }\sim 12.5-13.0$ to z ∼ 2.5. We show that this is due in part to the AGN fraction in galaxies rising with increasing stellar mass, combined with AGN observational selection effects that exacerbate this trend. Here, we use AGN specific accretion rate distribution functions determined as a function of stellar mass and redshift for star-forming and quiescent galaxies separately, combined with the latest galaxy-halo connection models, to determine the parent and sub-halo mass distribution function of AGN to various observational limits. We find that while the median (sub-)halo mass of AGN, $\approx 10^{12}\mathcal {M}_{\odot }$, is fairly constant with luminosity, specific accretion rate, and redshift, the full halo mass distribution function is broad, spanning several orders of magnitude. We show that widely used methods to infer a typical dark matter halo mass based on an observed AGN clustering amplitude can result in biased, systematically high host halo masses. While the AGN satellite fraction rises with increasing parent halo mass, we find that the central galaxy is often not an AGN. Our results elucidate the physical causes for the apparent uniformity of AGN host halos across cosmic time and underscore the importance of accounting for AGN selection biases when interpreting observational AGN clustering results. We further show that AGN clustering is most easily interpreted in terms of the relative bias to galaxy samples, not from absolute bias measurements alone.

scholarly journals Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys

2019 ◽  
Vol 624 ◽  
pp. A115 ◽  
Author(s):  
Natalia Porqueres ◽  
Doogesh Kodi Ramanah ◽  
Jens Jasche ◽  
Guilhem Lavaux
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Large Scale ◽  
Scale Effects ◽  
Power Spectra ◽  
Matter Density ◽  
Data Set ◽  
Dark Matter Density ◽  
Matter Power Spectrum ◽  
Robust Likelihood

The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce erroneous large-scale effects in recovered power spectra and inferred dark matter density fields. In this work, we present an effective solution to this problem in the form of a robust likelihood designed to account for effects due to unknown foreground and target contaminations. Conceptually, this robust likelihood marginalizes over the unknown large-scale contamination amplitudes. We showcase the effectiveness of this novel likelihood via an application to a mock SDSS-III data set subject to dust extinction contamination. In order to illustrate the performance of our proposed likelihood, we infer the underlying dark-matter density field and reconstruct the matter power spectrum, being maximally agnostic about the foregrounds. The results are compared to those of an analysis with a standard Poissonian likelihood, as typically used in modern large-scale structure analyses. While the standard Poissonian analysis yields excessive power for large-scale modes and introduces an overall bias in the power spectrum, our likelihood provides unbiased estimates of the matter power spectrum over the entire range of Fourier modes considered in this work. Further, we demonstrate that our approach accurately accounts for and corrects the effects of unknown foreground contaminations when inferring three-dimensional density fields. Robust likelihood approaches, as presented in this work, will be crucial to control unknown systematic error and maximize the outcome of the decadal surveys.

scholarly journals Probing the dark matter density evolution law with large scale structures

2021 ◽  
Vol 81 (7) ◽  
Author(s):  
Kamal Bora ◽  
R. F. L. Holanda ◽  
Shantanu Desai
Keyword(s):  
Dark Matter ◽  
Gravitational Lensing ◽  
Large Scale ◽  
Galaxy Cluster ◽  
Matter Density ◽  
Evolution Law ◽  
Large Scale Structures ◽  
Dark Matter Density ◽  
Scale Structures ◽  
Rule Out

AbstractWe propose a new method to explore a possible departure from the standard time evolution law for the dark matter density. We looked for a violation of this law by using a deformed evolution law, given by $$\rho _c(z) \propto (1+z)^{3+\epsilon }$$ ρ c ( z ) ∝ ( 1 + z ) 3 + ϵ , and then constrain $$\epsilon $$ ϵ . The dataset used for this purpose consists of Strong Gravitational Lensing data obtained from SLOAN Lens ACS, BOSS Emission-line Lens Survey, Strong Legacy Survey SL2S, and SLACS; along with galaxy cluster X-ray gas mass fraction measurements obtained using the Chandra Telescope. Our analyses show that $$\epsilon $$ ϵ is consistent with zero within 1 $$\sigma $$ σ c.l., but the current dataset cannot rule out with high confidence level interacting models of dark matter and dark energy.

scholarly journals Bayesian cosmic density field inference from redshift space dark matter maps

2019 ◽  
Vol 488 (2) ◽  
pp. 2573-2604 ◽  
Author(s):  
E G Patrick Bos ◽  
Francisco-Shu Kitaura ◽  
Rien van de Weygaert
Keyword(s):  
Dark Matter ◽  
Large Scale ◽  
Analytic Solution ◽  
Power Spectra ◽  
Matter Density ◽  
Density Field ◽  
Distortion Correction ◽  
Time Step ◽  
Intensity Mapping ◽  
Dark Matter Density

Abstract We present a self-consistent Bayesian formalism to sample the primordial density fields compatible with a set of dark matter density tracers after a cosmic evolution observed in redshift space. Previous works on density reconstruction did not self-consistently consider redshift space distortions or included an additional iterative distortion correction step. We present here the analytic solution of coherent flows within a Hamiltonian Monte Carlo posterior sampling of the primordial density field. We test our method within the Zel’dovich approximation, presenting also an analytic solution including tidal fields and spherical collapse on small scales. Our resulting reconstructed fields are isotropic and their power spectra are unbiased compared to the true field defined by our mock observations. Novel algorithmic implementations are introduced regarding the mass assignment kernels when defining the dark matter density field and optimization of the time-step in the Hamiltonian equations of motions. Our algorithm, dubbed barcode, promises to be specially suited for analysis of the dark matter cosmic web down to scales of a few megaparsecs. This large-scale structure is implied by the observed spatial distribution of galaxy clusters – such as obtained from X-ray, Sunyaev–Zel’dovich, or weak lensing surveys – as well as that of the intergalactic medium sampled by the Ly α forest or perhaps even by deep hydrogen intensity mapping. In these cases, virialized motions are negligible, and the tracers cannot be modelled as point-like objects. It could be used in all of these contexts as a baryon acoustic oscillation reconstruction algorithm.

scholarly journals The anisotropy of the power spectrum in periodic cosmological simulations

2021 ◽  
Vol 503 (4) ◽  
pp. 5638-5645
Author(s):  
Gábor Rácz ◽  
István Szapudi ◽  
István Csabai ◽  
László Dobos
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Large Scale ◽  
Cold Dark Matter ◽  
Initial Conditions ◽  
Power Spectra ◽  
Cosmological Simulations ◽  
Spherical Collapse ◽  
Lower Amplitude ◽  
Hydrodynamical Simulations

ABSTRACT The classical gravitational force on a torus is anisotropic and always lower than Newton’s 1/r2 law. We demonstrate the effects of periodicity in dark matter only N-body simulations of spherical collapse and standard Lambda cold dark matter (ΛCDM) initial conditions. Periodic boundary conditions cause an overall negative and anisotropic bias in cosmological simulations of cosmic structure formation. The lower amplitude of power spectra of small periodic simulations is a consequence of the missing large-scale modes and the equally important smaller periodic forces. The effect is most significant when the largest mildly non-linear scales are comparable to the linear size of the simulation box, as often is the case for high-resolution hydrodynamical simulations. Spherical collapse morphs into a shape similar to an octahedron. The anisotropic growth distorts the large-scale ΛCDM dark matter structures. We introduce the direction-dependent power spectrum invariant under the octahedral group of the simulation volume and show that the results break spherical symmetry.

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