Effect of Static Bifurcation on Logical Stochastic Resonance in a Symmetric Bistable System

In previous research works, logical stochastic resonance (LSR) was reported to frequently occur in an asymmetric bistable system, where the bias parameter is the key factor to make the LSR appear. In this work, we investigate the effect of different anharmonic periodic signals on the pitchfork and saddle-node bifurcations in a symmetric bistable system. We focus on the relationship between the static bifurcation and LSR. We use both numerical and circuit simulations to analyze some interesting phenomena. Like the bias parameter, some anharmonic periodic signals also break the symmetry of the symmetric bistable system and lead to the saddle-node bifurcation. The anharmonic periodic signal with a constant term in its expanded Fourier series induces a reliable LSR in the symmetric bistable system. The key factor of LSR is the saddle-node bifurcation which implies the asymmetry of the system. Here, we replace the bias parameter by choosing an anharmonic periodic signal and make the LSR occur in a different way.

Contributions from primordial non-Gaussianity and general relativity to the galaxy power spectrum

We compute the real space galaxy power spectrum, including the leading order effects of General Relativity and primordial non-Gaussianity from the f NL and g NL parameters. Such contributions come from the one-loop matter power spectrum terms dominant at large scales, and from the factors of the non-linear bias parameter b NL (akin to the Newtonian b ϕ). We assess the detectability of these contributions in Stage-IV surveys. In particular, we note that specific values of the bias parameter may erase the primordial and relativistic contributions to the configuration space power spectrum.

Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the QUIJOTE simulations, finding excellent agreement on scales k ≲ 0.25 h Mpc-1. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.

MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION

Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation. Missing values can be found in any type of data, included count data that has Poisson distributed. One solution to overcome that problem is applying multiple imputation techniques. The multiple imputation technique for the case of count data consists of three main stages, namely the imputation, the analysis, and pooling parameter. The use of the normal distribution refers to the sampling distribution using the central limit theorem for discrete distributions. This study is also equipped with numerical simulations which aim to compare accuracy based on the resulting bias value. Based on the study, the solutions proposed to overcome the missing values in the count data yield satisfactory results. This is indicated by the size of the bias parameter estimate is small. But the bias value tends to increase with increasing percentage of observation of missing values and when the parameter values are small.

Phase transitions for spatially extended pinning

We consider a directed polymer of length N interacting with a linear interface. The monomers carry i.i.d. random charges $$(\omega _i)_{i=1}^N$$ ( ω i ) i = 1 N taking values in $${\mathbb {R}}$$ R with mean zero and variance one. Each monomer i contributes an energy $$(\beta \omega _i-h)\varphi (S_i)$$ ( β ω i - h ) φ ( S i ) to the interaction Hamiltonian, where $$S_i \in {\mathbb {Z}}$$ S i ∈ Z is the height of monomer i with respect to the interface, $$\varphi :\,{\mathbb {Z}}\rightarrow [0,\infty )$$ φ : Z → [ 0 , ∞ ) is the interaction potential, $$\beta \in [0,\infty )$$ β ∈ [ 0 , ∞ ) is the inverse temperature, and $$h \in {\mathbb {R}}$$ h ∈ R is the charge bias parameter. The configurations of the polymer are weighted according to the Gibbs measure associated with the interaction Hamiltonian, where the reference measure is given by a Markov chain on $${\mathbb {Z}}$$ Z . We study both the quenched and the annealed free energy per monomer in the limit as $$N\rightarrow \infty $$ N → ∞ . We show that each exhibits a phase transition along a critical curve in the $$(\beta ,h)$$ ( β , h ) -plane, separating a localized phase (where the polymer stays close to the interface) from a delocalized phase (where the polymer wanders away from the interface). We derive variational formulas for the critical curves and we obtain upper and lower bounds on the quenched critical curve in terms of the annealed critical curve. In addition, for the special case where the reference measure is given by a Bessel random walk, we derive the scaling limit of the annealed free energy as $$\beta ,h \downarrow 0$$ β , h ↓ 0 in three different regimes for the tail exponent of $$\varphi $$ φ .

Homogeneous vs Biased IGM: Impact on Reionization

This paper considers the impact of large scale biasing of the IGM on reionization. The two simplest but extreme scenarios for IGM biasing are: an unbiased IGM which has a constant density and an IGM with density equal to the collapsed matter density. In this work, the relationship between the IGM density and the collapsed matter density is defined through an IGM bias parameter. The two extreme scenarios of homogeneous and perfectly biased IGM are produced for two extreme values of this bias parameter. It is found that, for the same level of reionization (i.e., for same global neutral hydrogen fraction). one could get very different 21 cm brightness temperature distributions for different values of this bias parameter. These distributions could give an order of magnitude more or less power as compared to the uniform case. It is also found that there exists a critical value for the IGM bias parameter for which there could be a near washout of the structure in the 21 cm brightness temperature distribution (i.e., zero power or a nearly uniform 21 cm brightness temperature distribution). To address the problem, a new method of generating 21 cm brightness temperature maps is used. The method uses the results of n-body simulations and then employs ray tracing to obtain the 21 cm brightness temperature maps. Towards the end, a prescription for the IGM bias parameter is given. This is derived within the framework of the Press-Schechter theory.

Meta-Analysis of Present-Bias Estimation Using Convex Time Budgets*

We examine 220 estimates of the present-bias parameter from 28 articles using the Convex Time Budget protocol. The literature shows that people are on average present-biased, but estimates exhibit substantial heterogeneity across studies. There is evidence of modest selective reporting in the direction of overreporting present-bias. The primary source of heterogeneity is the type of reward, either monetary or non-monetary, but this effect is weakened after correcting for selective reporting. In studies using monetary rewards, the delay until the issue of the reward associated with the “current” time period influences estimates of the present-bias parameter.

Correlation function: biasing and fractal properties of the cosmic web

Aims. Our goal is to determine how the spatial correlation function of galaxies describes biasing and fractal properties of the cosmic web. Methods. We calculated spatial correlation functions of galaxies, ξ(r), structure functions, g(r) = 1 + ξ(r), gradient functions, γ(r) = d log g(r)/d log r, and fractal dimension functions, D(r) = 3 + γ(r), using dark matter particles of the biased Λ cold dark matter (CDM) simulation, observed galaxies of the Sloan Digital Sky Survey (SDSS), and simulated galaxies of the Millennium and EAGLE simulations. We analysed how these functions describe fractal and biasing properties of the cosmic web. Results. The correlation functions of the biased ΛCDM model samples at small distances (particle and galaxy separations), r ≤ 2.25 h−1 Mpc, describe the distribution of matter inside dark matter halos. In real and simulated galaxy samples, only the brightest galaxies in clusters are visible, and the transition from clusters to filaments occurs at a distance r ≈ 0.8−1.5 h−1 Mpc. At larger separations, the correlation functions describe the distribution of matter and galaxies in the whole cosmic web. The effective fractal dimension of the cosmic web is a continuous function of the distance (separation). Real and simulated galaxies of low luminosity, Mr ≥ −19, have almost identical correlation lengths and amplitudes, indicating that dwarf galaxies are satellites of brighter galaxies, and do not form a smooth population in voids. Conclusions. The combination of several physical processes (e.g. the formation of halos along the caustics of particle trajectories and the phase synchronisation of density perturbations on various scales) transforms the initial random density field to the current highly non-random density field. Galaxy formation is suppressed in voids, which increases the amplitudes of correlation functions and power spectra of galaxies, and increases the large-scale bias parameter. The combined evidence leads to the large-scale bias parameter of L⋆ galaxies the value b⋆ = 1.85 ± 0.15. We find r0(L⋆) = 7.20 ± 0.19 for the correlation length of L⋆ galaxies.

Identifying present bias and time preferences with an application to land-lease-contract data1

Summary What can contracts—traded and priced in a competitive market and featuring a pre-specified system of future payments—teach us about time preferences and present bias? We first show that identification of present bias requires assumptions on the felicity function and that agents must have credit constraints on consumption expenditure. Moreover, when there is heterogeneity in present bias, identification requires that agents with the same present bias parameter buy houses with different contracts. We illustrate our findings with observational land-lease-contract data from Amsterdam.

Can assembly bias explain the lensing amplitude of the BOSS CMASS sample in a Planck cosmology?

ABSTRACT In this paper, we investigate whether galaxy assembly bias can reconcile the 20–40 ${{\ \rm per\ cent}}$ disagreement between the observed galaxy projected clustering signal and the galaxy–galaxy lensing signal in the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample. We use the suite of abacuscosmos lambda cold dark matter simulations at Planck best-fitting cosmology and two flexible implementations of extended halo occupation distribution (HOD) models that incorporate galaxy assembly bias to build forward models and produce joint fits of the observed galaxy clustering signal and the galaxy–galaxy lensing signal. We find that our models using the standard HODs without any assembly bias generalizations continue to show a 20–40 ${{\ \rm per\ cent}}$ overprediction of the observed galaxy–galaxy lensing signal. We find that our implementations of galaxy assembly bias do not reconcile the two measurements at Planck best-fitting cosmology. In fact, despite incorporating galaxy assembly bias, the satellite distribution parameter, and the satellite velocity bias parameter into our extended HOD model, our fits still strongly suggest a $\sim \! 34{{\ \rm per\ cent}}$ discrepancy between the observed projected clustering and galaxy–galaxy lensing measurements. It remains to be seen whether a combination of other galaxy assembly bias models, alternative cosmological parameters, or baryonic effects can explain the amplitude difference between the two signals.