Void Probability Research Articles

Comparative Evaluation of Single Rotary File System and Sequential Multi-file Rotary Systems on Time for Biomechanical Preparation and Obturation Quality in Single-visit Pulpectomy Protocol: A Double-blind Randomized Clinical Trial.

Single-visit pulpectomy (SVP) protocol with rotary files is highly recommended for the treatment of teeth with irreversible pulpitis. Various rotary endodontic files specially designed for use in the pediatric population are available. The aim is to clinically assess the parameters related to the time required for biomechanical preparation (TBMP) and quality of filling using a single file system vs a sequential multi-file system in infected primary mandibular molars. A total of 45 infected primary molars were allocated to three groups (two experimental groups (n = 15) and a control group. The first experimental group was instrumented using NiTi K-Flex files, the second group with a single file rotary system with variably variable (VV) taper, and the third with a sequential multi-file system with constant taper. Biomechanical preparation time was recorded and standardized digital radiograph (RVG) were taken pre- and postinstrumentation. The data recorded was sent for statistical analysis. There is a substantial reduction of TBMP in primary molars using single file VV taper and multi-sequential file constant taper. Obturation time for all three file systems was comparable and there were no differences between the three file systems used (p > 0.05). Multi-sequential file constant taper files showed a higher probability of optimal obturations and minimal voids followed by NiTi "K-Flex" files and single file system but the difference was nonsignificant (p > 0.05). However, using a rotary in primary teeth results in better canal shape, and less TBMP leading to a better quality of treatment in less time. Shetty B, Singh R, Patil V, et al. Comparative Evaluation of Single Rotary File System and Sequential Multi-file Rotary Systems on Time for Biomechanical Preparation and Obturation Quality in Single-visit Pulpectomy Protocol: A Double-blind Randomized Clinical Trial. Int J Clin Pediatr Dent 2023;16(S-3):S247-S252.

Void probability function inside cosmic voids: evidence for hierarchical scaling of high-order correlations in real space

ABSTRACT We compare the reduced void probability function (VPF) inside and outside of cosmic voids in the TNG300-1 simulation, both in real and simulated redshift space. The VPF is a special case of the Counts-in-Cells approach for extracting information of high-order clustering that is crucial for a full understanding of the distribution of galaxies. Previous studies have validated the hierarchical scaling paradigm of galaxy clustering moments, in good agreement with the ‘negative binomial’ model, in redshift surveys but have also reported that this paradigm is not valid in real space. However, in this work, we find that hierarchical scaling can indeed be found in real space inside cosmic voids. This is well fitted by the negative binomial model. We find this result to be robust against changes in void identification, galaxy mass, random dilutions, and redshift. We also obtain that the VPF in real space at high redshift approaches the negative binomial model, and, therefore, it is similar to the VPF inside voids at the present time. This study points, for the first time, towards evidence of hierarchical scaling of high-order clustering of galaxies in real space inside voids, preserving the pristine structure formation processes of the Universe.

On a special class of gibbs hard-core point processes modeling random patterns of non-overlapping grains

Inspired by issues of formal kinetics in materials science, we consider a class of processes with density with respect to an inhomogeneous finite Poisson point process, which may be regarded as a generalization of the classical Strauss hard-core process. We prove expressions for the intensity measure and the void probabilities, together with upper and lower bounds. A discussion on some special cases of interest, links with literature and a comparison between Matérn I and Strauss hard-core process are also provided. We apply such a special class of point processes in modeling patterns of non-overlapping grains and in the study of the mean volume density of particular birth-and-growth processes.

Geometric Poisson distribution of photons produced in the ultrarelativistic hadronic collisions

We show that the multiplicity distribution of photons produced with enhanced void probability in inelastic proton-proton collisions at sqrt{s} = 900 GeV, 2.76 TeV, and 7 TeV, measured at forward rapidities by the ALICE experiment at LHC, can be described by the geometric Poisson distribution. The traditionally used negative binomial distribution fails to reproduce the enhanced void probability and the shape of the modified combinants simultaneously. Our findings are relevant for the theoretical modeling of photon production processes in high-energy hadronic collisions.

Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100 h −1 cMpc)3 with different cosmological parameters (Ω m and σ 8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 < R <27 h −1 cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.

Nondestructive millimeter-scale void detection for thick steel-shell–concrete interface of immersed tube tunnel: case study

The structural form of sandwich-structured immersed tunnel (SSIT) can be complex. During the casting of self-compacting concrete, creating void defects between the steel shell and concrete interface is not difficult, which can adversely affect the overall safety and service life of the structure. However, detecting millimeter-scale voids covered by a thick steel plate is a technical challenge for current engineering industries. In this study, we proposed a nondestructive millimeter-scale void detection method for SSITs with thick steel shells by combining impact imaging and neutron methods. First, based on the near-source wavefield theory and count rate of thermal neutrons, the void area and depth calculation methods were derived theoretically, and then the coupling detection method and grading criteria for void severity were proposed. Additionally, the void detection performance was validated for a full-scale SSIT model test by blind detection. Finally, the proposed method was applied to the SSIT of the Shenzhen–Zhongshan bridge. The results showed that the proposed method could quantitatively determine the location and distribution pattern of a void; however, it could not accurately determine the void depth. In contrast, the neutron method could accurately calculate the void depth but had a large minimum detectable unit area. The proposed method could effectively compensate for the limitations of both methods. Statistically, the coincidence rate of the model test was 95%, 89%, and 87.5% for the void location, void area, and void depth, respectively, when the error range was ±2 mm. Using this method, 30 tubes in the Shenzhen–Zhongshan bridge were inspected, and by summarizing the void law, suggestions to improve the casting process were proposed, such as adjusting the casting speed. Meanwhile, the void probability decreased significantly. The proposed method provides an important basis for high-quality construction in SSIT projects.

Multisensor Multitarget Tracking and Collision Avoidance Based on the Labeled RFS Void Probability and KL-PaC Cost Function

Existing sensor control methods for multi-sensor and multi-target tracking seldom consider comprehensive control strategy and collision avoidance between sensors and targets or between sensors together. In this paper, we first propose a cost function for multi-sensor control strategy, which includes Kullback-Leibler (K-L) divergence, detection probability and sensor usage cost (KL-PaC cost function). Second, in order to solve collision avoidance between sensors and targets, we propose a collision avoidance strategy based on the labeled random finite set (RFS) void probability. The strategy is essentially to set up a safe region around the sensor or target, and then combine the void probability and cost function in the safe region as the evaluation function. The final simulation and experimental results verify the effectiveness of the proposed approach.

Constraints on the Epoch of Reionization with Roman Space Telescope and the Void Probability Function of Lyα Emitters

We use large simulations of Lyα emitters with different fractions of ionized intergalactic medium to quantify the clustering of Lyα emitters as measured by the void probability function (VPF), and how it evolves under different ionization scenarios. We quantify how well we might be able to distinguish between these scenarios with a deep spectroscopic survey using the future Nancy Grace Roman Space Telescope. Because Roman will be able to carry out blind spectroscopic surveys of Lyα emitters continuously between 7 < z < 12 to sensitivities of at least 10−17 erg s−1 over a wide field of view, it can measure the epoch of reionization as well as the pace of ionization of the intergalactic medium (IGM). We compare deep Roman surveys covering roughly 1, 4, and 16 deg2, and quantify what constraints on reionization the VPF may find for these surveys. A survey of 1 deg2 would distinguish between very late reionization and early reionization to 3σ near z = 7.7 with the VPF. The VPF of a 4 deg2 survey can distinguish between slow versus fast, as well as early versus late, reionization at >3σ–4σ at several redshifts between 7 < z < 9. However, a survey of 13–16 deg2 would allow the VPF to give several robust constraints (>5–8σ) across the epoch of reionization, and would yield a detailed history of the reionization of the IGM and its effect on Lyα emitter clustering.

Probing Patchy Reionization with the Void Probability Function of Lyα Emitters

We probe what constraints for the global ionized hydrogen fraction the void probability function (VPF) clustering can give for the Lyman Alpha Galaxies in the Epoch of Reionization (LAGER) narrowband survey as a function of area. Neutral hydrogen acts like a fog for Lyα emission, and measuring the drop in the luminosity function of Lyα emitters (LAEs) has been used to constrain the ionization fraction in narrowband surveys. However, the clustering of LAEs is independent of the luminosity function’s inherent evolution, and can offer additional constraints for reionization under different models. The VPF measures how likely a given circle is to be empty. It is a volume-averaged clustering statistic that traces the behavior of higher-order correlations, and its simplicity offers helpful frameworks for planning surveys. Using the Jensen et al. simulations of LAEs within various amounts of ionized intergalactic medium, we predict the behavior of the VPF in one (301 × 150.5 × 30 Mpc3), four (5.44 × 106 Mpc3), or eight (1.1 × 107 Mpc3) fields of LAGER imaging. We examine the VPF at 5′ and 13′, corresponding to the minimum scale implied by the LAE density and the separation of the 2D VPF from random, and the maximum scale from the eight-field 15.5 deg2 LAGER area. We find that even a single DECam field of LAGER (2–3 deg2) could discriminate between mostly neutral versus ionized. Additionally, we find four fields allow for the distinction between 30%, 50%, and 95% ionized, and eight fields could even distinguish between 30%, 50%, 73%, and 95% ionized.

Abundance matching analysis of the emission-line galaxy sample in the extended Baryon Oscillation Spectroscopic Survey

ABSTRACT We present the measurements of the small-scale clustering for the emission-line galaxy (ELG) sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) in the Sloan Digital Sky Survey IV (SDSS-IV). We use conditional abundance matching method to interpret the clustering measurements from 0.34 to $70\, h^{-1}\, \textrm {Mpc}$. In order to account for the correlation between properties of ELGs and their environment, we add a secondary connection between star formation rate of ELGs and halo accretion rate. Three parameters are introduced to model the ELG [O ii] luminosity and to mimic the target selection of eBOSS ELGs. The parameters in our models are optimized using Markov Chain Monte Carlo (MCMC) method. We find that by conditionally matching star formation rate of galaxies and the halo accretion rate, we are able to reproduce the eBOSS ELG small-scale clustering within 1σ error level. Our best-fitting model shows that the eBOSS ELG sample only consists of $\sim 12{{\ \rm per\ cent}}$ of all star-forming galaxies, and the satellite fraction of eBOSS ELG sample is 19.3 per cent. We show that the effect of assembly bias is $\sim 20{{\ \rm per\ cent}}$ on the two-point correlation function and $\sim 5{{\ \rm per\ cent}}$ on the void probability function at scale of $r\sim 20 \, h^{-1}\, \rm Mpc$.

Multi-Sensor Multi-Target Tracking and Collision Avoidance Strategy Based on the Labeled Rfs Void Probability and K-L Divergence

Multi-Sensor Multi-Target Tracking and Collision Avoidance Strategy Based on the Labeled Rfs Void Probability and K-L Divergence

Void Probability Function of Simulated Surveys of High-redshift Lyα Emitters

Abstract We calculate the void probability function (VPF) in simulations of Lyα emitters (LAEs) across a wide redshift range (z = 3.1, 4.5, 5.7, 6.6). The VPF measures the zero-point correlation function (i.e., places devoid of galaxies) and naturally connects to higher-order correlation functions while being computationally simple to calculate. We explore the Poissonian and systematic errors on the VPF, specify its accuracy as a function of average source density and the volume probed, and provide the appropriate size scales to measure the VPF. At small radii, the accuracy of the VPF is limited by galaxy density, while at large radii, the VPF is limited by the number of independent volumes probed. We also offer guidelines for understanding and quantifying the error in the VPF. We approximate the error in the VPF by using independent subvolumes of the catalogs, after finding that jackknife statistics underestimate the uncertainty. We use the VPF to probe the strength of higher-order correlation functions by measuring and examining the hierarchical scaling between the correlation functions using the count-in-cells method. The negative binomial model (NBM) has been shown to best describe the scaling between the two-point correlation function and the VPF for low-redshift galaxy observations. We further test the fit of the NBM by directly deriving the volume-averaged two-point correlation function from the VPF and vice versa. We find the NBM best describes the simulated LAEs, with a 1σ deviation from the model in the z = 6.6 catalog. This suggests that LAEs show higher-order clustering terms similar to those of normal low-redshift galaxies.

Constraining reionization in progress at z = 5.7 with Lyman-α emitters: voids, peaks, and cosmic variance

ABSTRACT A number of independent observations suggest that the intergalactic medium was significantly neutral at z = 7 and that reionization was, perhaps, still in progress at z = 5.7. The narrowband survey, SILVERRUSH, has mapped over 2000 Lyman-α emitters (LAEs) at these redshifts ( G58). Previous analyses have assumed that reionization was over by z = 5.7, but this data may actually sample the final stages of reionization when the last neutral islands were relegated to the cosmic voids. Motivated by these developments, we re-examine LAE void and peak statistics and their ability to constrain reionization. We construct models of the LAE distribution in (1 Gpc h−1)3 volumes, spanning a range of neutral fractions at z = 5.7 and 6.6. Models with a higher neutral fraction show an enhanced probability of finding holes in the LAE distribution. When comparing models at fixed mean surface density, however, LAEs obscured by neutral gas in the voids must be compensated by visible LAEs elsewhere. Hence, in these models, the likelihood of finding an overdense peak is also enhanced in the latter half of reionization. Compared to the widely used angular two-point correlation function (2PCF), we find that the void probability function (VPF) provides a more sensitive test of models during the latter half of reionization. By comparison, at neutral fractions $\sim 50{{\ \rm per\ cent}}$, the VPF and a simple peak thresholding statistic are both similar to the 2PCF in constraining power. Lastly, we find that the cosmic variance and large-scale asymmetries observed in the SILVERRUSH fields are consistent with large-scale structure in a ΛCDM universe.

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

A molecular dynamics study of surface wettability effects on heterogeneous bubble nucleation

In this paper, the bubble nucleation on ideally smooth surfaces with different wettabilities is investigated by molecular dynamics simulations. Nucleation is induced by raising temperature of solid surface to 160 K while removing the thermostat of the liquid. To fully understand the effect of surface wettability on bubble nucleation, several aspects related to the nucleation process are investigated, including void number, void probability, waiting time and bubble growth rate. It is found that there exists an appropriate range of interaction parameter (0.00624 eV < εAr-Pt < 0.052 eV) between solid and liquid atoms to enable the occurrence of bubble nucleation. Simultaneously, it is found that the initial stage of nucleate boiling can be divided into two stages: slow nucleation stage and rapid growth stage. Furthermore, the probability of void formation above the surface gradually increases with the increase of hydrophobicity of the surface, while the bubble growth rate increase with the increase of hydrophilicity of the surface. Finally, in consideration of the balance between the bubble growth rate and void probability, a mixed-wettable surface is proposed, which is verified to benefit the bubble nucleation at the nanoscale.

A Look at Multiplicity Distributions via Modified Combinants

The experimentally measured multiplicity distributions exhibit, after a closer inspection, the peculiarly enhanced void probability and the oscillatory behavior of modified combinants. We show that both these features can be used as additional sources of information, not yet fully explored, on the mechanism of multiparticle production. We provide their theoretical understanding within the class of compound distributions.

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

ABSTRACT Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analysed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy–halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function $w$p(rp), the galaxy–galaxy lensing signal ΔΣ(rp), the void probability function VPF(r), the distributions of counts-in-cylinders P(NCIC), and counts-in-annuli P(NCIA), and the distribution of the ratio of counts in cylinders of different sizes P(N2/N5). We find that despite the frequent use of the combination $w$p(rp) + ΔΣ(rp) in interpreting galaxy data, the count statistics, P(NCIC) and P(NCIA), are generally more efficient in constraining galaxy assembly bias when combined with $w$p(rp). Constraints based upon $w$p(rp) and ΔΣ(rp) share common degeneracy directions in the parameter space, while combinations of $w$p(rp) with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy–halo connection.

Intriguing properties of multiplicity distributions

Multiplicity distributions exhibit, after closer inspection, peculiarly enhanced void probability and oscillatory behavior of the modified combinants. We discuss the possible sources of these oscillations and their impact on our understanding of the multiparticle production mechanism. Theoretical understanding of both phenomena within the class of compound distributions is presented.

Probing Galaxy assembly bias in BOSS galaxies using void probabilities

ABSTRACT We measure the void probability function (VPF) of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS). The VPF provides complementary information to standard two-point statistics in that it is sensitive to galaxy bias in the most extreme underdensities in the cosmic web. Thus, the VPF is ideal for testing whether halo occupation of galaxies depends on large-scale density, an effect known as galaxy assembly bias. We find that standard HOD model – one parameterized by halo mass only – fit only to the two-point function, accurately predicts the VPF. Additionally, for HOD models where density dependence is explicitly incorporated, the best-fitting models fit to the combination of the correlation function and the VPF have zero density dependence. Thus, galaxy assembly bias is not a strong source of systematic uncertainty when modelling the clustering of massive galaxies.

Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.