The Four Cosmic Tidal Web Elements from the β-skeleton

Precise cosmic web classification of observed galaxies in massive spectroscopic surveys can be either highly uncertain or computationally expensive. As an alternative, we explore a fast Machine Learning-based approach to infer the underlying dark matter tidal cosmic web environment of a galaxy distribution from its β-skeleton graph. We develop and test our methodology using the cosmological magnetohydrodynamic simulation Illustris-TNG at z = 0. We explore three different tree-based machine-learning algorithms to find that a random forest classifier can best use graph-based features to classify a galaxy as belonging to a peak, filament, or sheet as defined by the T-Web classification algorithm. The best match between the galaxies and the dark matter T-Web corresponds to a density field smoothed over scales of 2 Mpc, a threshold over the eigenvalues of the dimensionless tidal tensor of λ th = 0.0, and galaxy number densities around 8 × 10−3 Mpc−3. This methodology results on a weighted F1 score of 0.728 and a global accuracy of 74%. More extensive tests that take into account light-cone effects and redshift space distortions are left for future work. We make one of our highest ranking random forest models available on a public repository for future reference and reuse.

Probabilistic cosmic web classification using fast-generated training data

ABSTRACT We present a novel method of robust probabilistic cosmic web particle classification in three dimensions using a supervised machine learning algorithm. Training data were generated using a simplified ΛCDM toy model with pre-determined algorithms for generating haloes, filaments, and voids. While this framework is not constrained by physical modelling, it can be generated substantially more quickly than an N-body simulation without loss in classification accuracy. For each particle in this data set, measurements were taken of the local density field magnitude and directionality. These measurements were used to train a random forest algorithm, which was used to assign class probabilities to each particle in a ΛCDM, dark matter-only N-body simulation with 2563 particles, as well as on another toy model data set. By comparing the trends in the ROC curves and other statistical metrics of the classes assigned to particles in each data set using different feature sets, we demonstrate that the combination of measurements of the local density field magnitude and directionality enables accurate and consistent classification of halo, filament, and void particles in varied environments. We also show that this combination of training features ensures that the construction of our toy model does not affect classification. The use of a fully supervised algorithm allows greater control over the information deemed important for classification, preventing issues arising from arbitrary hyperparameters and mode collapse in deep learning models. Due to the speed of training data generation, our method is highly scalable, making it particularly suited for classifying large data sets, including observed data.

Document Organization using Naive Bayes Related Classifier

Document organization is necessary for better utilization of documents. The major problem of organization online documents is so complex because documents should be grouped into its appropriate group during its appearance on the web. Classification is one of the best solutions to organize the documents. Naive Bayes categorization is playing a vital role in document organization. It is one of the simplest probabilistic Bayesian categorization and assumption that the effect of an attribute value on a given category is independent of the values. The document classification is the essential task of organization and necessary for efficient control of textual fact systems. The files may be classified as unconfirmed, supervised and semi supervised methods. In this paper, to review and study of various types of document organization approach using naive Bayesian classification and other related existing document organization methods.

Baryons in the Cosmic Web of IllustrisTNG – I: gas in knots, filaments, sheets, and voids

ABSTRACT We analyse the IllustrisTNG simulations to study the mass, volume fraction, and phase distribution of gaseous baryons embedded in the knots, filaments, sheets, and voids of the Cosmic Web from redshift z = 8 to redshift z = 0. We find that filaments host more star-forming gas than knots, and that filaments also have a higher relative mass fraction of gas in this phase than knots. We also show that the cool, diffuse intergalactic medium [IGM; $T\lt 10^5 \, {\rm K}$, $n_{\rm H}\lt 10^{-4}(1+z) \, {\rm cm^{-3}}$] and the warm-hot intergalactic medium [WHIM; $10^5 \lt T\lt 10^7 \, {\rm K}$, $n_{\rm H} \lt 10^{-4}(1+z)\, {\rm cm^{-3}}$] constitute ${\sim } 39$ and ${\sim } 46{{\ \rm per\ cent}}$ of the baryons at redshift z = 0, respectively. Our results indicate that the WHIM may constitute the largest reservoir of missing baryons at redshift z = 0. Using our Cosmic Web classification, we predict the WHIM to be the dominant baryon mass contribution in filaments and knots at redshift z = 0, but not in sheets and voids where the cool, diffuse IGM dominates. We also characterize the evolution of WHIM and IGM from redshift z = 4 to redshift z = 0, and find that the mass fraction of WHIM in filaments and knots evolves only by a factor of ∼2 from redshift z = 0 to 1, but declines faster at higher redshift. The WHIM only occupies $4\!-\!11{{\ \rm per\ cent}}$ of the volume at redshift 0 ≤ z ≤ 1. We predict the existence of a significant number of currently undetected O vii and Ne ix absorption systems in cosmic filaments, which could be detected by future X-ray telescopes like Athena.