TNG50 stellar disks are grouped into four j-types from sAM surface density maps, revealing a redshift-dependent sequence from irregular to barred forms driven by gas content and V/σ.
hub Mixed citations
Scikit-learn: Machine Learning in Python
Mixed citation behavior. Most common role is method (67%).
32
Pith papers citing it
Method
67% of classified citations
abstract
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.
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representative citing papers
Conditional normalizing flows approximate intractable likelihoods arising from cell division history to conclude that glc3 is mostly inactive under nutrient stress in yeast, with brief transient expression.
A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
A Dutch BERT model encodes gender linearly by epoch 20 but does not dynamically update its representations when explicit female cues contradict learned stereotypical associations in short sentence templates.
Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.
MOFAT applied to SN2024ggi shows CO triggering inner SiO formation with a receding edge, order-of-magnitude mass drop, clumping signatures, and no dust formation.
No credible periodic artificial signals were detected in the first lunar farside SETI observations using Chang'E-4 low-frequency radio spectrometer data.
First NLO-QCD amplitude-assisted ML regression for longitudinal-boson production rate in di-boson events at the LHC, benchmarked against random forests.
IllustrisTNG yields γ=2.23±0.20 for luminosity density evolution that explains the Tolman and distance-duality test signals in standard cosmology.
A homogenized ICL definition applied to Horizon-AGN, TNG100, Gizmo-Simba and Hydrangea yields consistent z=0 fractions of 0.1-0.2 with no significant redshift evolution and dominant contributions from satellites of 10^10.5-10^11.5 solar masses.
Calibrates a one-parameter semi-analytic damping model for oscillatory primordial power spectrum features using N-body simulations and validates sub-percent accuracy via GPR emulation when modulation frequency is high enough.
Q-PhotoNAS applies genetic algorithm search to jointly optimize classical preprocessing, phase encoding, and photonic circuit structure for hybrid quantum-classical models, reporting 99.44% and 98.78% accuracy on Digits and MNIST with projected photonic QPU inference times.
An unsupervised multilingual laughter segmentation method using Isolation Forest on BYOL-A audio representations outperforms existing supervised methods on non-English datasets.
Morphological metrics in galaxy images suffer systematic biases from resolution, depth, and noise that can be quantified and corrected empirically, with new metrics proposed to reduce those effects.
No evidence for KS0 or KL0 to pi+ pi- mu+ mu- decays; first upper limits set at 1.4e-9 and 6.6e-7 (90% CL).
Mutual information analysis of TNG50 simulations shows gravitational potential and total energy retain merger mass and infall time information longest, while radial velocity loses it within ~5 Gyr, with washout depending on radius, merger age, and mass.
The ttbar production cross section in PbPb collisions at 5.36 TeV is measured as 3.42 +0.54-0.51 (stat) +0.50-0.43 (syst) μb and is consistent with NNLO pQCD predictions using nuclear PDFs.
A Bayesian global fit at full NLO+NLL accuracy extracts the posterior distribution for the non-perturbative initial condition of the NLO Balitsky-Kovchegov equation from HERA inclusive and charm data.
A Hubble-like sequence of galaxy morphologies exists by redshift 4, with low-mass galaxies as persistent star-forming disks and massive galaxies following either stable disk or rapid compaction-quenching paths.
Controlled comparison on synthetic data shows objective choice in multiobjective unsupervised feature selection creates strong biases, with PCA reconstruction loss yielding compact subsets whose downstream accuracy matches direct supervised optimization.
TF-IDF identifies labeled experts in the top 25 recommendations 79.5% of the time versus 51.5% for GPT-4o mini on an astronomy observatory dataset.
The authors introduce analog matching to generate Roman Space Telescope mock catalogs that reproduce emission-line galaxy statistics and highlight the need to match void properties separately from two-point clustering for CMB cross-correlation studies.
A Higgs-assisted razor search with ML for Higgsino NLSPs decaying to Bino LSP via Z/h at 100 TeV projects 5σ discovery to 1.4 TeV Higgsino mass (Bino ~0.9 TeV) with 3000 fb^{-1}.
Simulations of the Aquila Rift show uneven clumps accreting gas and merging along filaments to form a fractal cluster whose velocity anisotropies, rotation, and expansion record the assembly history even after gas removal.
citing papers explorer
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Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection
Controlled comparison on synthetic data shows objective choice in multiobjective unsupervised feature selection creates strong biases, with PCA reconstruction loss yielding compact subsets whose downstream accuracy matches direct supervised optimization.