The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
The NumPy Array: A Structure for Efficient Numerical Computation
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abstract
In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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Massive young star clusters clear their natal gas faster than lower-mass clusters, based on HST and JWST imaging of four galaxies.
Early-time Hubble tension solutions generically raise the preferred baryon density ω_b, conflicting with BBN deuterium constraints and producing poorer fits to CMB, BAO, supernova, and BBN data than ΛCDM.
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
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The Causal Bootstrap: Bounding Smeared Spectral Functions from Non-Perturbative Euclidean Data
The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
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The emerging timescale of young star clusters regulated by cluster stellar mass
Massive young star clusters clear their natal gas faster than lower-mass clusters, based on HST and JWST imaging of four galaxies.
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A generic $\omega_b$ tension in early-time solutions to the Hubble tension
Early-time Hubble tension solutions generically raise the preferred baryon density ω_b, conflicting with BBN deuterium constraints and producing poorer fits to CMB, BAO, supernova, and BBN data than ΛCDM.
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.