Refined statistical pipeline on Gaia FPR residuals detects 343 binary asteroid candidates, with 88% fewer false positives in noise simulations and overlaps with 9 known binaries.
Building a Framework for Predictive Science
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
Key questions that scientists and engineers typically want to address can be formulated in terms of predictive science. Questions such as: "How well does my computational model represent reality?", "What are the most important parameters in the problem?", and "What is the best next experiment to perform?" are fundamental in solving scientific problems. Mystic is a framework for massively-parallel optimization and rigorous sensitivity analysis that enables these motivating questions to be addressed quantitatively as global optimization problems. Often realistic physics, engineering, and materials models may have hundreds of input parameters, hundreds of constraints, and may require execution times of seconds or longer. In more extreme cases, realistic models may be multi-scale, and require the use of high-performance computing clusters for their evaluation. Predictive calculations, formulated as a global optimization over a potential surface in design parameter space, may require an already prohibitively large simulation to be performed hundreds, if not thousands, of times. The need to prepare, schedule, and monitor thousands of model evaluations, and dynamically explore and analyze results, is a challenging problem that requires a software infrastructure capable of distributing and managing computations on large-scale heterogeneous resources. In this paper, we present the design behind an optimization framework, and also a framework for heterogeneous computing, that when utilized together, can make computationally intractable sensitivity and optimization problems much more tractable.
representative citing papers
A new heuristic estimates photonuclear reactions' contribution to muon numbers in extensive air showers with roughly 10% absolute error over wide parameter ranges.
Simulations show LIFE could use 25-80 m baselines or discrete values with under 10% loss in planet yield and fringe tracking.
Smokescreen is a Python package that blinds cosmological data vectors using Firecrown likelihoods on SACC files while encrypting the true data to avoid premature unblinding.
Simulation study finds a size-dependent relation between solar sail area and asteroid diameter for the frequency of attitude changes needed to station-keep around irregular gravity fields using a decision-tree guidance agent.
cloelike is a new open Python package implementing composable Gaussian likelihoods for WL, GCph, GGL, full-shape spectra, and BAO in joint probe combinations for Euclid analyses.
cloelib is a modular JAX-based Python library for cosmological observables intended as reference infrastructure for Euclid's first data release.
citing papers explorer
-
Follow the wobble: Statistical methods to detect astrometric binary asteroids in Gaia FPR
Refined statistical pipeline on Gaia FPR residuals detects 343 binary asteroid candidates, with 88% fewer false positives in noise simulations and overlaps with 9 known binaries.
-
Readdressing the contribution of photonuclear reactions to the muon content of extensive air showers: a heuristic approach
A new heuristic estimates photonuclear reactions' contribution to muon numbers in extensive air showers with roughly 10% absolute error over wide parameter ranges.
-
A preliminary exploration of the effects of baseline length for the LIFE space mission
Simulations show LIFE could use 25-80 m baselines or discrete values with under 10% loss in planet yield and fringe tracking.
-
Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses
Smokescreen is a Python package that blinds cosmological data vectors using Firecrown likelihoods on SACC files while encrypting the true data to avoid premature unblinding.
-
A study on station-keeping over irregularly shaped asteroids with different sized solar sails
Simulation study finds a size-dependent relation between solar sail area and asteroid diameter for the frequency of attitude changes needed to station-keep around irregular gravity fields using a decision-tree guidance agent.
-
cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era
cloelike is a new open Python package implementing composable Gaussian likelihoods for WL, GCph, GGL, full-shape spectra, and BAO in joint probe combinations for Euclid analyses.
-
cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era
cloelib is a modular JAX-based Python library for cosmological observables intended as reference infrastructure for Euclid's first data release.