We present procedures based on Bayesian statistics for estimating, from data, the parameters of effective field theories (EFTs). The extraction of low-energy constants (LECs) is guided by theoretical expectations in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools are developed that analyze the fit and ensure that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems, including the extraction of LECs for the nucleon mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Abstract.6 He has a cluster structure with a tight 4 He (α) core surrounded by two loosely bound neutrons (n) making it a halo nucleus. The leading-order (LO) Halo Effective Field Theory (EFT) [1, 2] calculations using momentum-space Faddeev equations pertinent to a bound 6 He were carried out in [3]. In this work, we investigate 6 He up to next-to-leading order (NLO) within Halo EFT.
Pleural effusion is seen in up to 10 percent of patients on peritoneal dialysis (PD). 1 The differential diagnosis of pleural effusion in PD patients is extensive. It includes general causes of pleural effusion and causes unique to PD patients. Transudative pleural effusions in PD patients may be commonly due to volume overload and cardiac failure, or rarely due to pleuroperitoneal leakage. 2 Exudative effusions could occur due to infection, inflammation, or hemorrhage. The presence of a hemorrhagic pleural effusion can narrow the differential diagnoses to trauma,
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