Effective String Theory Simplified

In this set of notes we simplify the formulation of the Poincare'-invariant effective string theory in D dimensions by adding an intrinsic metric and embedding its dynamics into the Polyakov formalism. We use this formalism to construct operators order by order in the inverse physical length of the string, in a fully gauge-invariant framework. We use this construction to discuss universality and nonuniversality of observables up to and including next-to-next-to-leading order in the long string expansion.Comment: v. 3, minor change

Image denoising in photon-counting CT using PFGM++ with hijacked regularized sampling

Deep learning (DL) has proven to be an important tool for high quality image denoising in low-dose and photon-counting CT. However, DL models are usually trained using supervised methods, requiring paired data that may be difficult to obtain in practice. Physics-inspired generative models, such as score-based diffusion models, offer unsupervised means of solving a wide range of inverse problems via posterior sampling. The latest in this family are Poisson flow generative models (PFGM)++ which, inspired by electrostatics, treat the $N$-dimensional data as positive electric charges in a $N+D$-dimensional augmented space. The electric field lines generated by these charges are used to find an invertible mapping, via an ordinary differential equation, between an easy-to-sample prior and the data distribution of interest. In this work, we propose a method for CT image denoising based on PFGM++ that does not require paired training data. To achieve this, we adapt PFGM++ for solving inverse problems via posterior sampling, by hijacking and regularizing the sampling process. Our method incorporates score-based diffusion models (EDM) as a special case as $D\rightarrow \infty$, but additionally allows trading off robustness for rigidity by varying $D$. The network is efficiently trained on randomly extracted patches from clinical normal-dose CT images. The proposed method demonstrates promising performance on clinical low-dose CT images and clinical images from a prototype photon-counting system

PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models

Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general, and for clinical applications in particular, is slow sampling. Due to their iterative nature, the number of function evaluations (NFE) required is usually on the order of $10-10^3$, both for conditional and unconditional generation. In this paper, we present posterior sampling Poisson flow generative models (PPFM), a novel image denoising technique for low-dose and photon-counting CT that produces excellent image quality whilst keeping NFE=1. Updating the training and sampling processes of Poisson flow generative models (PFGM)++, we learn a conditional generator which defines a trajectory between the prior noise distribution and the posterior distribution of interest. We additionally hijack and regularize the sampling process to achieve NFE=1. Our results shed light on the benefits of the PFGM++ framework compared to diffusion models. In addition, PPFM is shown to perform favorably compared to current state-of-the-art diffusion-style models with NFE=1, consistency models, as well as popular deep learning and non-deep learning-based image denoising techniques, on clinical low-dose CT images and clinical images from a prototype photon-counting CT system

Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study.

PurposePhoton counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition.ApproachThe effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis.ResultsFor single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by ∼10% . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching.ConclusionsThe performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition

Analytic Continuation of Liouville Theory

Correlation functions in Liouville theory are meromorphic functions of the Liouville momenta, as is shown explicitly by the DOZZ formula for the three-point function on the sphere. In a certain physical region, where a real classical solution exists, the semiclassical limit of the DOZZ formula is known to agree with what one would expect from the action of the classical solution. In this paper, we ask what happens outside of this physical region. Perhaps surprisingly we find that, while in some range of the Liouville momenta the semiclassical limit is associated to complex saddle points, in general Liouville's equations do not have enough complex-valued solutions to account for the semiclassical behavior. For a full picture, we either must include "solutions" of Liouville's equations in which the Liouville field is multivalued (as well as being complex-valued), or else we can reformulate Liouville theory as a Chern-Simons theory in three dimensions, in which the requisite solutions exist in a more conventional sense. We also study the case of "timelike" Liouville theory, where we show that a proposal of Al. B. Zamolodchikov for the exact three-point function on the sphere can be computed by the original Liouville path integral evaluated on a new integration cycle.Comment: 86 pages plus appendices, 9 figures, minor typos fixed, references added, more discussion of the literature adde

Light States in Chern-Simons Theory Coupled to Fundamental Matter

Motivated by developments in vectorlike holography, we study SU(N) Chern-Simons theory coupled to matter fields in the fundamental representation on various spatial manifolds. On the spatial torus T^2, we find light states at small `t Hooft coupling \lambda=N/k, where k is the Chern-Simons level, taken to be large. In the free scalar theory the gaps are of order \sqrt {\lambda}/N and in the critical scalar theory and the free fermion theory they are of order \lambda/N. The entropy of these states grows like N Log(k). We briefly consider spatial surfaces of higher genus. Based on results from pure Chern-Simons theory, it appears that there are light states with entropy that grows even faster, like N^2 Log(k). This is consistent with the log of the partition function on the three sphere S^3, which also behaves like N^2 Log(k). These light states require bulk dynamics beyond standard Vasiliev higher spin gravity to explain them.Comment: 58 pages, LaTeX, no figures, Minor error corrected, references added, The main results of the paper have not change