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Differing Manifestations of Spatial Curvature in Cosmological FRW Models
Get PDFWe find statistical evidence for a mismatch between the (global) spatial curvature parameter in the geodesic equation for incoming photons, and the corresponding parameter in the Friedmann equation that determines the time evolution of the background spacetime and its perturbations. The mismatch hereafter referred to as `curvature-slip\u27 is especially evident when the SH0ES prior on the current expansion rate is assumed. This result is based on joint analyses of cosmic microwave background (CMB) observations with the PLANCK satellite (P18), first year results of the Dark Energy Survey (DES), Baryonic Oscillation (BAO) data, and - at a lower level of significance - also on Pantheon SNIa (SN) catalog. For example, the betting odds against the Null Hypothesis are greater than :1, 1400:1 and 1000:1 when P18+SH0ES, P18+DES+SH0ES, and P18+BAO+SH0ES, respectively, are considered. Datasets involving SNIa weaken this curvature slip considerably. Notably, even when the SH0ES prior is not imposed the betting odds for the rejection of the Null Hypothesis are 70:1 and 160:1 in cases where P18+DES and P18+BAO are considered. When the SH0ES prior is imposed, global fit of the modified model (that allows for a nonvanishing `curvature slip\u27) strongly outperforms that of CDM as is manifested by significant Deviance Information Criterion (DIC) gains, ranging between 7 and 23, depending on the dataset combination considered. Even in comparison to KCDM the proposed model results in significant, albeit smaller, DIC gains when SN data are excluded. Our finding could possibly be interpreted as an inherent inconsistency between the (idealized) maximally symmetric nature of the FRW metric, and the dynamical evolution of the GR-based homogeneous and isotropic CDM model (abridged)Submitted. Comments are welcom
A functional treatment of small instanton-induced axion potentials
Get PDFWe present a functional method to perform complete one-instanton calculations of the axion potential. This is done for an gauge theory with a matter content in any representation of the gauge group. This type of computation requires the expression of the fermion zero modes of the theory. We construct them for all representations of , which serve as building blocks for obtaining the fermion zero modes for arbitrary representations of . The method is applied to the Minimal Supersymmetric model and its low-energy counterpart, the Minimal Supersymmetric Standard Model extended with two color triplets.43 pages, 6 figure
High-velocity outflows persist up to 1 Gyr after a starburst in recently-quenched galaxies at z > 1
Get PDFHigh-velocity outflows are ubiquitous in star-forming galaxies at cosmic noon, but are not as common in passive galaxies at the same epoch. Using optical spectra of galaxies selected from the UKIDSS Ultra Deep Survey (UDS) at z > 1, we perform a stacking analysis to investigate the transition in outflow properties along a quenching time sequence. To do this, we use MgII (2800 A) absorption profiles to investigate outflow properties as a function of time since the last major burst of star formation (tburst). We find evidence for high-velocity outflows in the star-forming progenitor population (vout ~ 1400 210 km/s), for recently quenched galaxies with tburst 1 Gyr) show no evidence for significant outflows. Our samples show no signs of AGN in optical observations, suggesting that any AGN in these galaxies have very short duty cycles, and were \u27off\u27 when observed. The presence of significant outflows in the older quenched galaxies (tburst > 0.6 Gyr) is difficult to explain with starburst activity, however, and may indicate energy input from episodic AGN activity as the starburst fades.10 pages, 4 figures. Accepted for publication in MNRA
Systematic Interpretability and the Likelihood for Boosted Top Quark Identification
Get PDFIdentification of boosted, hadronically-decaying top quarks is a problem of central importance for physics goals of the Large Hadron Collider. We present a theoretical analysis of top quark tagging, establishing zeroth-order, minimal assumptions that should be satisfied by any purported top-tagged jet, like existence of three hard subjets, a bottom-tagged subjet, total mass consistent with the top quark, and a pairwise subjet mass consistent with the W boson. From these minimal assumptions, we construct the optimal discrimination observable, the likelihood ratio, for the binary discrimination problem of top quark-initiated versus bottom quark-initiated jets through next-to-leading order in the strong coupling. We compare and compute corresponding signal and background efficiencies both analytically and from simulated data, validating an understanding of the relevant physics identified and exploited by the likelihood. In the process, we construct a method for systematic interpretability of the likelihood ratio for this problem, and explicitly establish a hard floor on possible discrimination power. These results can correspondingly be applied to understanding and interpreting machine learning studies of this problem.18 pages, 3 figure
On the Hilbert matrix operator: a brief survey
No full textThis article aims to explore the most recent developments in the study of the Hilbert matrix, acting as an operator on spaces of analytic functions and sequence spaces. We present the latest advances in this area, aiming to provide a concise overview for researchers interested in delving into the captivating theory of operator matrices.19 pages, submitted to the IWOTA 2023 Conference Proceedings in the book series Operator Theory: Advances and Application
Photonic neural networks with spatiotemporal chaos in multimode fibers
No full textOptical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency. Optical systems offer promising alternatives to digital neural networks by exploiting light\u27s parallelism. This study explores a photonic neural network design using spatiotemporal chaos within grad-ed-index multimode fibers to improve machine learning performance. Through numerical simulations and experiments, we show that chaotic light propagation in multimode fibers enhances data classification accu-racy across domains, including biomedical imaging, fashion, and satellite geospatial analysis. This chaotic optical approach enables high-dimensional transformations, amplifying data separability and differentiation for greater accuracy. Fine-tuning parameters such as pulse peak power optimizes the reservoir\u27s chaotic properties, highlighting the need for careful calibration. These findings underscore the potential of chaos-based nonlinear photonic neural networks to advance optical computing in machine learning, paving the way for efficient, scalable architectures.14 page
MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
No full textEffective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--individual researchers would need to decide on the particular featurization and tabularization processes to apply to their individual raw, longitudinal data; and then train a supervised model over those data to produce a baseline result to compare novel methods against, all for just one task and one dataset. In this work, powered by complementary advances in core data standardization through the MEDS framework, we dramatically simplify and accelerate this process of tabularizing irregularly sampled time-series data, providing researchers the ability to automatically and scalably featurize and tabularize their longitudinal EHR data across tens of thousands of individual features, hundreds of millions of clinical events, and diverse windowing horizons and aggregation strategies, all before ultimately leveraging these tabular data to automatically produce high-caliber XGBoost baselines in a highly computationally efficient manner. This system scales to dramatically larger datasets than tabularization tools currently available to the community and enables researchers with any MEDS format dataset to immediately begin producing reliable and performant baseline prediction results on various tasks, with minimal human effort required. This system will greatly enhance the reliability, reproducibility, and ease of development of powerful ML solutions for health problems across diverse datasets and clinical settings
Veering triangulations and transverse foliations
No full textWe present a combinatorial approach to the existence of foliations and contact structures transverse to a given pseudo-Anosov flow. Let be a transitive pseudo-Anosov flow on a closed oriented 3-manifold. Our main technical result is that every codimension 1 foliation transverse to is carried by a single branched surface coming from a veering triangulation. Combined with recent breakthrough work of Massoni, this reduces the existence problem for transverse foliations to something like the feasibility of a system of inequalities (rather than equations!) over . As a proof of concept, we show that for the hyperbolic, fibered, non-L-space knot , the natural pseudo-Anosov flow on the slope Dehn surgery admits a transverse foliation for , but does not admit such a foliation for . The negative result is part of a more general Milnor--Wood type phenomenon which puts limitations on some well known methods for constructing taut foliations on Dehn surgeries
Attaining high accuracy for charge-transfer excitations in non-covalent complexes at second-order perturbation cost: the importance of state-specific self-consistency
No full textIntermolecular charge-transfer (xCT) excited states important for various practical applications are challenging for many standard computational methods. It is highly desirable to have an affordable method that can treat xCT states accurately. In the present work, we extend our self-consistent perturbation methods, named one-body second-order Møller-Plesset (OBMP2) and its spin-opposite scaling variant, for excited states without additional costs to the ground state. We then assessed their performance for the prediction of xCT excitation energies. Thanks to self-consistency, our methods yield small errors relative to high-level coupled cluster methods and outperform other same scaling () methods like CC2 and ADC(2). In particular, the spin-opposite scaling variant (O2BMP2), whose scaling can be reduced to , can even reach the accuracy of CC3 () with errors less than 0.1 eV. This method is thus highly promising for treating xCT states in large compounds vital for applications.16 pages, 5 figures, 3 table
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach
No full textClinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage due to privacy and legal concerns. Hence, image augmentation is a necessary and challenging step to improve the performance of the DL models. However, the current DL-based augmentation models are inadequate and operate as a black box resulting lack of information and justifications about their suitability and efficacy. Additionally, pre and post-augmentation need high-performance computational resources and time to produce the augmented image and evaluate the model performance. Thus, this study aims to develop a novel efficient augmentation approach for BUS images with advanced neural style transfer (NST) and Explainable AI (XAI) harnessing GPU-based parallel infrastructure. We scale and distribute the training of the augmentation model across 8 GPUs using the Horovod framework on a DGX cluster, achieving a 5.09 speedup while maintaining the model\u27s accuracy. The proposed model is evaluated on 800 (348 benign and 452 malignant) BUS images and its performance is analyzed with other progressive techniques, using different quantitative analyses. The result indicates that the proposed approach can successfully augment the BUS images with 92.47% accuracy