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We investigate primordial non-Gaussianity in the inflation without an inflaton framework, where scalar perturbations are generated at second order by primordial gravitational waves in Einstein gravity on an exact de Sitter background. Since scalar modes are produced nonlinearly from tensor modes, non-Gaussianity is an intrinsic prediction of the mechanism. We compute the corresponding scalar bispectrum, derive the relevant contribution to the three-point function of the scalar potential, and evaluate its shape numerically. Similar to the scalar power spectrum, we find that the bispectrum depends on the number of observed e-folds <inline-formula><mml:math><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi>obs</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula> through the ultraviolet cutoff. We show that the bispectrum shape is enhanced toward squeezed configurations, but its amplitude becomes strongly suppressed once the scalar power spectrum is normalized to the observed value. The resulting non-Gaussianity at cosmic microwave background scales is therefore negligibly small, well below present observational sensitivity.

We present py5vec, a Python package for implementing and extending the 5-vector method, used to search for continuous gravitational wave (CW) signals. We also provide a comprehensive theoretical review of the 5-vector method and extend the relative likelihood formalism by marginalizing over the noise variance, resulting in a more robust Student's <inline-formula> <mml:math><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math></inline-formula>-likelihood, and over the initial phase to account for pulsar glitches. py5vec provides a modular architecture that separates data representation, signal demodulation, and statistical inference into independent abstract stages. It supports multiple input data formats and interoperates with existing Python software, providing a bridge between different statistical approaches and data processing pipelines. For example, using a bilby-based interface, py5vec implements Bayesian parameter estimation within the 5-vector formalism for the first time. The modular design also allows for making exact multi-level and direct comparisons between other software, such as cwinpy and SNAG in MATLAB. In py5vec, we implement a multidetector targeted search for known pulsars, validated using LIGO data from the O4a run and hardware injections, demonstrating consistent reconstruction of signal parameters. This package therefore provides a flexible platform for current targeted searches and for future extensions to other CW search strategies.