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The redshifted 21 cm signal from the Epoch of Reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating sources during EoR introduces scale-dependent features in the spherically averaged EoR 21 cm signal power spectrum. Aims. The goal is to study these scale-dependent features at different stages of reionization using numerical simulations and to build a source model-independent framework to probe the properties of the intergalactic medium using EoR 21 cm signal power spectrum measurements. Methods. Under the assumption of high spin temperature, we modeled the redshift evolution of the ratio of the EoR 21 cm brightness temperature power spectrum to the corresponding density power spectrum using an ansatz consisting of a set of redshift and scale-independent parameters. This set of eight parameters probes the redshift evolution of the average ionization fraction and the quantities related to the morphology of the ionized regions. Results. We tested this ansatz on different reionization scenarios generated using different simulation algorithms and found that it is able to recover the redshift evolution of the average neutral fraction within an absolute deviation ≲0.1. Conclusions. Our framework allows us to interpret 21 cm signal power spectra in terms of parameters related to the state of the IGM. This source model-independent framework is able to efficiently constrain reionization scenarios using multi-redshift power spectrum measurements with ongoing and future radio telescopes such as LOFAR, MWA, HERA, and SKA. This will add independent information regarding the EoR IGM properties. 

Context.High-precision pulsar timing is highly dependent on the precise and accurate modelling of any effects that can potentially impact the data. In particular, effects that contain stochastic elements contribute to some level of corruption and complexity in the analysis of pulsar-timing data. It has been shown that commonly used solar wind models do not accurately account for variability in the amplitude of the solar wind on both short and long timescales. Aims.In this study, we test and validate a new, cutting-edge solar wind modelling method included in theenterprisesoftware suite (widely used for pulsar noise analysis) through extended simulations. We use it to investigate temporal variability in LOFAR data. Our model testing scheme in itself provides an invaluable asset for pulsar timing array (PTA) experiments. Since, improperly accounting for the solar wind signature in pulsar data can induce false-positive signals, it is of fundamental importance to include in any such investigations. Methods.We employed a Bayesian approach utilising a continuously varying Gaussian process to model the solar wind. It uses a spherical approximation that modulates the electron density. This method, which we refer to as a solar wind Gaussian process (SWGP), has been integrated into existing noise analysis software, specificallyenterprise. Our Validation of this model was performed through simulations. We then conduct noise analysis on eight pulsars from the LOFAR dataset, with most pulsars having a time span of ∼11 years encompassing one full solar activity cycle. Furthermore, we derived the electron densities from the dispersion measure values obtained by the SWGP model. Results.Our analysis reveals a strong correlation between the electron density at 1 AU and the ecliptic latitude (ELAT) of the pulsar. Pulsars with |ELAT|< 3° exhibit significantly higher average electron densities. Furthermore, we observed distinct temporal patterns in the electron densities in different pulsars. In particular, pulsars within |ELAT|< 3° exhibit similar temporal variations, while the electron densities of those outside this range correlate with the solar activity cycle. Notably, some pulsars exhibit sensitivity to the solar wind up to 45° away from the Sun in LOFAR data. Conclusions.The continuous variability in electron density offered in this model represents a substantial improvement over previous models, that assume a single value for piece-wise bins of time. This advancement holds promise for solar wind modelling in future International Pulsar Timing Array (IPTA) data combinations.