1. A system (100) for constraining extended dark energy models within a cosmological framework, the system (100) comprising: a data unit (102) adapted to obtain observational datasets comprising cosmic microwave background measurements, baryon acoustic oscillation data, and supernova observations; a modelling unit (104) adapted to generate a redshift-dependent energy density model by incorporating a first component proportional to (1 + z) and a second component proportional to (1 + z)² into a baseline ΛCDM cosmology, where z denotes redshift; and a controller (106) operatively coupled to the data unit (102) and the modelling unit (104), characterized in that controller (106) configured to: receive the observational datasets from the data unit (102) and the redshift-dependent energy density model from the modelling unit (104); model cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components; compute theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space; compare the computed observables with the observational datasets using a statistical inference process; adaptively explore the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters; and reconstruct a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.

2. The system (100) as claimed in claim 1, wherein the controller (106) is configured to maintain consistency with early-universe radiation and matter-dominated epochs by constraining deviations to late-time expansion regimes.

3. The system (100) as claimed in claim 1, wherein the controller (106) is configured to compute expansion history, distance measures, and growth of structure parameters.

4. The system (100) as claimed in claim 1, wherein the controller (106) is configured to compare the computed observables with the observational datasets by minimizing a likelihood function defined over combined multi-probe datasets.

5. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reduce redundant evaluations through an optimized sampling or search mechanism during exploration of the parameter space.

6. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reconstruct the dark energy equation-of-state profile by identifying transitions across a phantom divide.

7. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reconstruct the dark energy equation-of-state profile by determining asymptotic behaviour corresponding to de Sitter expansion.

8. The system (100) as claimed in claim 1, wherein the controller (106) is configured to integrate observational datasets within a unified modular architecture enabling scalable processing of high-dimensional parameter spaces.

9. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reduce parameter degeneracy by jointly utilizing multiple observational datasets.

10. A method (300) for constraining extended dark energy models within a cosmological framework, the method (300) is characterized by steps of: receiving observational datasets from a data unit (102) and a redshift-dependent energy density model from a modelling unit (104); modelling cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components; computing theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space; comparing the computed observables with the observational datasets using a statistical inference process; adaptively exploring the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters; and reconstructing a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics. Date: April 08, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to cosmology and a computational modelling system and particularly to a system and method for constraining extended dark energy models.
Description of Related Art
[002] The Lambda Cold Dark Matter (ΛCDM) cosmological model serves as a standard framework for describing the evolution and large-scale structure of the Universe. The model explains a wide range of cosmological observations with high precision. However, discrepancies between early-universe and late-time measurements, including variation in estimation of the Hubble constant, indicate limitations in the current framework. Such inconsistencies create uncertainty in characterization of dark energy and expansion dynamics. Further, increasing complexity of observational datasets introduces challenges in accurate and efficient parameter estimation across extended cosmological models.
[003] Existing approaches for cosmological parameter estimation rely on computational tools and inference frameworks such as CAMB, CLASS, CosmoMC, MontePython, and Cobaya. These solutions utilize Bayesian inference techniques for evaluation of cosmological parameters based on observational datasets including Cosmic Microwave Background (CMB), Baryon Acoustic Oscillation (BAO), and Type Ia Supernovae data. Such frameworks implement standard ΛCDM formulations or generalized dark energy parameterizations and employ sampling-based methods for likelihood evaluation across high-dimensional parameter spaces.
[004] However, such existing approaches involve substantial computational cost due to repeated likelihood evaluations and extensive sampling procedures. These methods exhibit limited efficiency in handling high-dimensional and degenerate parameter spaces. Further, conventional frameworks lack optimization for integration of multiple observational probes within a unified architecture and do not ensure consistent treatment of early-universe constraints alongside extended late-time cosmological variations. Such limitations result in reduced scalability, slower convergence, and restricted capability for precise and interpretable cosmological analysis.
[005] There is thus a need for an improved and advanced system and method for constraining extended dark energy models that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for constraining extended dark energy models. The system comprising a data unit adapted to obtain observational datasets comprising cosmic microwave background measurements, baryon acoustic oscillation data, and supernova observations. The system further comprising a modelling unit adapted to generate a redshift-dependent energy density model by incorporating a first component proportional to (1 + z) and a second component proportional to (1 + z)² into a baseline ΛCDM cosmology, where z denotes redshift. The system further comprising a controller operatively coupled to the data unit and the modelling unit. The controller is configured to receive the observational datasets from the data unit and the redshift-dependent energy density model from the modelling unit, model cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components, compute theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space, compare the computed observables with the observational datasets using a statistical inference process, adaptively explore the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters, and reconstruct a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.
[007] Embodiments in accordance with the present invention further provide a method for constraining extended dark energy models. The method comprising steps of: receiving observational datasets from a data unit and a redshift-dependent energy density model from a modelling unit; modelling cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components; computing theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space; comparing the computed observables with the observational datasets using a statistical inference process; adaptively exploring the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters; and reconstructing a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for constraining extended dark energy models.
[009] Next, embodiments of the present application may provide a system for constraining extended dark energy models that enables efficient constraint of extended dark energy models while maintaining consistency with early-universe cosmology.
[0010] Next, embodiments of the present application may provide a system for constraining extended dark energy models that reduces computational complexity associated with parameter estimation by minimizing redundant likelihood evaluations.
[0011] Next, embodiments of the present application may provide a system for constraining extended dark energy models that improves exploration of high-dimensional and degenerate parameter spaces through optimized inference mechanisms.
[0012] Next, embodiments of the present application may provide a system for constraining extended dark energy models that supports unified integration of multiple observational datasets including Cosmic Microwave Background, Baryon Acoustic Oscillation, and Type Ia Supernovae data within a single framework.
[0013] These and other advantages will be apparent from the present application of the embodiments described herein.
[0014] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0016] FIG. 1 illustrates a block diagram of a system for constraining extended dark energy models, according to an embodiment of the present invention;
[0017] FIG. 2 illustrates a block diagram of a controller of the system for constraining extended dark energy models, according to an embodiment of the present invention; and
[0018] FIG. 3 depicts a flowchart of a method for constraining extended dark energy models, according to an embodiment of the present invention.
[0019] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0020] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0021] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0022] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0023] FIG. 1 illustrates a block diagram of a system 100 for constraining extended dark energy models within a cosmological framework, according to an embodiment of the present invention. The system 100 may provide a scalable and computationally efficient architecture that can systematically process observational datasets, construct redshift-dependent cosmological models, perform parameter estimation, and generate physically interpretable outputs suitable for cosmological analysis.
[0024] In an embodiment of the present invention, the system 100 may differ from conventional computational frameworks by directly integrating the redshift-dependent energy density formulation within an inference architecture. Further, model generation, observational dataset integration, parameter exploration, and equation-of-state reconstruction may be executed within a unified processing pipeline. Unlike sampling-based external frameworks that rely on repeated independent likelihood evaluations, the system 100 may be adapted to coordinate interdependent computations across modules to reduce redundancy and improve convergence efficiency.
[0025] In an embodiment of the present invention, the system 100 may be applicable to domains including cosmological parameter estimation, resolution of Hubble constant discrepancies, Bayesian and non-Bayesian inference optimization, dynamical dark energy modelling, and large-scale structure analysis, thereby enabling broad applicability across theoretical and observational cosmology.
[0026] In an embodiment of the present invention, the system 100 may be adapted to accommodate increasing data volumes and complexity associated with next-generation observational surveys. Further, modular and scalable architecture of the system 100 may enable efficient incorporation of high-resolution datasets without proportional increase in computational cost, thereby ensuring suitability for future cosmological analysis requirements.
[0027] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise a data unit 102, a modelling unit 104, and a controller 106. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0028] In an embodiment of the present invention, the data unit 102 may be adapted to obtain observational datasets comprising cosmic microwave background measurements, baryon acoustic oscillation data, and supernova observations. The observational datasets may comprise parameters indicative of cosmological evolution, including temperature anisotropies, distance scales, luminosity distances, and so forth. In an embodiment of the present invention, the data unit 102 may be further adapted to normalize, align, and temporally synchronize observational datasets obtained from heterogeneous observational probes including cosmic microwave background measurements, baryon acoustic oscillation data, and supernova observations. Each dataset may be associated with distinct uncertainty distributions and redshift ranges. The controller 106 may be adapted to perform weighted integration of the observational datasets by assigning statistical weights based on measurement variance and covariance structures, thereby enabling consistent multi-probe likelihood evaluation and reducing inconsistencies arising from probe-specific biases.
[0029] The data unit 102 may be, but not limited to, a data acquisition interface, a dataset repository, a cloud-based data source, or a distributed storage system, and so forth. The data unit 102 may be adapted to obtain datasets continuously or periodically based on availability of observational data sources. Embodiments of the present invention are intended to include or otherwise cover any type of the data unit 102, including known, related art, and/or later developed technologies.
[0030] In an embodiment of the present invention, the observational datasets may be indicative of cosmological measurements acquired from multiple observational probes. The observational probes may include, but not limited to, space-based observatories, ground-based telescopes, survey missions, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of observational probe sources, including known, related art, and/or later developed technologies.
[0031] In an embodiment of the present invention, the modelling unit 104 may be adapted to generate a redshift-dependent energy density model by incorporating a first component proportional to (1 + z) and a second component proportional to (1 + z)² into a baseline ΛCDM cosmology, where z denotes redshift. In an embodiment of the present invention, the modelling unit 104 may be adapted to implement a redshift-expanded cosmological formulation referred to as an Omega one Omega two Lambda Cold Dark Matter model, The total energy density may be expressed as a combination of a cosmological constant component, a first redshift-dependent component proportional to Omega one multiplied by (1 + z), and a second redshift-dependent component proportional to Omega two multiplied by (1 + z) squared. The modelling unit 104 may be further adapted to regulate contribution of the first redshift-dependent component and the second redshift-dependent component such that the contribution may diminish at high redshift regimes to preserve radiation-dominated and matter-dominated epochs. Thus, ensuring consistency with early-universe physics while enabling controlled deviations in late-time cosmological expansion dynamics.
[0032] The modelling unit 104 may be, but not limited to, a mathematical modelling module, a cosmological parameterization engine, or a computational physics module, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the modelling unit 104, including known, related art, and/or later developed technologies.
[0033] In an embodiment of the present invention, the redshift-dependent energy density model may represent a cosmological formulation that preserves early-universe physical conditions while enabling controlled deviations in late-time expansion dynamics. The model may define energy density contributions as functions of redshift to facilitate analysis of cosmological evolution across different epochs.
[0034] In an embodiment of the present invention, the controller 106 may be operatively coupled to the data unit 102 and the modelling unit 104. The controller 106 may be configured to receive the observational datasets from the data unit 102 and the redshift-dependent energy density model from the modelling unit 104, model cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components. In an embodiment of the present invention, the modelling unit 104 and the controller 106 may be adapted to handle redshift-evolving parameterizations. The energy density components may vary as explicit functions of redshift, resulting in increased dimensionality and non-linearity of the parameter space. The controller 106 may be adapted to stabilize numerical evaluation of such redshift-dependent formulations by enforcing bounded variation constraints and adaptive discretization across redshift intervals. Thus, ensuring computational stability and accuracy during parameter estimation.
[0035] In an embodiment of the present invention, the controller 106 may further align the datasets with corresponding model parameters to enable consistent evaluation across a parameter space. The controller 106 may be configured to model cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components. The controller 106 may enforce constraints associated with radiation-dominated and matter-dominated epochs to maintain physical consistency. The controller 106 may be configured to compute theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space. The theoretical cosmological observables may comprise expansion history, distance measures, growth of structure parameters, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of cosmological observables, including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the controller 106 may be configured to compare the computed observables with the observational datasets using a statistical inference process. The statistical inference process may include evaluation of a likelihood function defined over combined multi-probe datasets to determine agreement between theoretical predictions and observed data. The controller 106 may further be configured to adaptively explore the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters. The controller 106 may utilize an optimized sampling or search mechanism to reduce redundant evaluations and improve convergence efficiency. The controller 106 may be configured to reconstruct a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics. The controller 106 may identify transitions across a phantom divide and determine asymptotic behaviour corresponding to de Sitter expansion.
[0037] In an embodiment of the present invention, the controller 106 may be adapted to analyse the reconstructed dark energy equation-of-state profile to identify transitions across a phantom divide corresponding to an equation-of-state parameter less than negative one and to determine asymptotic convergence towards a de Sitter expansion regime. The reconstructed profile may be further utilized to characterize late-time cosmological acceleration behaviour and to provide physically interpretable insights into evolution of dark energy dynamics across different redshift regimes.
[0038] In an embodiment of the present invention, the controller 106 may be adapted to optimize the statistical inference process by selectively evaluating regions of the parameter space based on adaptive sampling criteria. Further, redundant likelihood computations may be minimized through reuse of previously evaluated parameter regions and pruning of low-probability regions. The controller 106 may be further adapted to implement convergence-driven exploration mechanisms to accelerate parameter estimation. Thus, reducing computational complexity associated with high-dimensional cosmological inference.
[0039] In an embodiment of the present invention, the controller 106 may be further adapted to identify and mitigate parameter degeneracy by evaluating correlation structures within the multidimensional parameter space. Further, degenerate parameter combinations may be resolved by jointly utilizing multiple observational datasets and enforcing consistency constraints across independent probes. The controller 106 may be adapted to refine parameter bounds iteratively based on likelihood gradients and convergence thresholds. Thus, improving robustness and uniqueness of the constrained cosmological parameter estimates.
[0040] In an embodiment of the present invention, the controller 106 may further integrate observational datasets within a unified modular architecture to enable scalable processing of high-dimensional parameter spaces. The controller 106 may jointly utilize multiple observational datasets to reduce parameter degeneracy and improve robustness of parameter estimation.
[0041] In an embodiment of the present invention, the controller 106 may be adapted to support scalable execution by distributing computational tasks associated with dataset processing, cosmological computation, and parameter estimation across parallel processing units and distributed computing resources. The modular architecture may enable independent scaling of the data unit 102, the modelling unit 104, and the controller 106 based on dataset size and parameter dimensionality. Thus, facilitating efficient processing of large-scale observational datasets generated from next-generation cosmological survey missions.
[0042] The controller 106 may be, but not limited to, a processing unit, a control unit, a central computing system, a high-performance computing platform, or a distributed processing architecture, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the controller 106, including known, related art, and/or later developed technologies. The controller 106 may further be explained in detail in conjunction with FIG. 2.
[0043] FIG. 2 illustrates components of the controller 106 of the system 100, according to an embodiment of the present invention. The controller 106 may comprise a data processing module 200, a cosmological computation module 202, a parameter estimation module 204, and a reconstruction module 206. Further, the controller 106 may be configured to enable parallel execution of computations across multiple processing cores or distributed computing resources. Such architecture may facilitate efficient handling of high-dimensional parameter spaces and large observational datasets.
[0044] In an embodiment of the present invention, the data processing module 200 may be configured to receive observational datasets from the data unit 102. The observational datasets may comprise cosmic microwave background measurements, baryon acoustic oscillation data, supernova observations. The data processing module 200 may be further configured to process the received observational datasets to generate processed datasets suitable for cosmological analysis. The processing of the observational datasets may include, but not limited to, normalization, filtering, structuring, and integration of multi-probe datasets.
[0045] In an embodiment of the present invention, the data processing module 200 may be further configured to perform multi-probe data integration by combining observational datasets obtained from different cosmological probes into a unified data structure. The data processing module 200 may be configured to assign statistical weights to each dataset based on measurement uncertainty and observational variance. The data processing module 200 may be configured to align the datasets across a common redshift range to enable consistent comparison during parameter estimation. Such integration may enable joint analysis of multiple observational datasets within a unified framework. Further, the data processing module 200 may be configured to transmit the processed datasets to the cosmological computation module 202.
[0046] In an embodiment of the present invention, the cosmological computation module 202 may be configured to receive the processed datasets from the data processing module 200 and the redshift-dependent energy density model from the modelling unit 104. The cosmological computation module 202 may be further configured to compute theoretical cosmological observables corresponding to the redshift-dependent energy density model. The theoretical cosmological observables may include, but not limited to, expansion history, distance measures, growth of structure parameters, and so forth. Further, the cosmological computation module 202 may be configured to preserve early-universe physical conditions during computation of the theoretical cosmological observables.
[0047] In an embodiment of the present invention, the cosmological computation module 202 may be further configured to enforce early-universe constraints during computation of the theoretical cosmological observables. The cosmological computation module 202 may be configured to maintain consistency with radiation-dominated and matter-dominated epochs by restricting deviation of the redshift-dependent components within predefined bounds at low redshift regimes. Such constraint enforcement may ensure physical validity of the cosmological model across different epochs. The cosmological computation module 202 may be further configured to transmit the computed cosmological observables to the parameter estimation module 204.
[0048] In an embodiment of the present invention, the parameter estimation module 204 may be configured to receive the computed cosmological observables from the cosmological computation module 202. The parameter estimation module 204 may be further configured to compare the computed cosmological observables with the observational datasets using the statistical inference process. The statistical inference process may include evaluation of the likelihood function defined over combined multi-probe datasets. Further, the parameter estimation module 204 may be configured to explore the multidimensional parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters. The parameter estimation module 204 may be configured to utilize an optimized sampling or search mechanism to reduce redundant evaluations during exploration of the parameter space.
[0049] In an embodiment of the present invention, the parameter estimation module 204 may be further configured to reduce parameter degeneracy by jointly utilizing multiple observational datasets during exploration of the multidimensional parameter space. The parameter estimation module 204 may be configured to identify correlated parameter regions and refine parameter bounds based on combined likelihood evaluation. Such reduction in degeneracy may improve robustness and reliability of constrained parameter estimates.
[0050] In an embodiment of the present invention, the parameter estimation module 204 may be further configured to implement an optimized sampling mechanism for exploration of the parameter space. The parameter estimation module 204 may be configured to selectively evaluate parameter regions based on convergence criteria and likelihood gradients to reduce redundant evaluations. Such optimization may improve computational efficiency and accelerate convergence during statistical inference. The parameter estimation module 204 may be further configured to transmit the constrained estimates to the reconstruction module 206.
[0051] In an embodiment of the present invention, the reconstruction module 206 may be configured to receive the constrained estimates from the parameter estimation module 204. The reconstruction module 206 may be further configured to reconstruct the dark energy equation-of-state profile based on the constrained estimates. The reconstruction module 206 may be configured to identify transitions across a phantom divide and determine asymptotic behaviour corresponding to de Sitter expansion. Further, the reconstruction module 206 may be configured to generate output indicative of late-time cosmological dynamics.
[0052] FIG. 3 depicts a flowchart of a method 300 for constraining extended dark energy models, according to an embodiment of the present invention.
[0053] At step 302, the system 100 may receive the observational datasets from the data unit 102 and the redshift-dependent energy density model from the modelling unit 104.
[0054] At step 304, the system 100 may model the cosmological evolution by preserving early-universe physical conditions while enabling the late-time deviations through the incorporated components.
[0055] At step 306, the system 100 may compute the theoretical cosmological observables corresponding to the redshift-dependent energy density model across the parameter space.
[0056] At step 308, the system 100 may compare the computed observables with the observational datasets using the statistical inference process.
[0057] At step 310, the system 100 may adaptively explore the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters.
[0058] At step 312, the system 100 may reconstruct the dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.
[0059] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0060] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for constraining extended dark energy models within a cosmological framework, the system (100) comprising:
a data unit (102) adapted to obtain observational datasets comprising cosmic microwave background measurements, baryon acoustic oscillation data, and supernova observations;
a modelling unit (104) adapted to generate a redshift-dependent energy density model by incorporating a first component proportional to (1 + z) and a second component proportional to (1 + z)² into a baseline ΛCDM cosmology, where z denotes redshift; and
a controller (106) operatively coupled to the data unit (102) and the modelling unit (104), characterized in that controller (106) configured to:
receive the observational datasets from the data unit (102) and the redshift-dependent energy density model from the modelling unit (104);
model cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components;
compute theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space;
compare the computed observables with the observational datasets using a statistical inference process;
adaptively explore the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters; and
reconstruct a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.
2. The system (100) as claimed in claim 1, wherein the controller (106) is configured to maintain consistency with early-universe radiation and matter-dominated epochs by constraining deviations to late-time expansion regimes.
3. The system (100) as claimed in claim 1, wherein the controller (106) is configured to compute expansion history, distance measures, and growth of structure parameters.
4. The system (100) as claimed in claim 1, wherein the controller (106) is configured to compare the computed observables with the observational datasets by minimizing a likelihood function defined over combined multi-probe datasets.
5. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reduce redundant evaluations through an optimized sampling or search mechanism during exploration of the parameter space.
6. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reconstruct the dark energy equation-of-state profile by identifying transitions across a phantom divide.
7. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reconstruct the dark energy equation-of-state profile by determining asymptotic behaviour corresponding to de Sitter expansion.
8. The system (100) as claimed in claim 1, wherein the controller (106) is configured to integrate observational datasets within a unified modular architecture enabling scalable processing of high-dimensional parameter spaces.
9. The system (100) as claimed in claim 1, wherein the controller (106) is configured to reduce parameter degeneracy by jointly utilizing multiple observational datasets.
10. A method (300) for constraining extended dark energy models within a cosmological framework, the method (300) is characterized by steps of:
receiving observational datasets from a data unit (102) and a redshift-dependent energy density model from a modelling unit (104);
modelling cosmological evolution by preserving early-universe physical conditions while enabling late-time deviations through the incorporated components;
computing theoretical cosmological observables corresponding to the redshift-dependent energy density model across a parameter space;
comparing the computed observables with the observational datasets using a statistical inference process;
adaptively exploring the parameter space to obtain constrained estimates of cosmological parameters including the Hubble constant and energy density parameters; and
reconstructing a dark energy equation-of-state profile based on the constrained estimates to characterize late-time cosmological dynamics.
Date: April 08, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant