DAIMON v2.1: A Canonical Scalar Field Model with Informational Dark Energy Tested Against Cosmological Data

We present the first confrontation of the DAIMON v2.1 model –a canonical scalar-field framework driven by an informational energy component– with current cosmological observations. In this formulation, the cosmological constant is replaced by a dynamically evolving density, Ωinfo(z), parameterized through a log-normal activation profile peaked at intermediate redshift. Unlike many alternative dark-energy scenarios that either violate physical consistency (e.g., phantom models) or fail to simultaneously fit multiple datasets, DAIMON remains strictly canonical, guarantees w(z) ≥ −1 across the entire redshift range, and yields a smooth, monotonic evolution of both φ(z) and V (φ).Using the Pantheon+ supernova sample in combination with BAO measurements from SDSS/BOSS, we show that DAIMON achieves competitive fits without fine-tuning. A grid-based χ2 analysis identifies regions of parameter space that minimize residuals while ensuring Ωinfo(z) ≪ 10−2 at recombination, thereby preserving compatibility with CMB distance priors. These results establish DAIMON v2.1 as a theoretically robust and observationally competitive alternative to ΛCDM, offering not only empirical adequacy but also a distinct ontological interpretation rooted in informational cosmology.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-30
DOI
https://doi.org/10.5281/zenodo.17238888
Primary Topic
Cosmology and Gravitation Theories
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DAIMON v2.1: A Canonical Scalar Field Model with Informational Dark Energy Tested Against Cosmological Data

Zenodo (CERN European Organization for Nuclear Research)
Cosmology and Gravitation Theories
article

DAIMON v2.1: A Canonical Scalar Field Model with Informational Dark Energy Tested Against Cosmological Data

article en

Abstract

We present the first confrontation of the DAIMON v2.1 model –a canonical scalar-field framework driven by an informational energy component– with current cosmological observations. In this formulation, the cosmological constant is replaced by a dynamically evolving density, Ωinfo(z), parameterized through a log-normal activation profile peaked at intermediate redshift. Unlike many alternative dark-energy scenarios that either violate physical consistency (e.g., phantom models) or fail to simultaneously fit multiple datasets, DAIMON remains strictly canonical, guarantees w(z) ≥ −1 across the entire redshift range, and yields a smooth, monotonic evolution of both φ(z) and V (φ).Using the Pantheon+ supernova sample in combination with BAO measurements from SDSS/BOSS, we show that DAIMON achieves competitive fits without fine-tuning. A grid-based χ2 analysis identifies regions of parameter space that minimize residuals while ensuring Ωinfo(z) ≪ 10−2 at recombination, thereby preserving compatibility with CMB distance priors. These results establish DAIMON v2.1 as a theoretically robust and observationally competitive alternative to ΛCDM, offering not only empirical adequacy but also a distinct ontological interpretation rooted in informational cosmology.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 50%
Cosmology and Gravitation Theories
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

DAIMON v2.1: A Canonical Scalar Field Model with Informational Dark Energy Tested Against Cosmological Data · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS