When dark matter heats up: A model-independent search for noncold behavior

Abstract

This article questions the common assumption of cold dark matter (DM) by exploring the possibility of a nonzero equation of state (EOS) without relying on any parametric approach. In standard cosmological analyses, DM is typically modeled as pressureless dust with <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:msub> <a:mrow> <a:mi>w</a:mi> </a:mrow> <a:mrow> <a:mrow> <a:mi>DM</a:mi> </a:mrow> </a:mrow> </a:msub> <a:mo>=</a:mo> <a:mn>0</a:mn> </a:mrow> </a:math> , an assumption that aligns with large-scale structure formation, supports the empirical success of the <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi mathvariant="normal">Λ</c:mi> </c:math> -cold dark matter model, and simplifies cosmological modeling. However, there is no fundamental reason to exclude a nonzero <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:msub> <f:mi>w</f:mi> <f:mrow> <f:mi>DM</f:mi> </f:mrow> </f:msub> </f:math> from the cosmological framework. In this work, we explore this possibility through nonparametric and parametric reconstructions based on Gaussian process regression. The reconstructions use Hubble parameter measurements from cosmic chronometers, the <h:math xmlns:h="http://www.w3.org/1998/Math/MathML" display="inline"> <h:mrow> <h:mi>Pantheon</h:mi> <h:mo>+</h:mo> <h:mtext>sample</h:mtext> </h:mrow> </h:math> of Type Ia supernovae, and baryon acoustic oscillation (BAO) data from DESI DR1 and DR2. Our findings suggest that a dynamical EOS for DM, although only mildly supported statistically, cannot be conclusively ruled out. Notably, we observe a mild tendency ( <j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline"> <j:mo>∼</j:mo> <j:mn>1</j:mn> <j:mi>σ</j:mi> </j:math> ) toward a negative <l:math xmlns:l="http://www.w3.org/1998/Math/MathML" display="inline"> <l:msub> <l:mi>w</l:mi> <l:mrow> <l:mi>DM</l:mi> </l:mrow> </l:msub> </l:math> at the present epoch, which is most likely due to inconsistencies between the BAO data from DESI and other datasets.