Probing decaying dark matter using the post-reionization HI 21-cm signal

Abstract We propose the HI 21-cm power spectrum from the post-reionization epoch as a probe of a cosmological model with decaying dark matter particles. The unstable particles are assumed to undergo a 2-body decay into a massless and massive daughter. We assume that a fraction f of the total dark matter budget to be unstable and quantify the decay using the life time $$\Gamma ^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>Γ</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> and the relative mass splitting $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> between the parent and the massive daughter. The redshift space anisotropic power spectrum of the post-reionization 21-cm signal brightness temperature, as a tracer of the dark matter clustering, imprints the decaying dark matter model through its effect on background evolution and the suppression of power on small scales. We find that with an idealized futuristic intensity mapping experiment with a SKA1-Mid like radio-array, $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> and $$\Gamma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Γ</mml:mi> </mml:math> can be measured at $$3.37\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.37</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$6.86\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.86</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> around their fiducial values of $$\epsilon = 0.012$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ϵ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.012</mml:mn> </mml:mrow> </mml:math> and $$\Gamma = 0.008~\textrm{Gyr}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Γ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.008</mml:mn> <mml:mspace/> <mml:msup> <mml:mtext>Gyr</mml:mtext> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> respectively. When only the foreground-free window is considered in the k -space, these percentage errors in $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> and $$\Gamma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Γ</mml:mi> </mml:math> degrade to $$9.91\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>9.91</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$12.03\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>12.03</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> respectively. The forecasts under identical assumptions for a second, literature-anchored benchmark $$(\epsilon =1.14\times 10^{-4},$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>ϵ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1.14</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> $$\Gamma =1.04\times 10^{-3}~{\textrm{Gyr}}^{-1})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Γ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1.04</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mspace/> <mml:msup> <mml:mrow> <mml:mtext>Gyr</mml:mtext> </mml:mrow>