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Abstract / Description of output
We present constraints on the Dark Scattering model through cosmic shear measurements from the Kilo Degree Survey (KiDS1000), using an accelerated pipeline with novel emulators produced with CosmoPower. Our main emulator, for the Dark Scattering non-linear matter power spectrum, is trained on predictions from the halo model reaction framework, previously validated against simulations. Additionally, we include the effects of baryonic feedback from HMcode2016, whose contribution is also emulated. We analyse the complete set of statistics of KiDS-1000, namely Band Powers, COSEBIs and Correlation Functions, for Dark Scattering in two distinct cases. In the first case, taking into account only KiDS cosmic shear data, we constrain the amplitude of the dark energy – dark matter interaction to be |𝐴ds| ≲ 20 b/GeV at 68% C.L. Furthermore, we add information from the cosmic
microwave background (CMB) from Planck, along with baryon acoustic oscillations (BAO) from 6dFGS, SDSS and BOSS, approximating a combined weak lensing + CMB + BAO analysis. From this combination, we constrain 𝐴ds = 10.6+4.5−7.3b/GeV at 68% C.L. We confirm that with this estimated value of 𝐴ds the interacting model considered in this work offers a promising alternative to solve the 𝑆8 tension.
microwave background (CMB) from Planck, along with baryon acoustic oscillations (BAO) from 6dFGS, SDSS and BOSS, approximating a combined weak lensing + CMB + BAO analysis. From this combination, we constrain 𝐴ds = 10.6+4.5−7.3b/GeV at 68% C.L. We confirm that with this estimated value of 𝐴ds the interacting model considered in this work offers a promising alternative to solve the 𝑆8 tension.
Original language | English |
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Pages (from-to) | 3914-3925 |
Number of pages | 12 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 532 |
Issue number | 4 |
Early online date | 8 Jul 2024 |
DOIs | |
Publication status | Published - 1 Aug 2024 |
Keywords / Materials (for Non-textual outputs)
- cosmology: theory
- observations
- large-scale structure of the Universe
- methods: numerical
- machine learning
- statistical
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