%0 Generic %1 he2020forwardmodelling %A He, Qiuhan %A Robertson, Andrew %A Nightingale, James %A Cole, Shaun %A Frenk, Carlos S. %A Massey, Richard %A Amvrosiadis, Aristeidis %A Li, Ran %A Cao, Xiaoyue %A Etherington, Amy %D 2020 %K astrophysics cosmology dark_matter hep-ph machine_learning phd %R 10.1093/mnras/stac191 %T A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses %U http://arxiv.org/abs/2010.13221 %X A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other dark matter models, such as a warm thermal relic (WDM), predict a cutoff in the mass function at a mass which, for popular models, lies approximately between $10^7$ and $10^10~M_ødot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information about low-mass dark haloes lying along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_DM$. With 50 strong lenses at Hubble Space Telescope resolution and a maximum pixel signal-to-noise ratio of $\sim50$, the expected median 2$\sigma$ constraint for a CDM-like model (with a halo mass cutoff at $10^7~M_ødot$) is $m_DM > 4.10 \, keV$ (50% chance of constraining $m_DM$ to be better than 4.10 keV). If, however, the dark matter is a warm particle of $m_DM=2.2 \, keV$, our 'Approximate Bayesian Computation' method would result in a median estimate of $m_DM$ between 1.43 and 3.21 keV. Our method can be extended to the large samples of strong lenses that will be observed by future telescopes, and could potentially rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).

@misc{he2020forwardmodelling, abstract = {A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other dark matter models, such as a warm thermal relic (WDM), predict a cutoff in the mass function at a mass which, for popular models, lies approximately between $10^7$ and $10^{10}~{\rm M}_\odot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information about low-mass dark haloes lying along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_\mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and a maximum pixel signal-to-noise ratio of $\sim50$, the expected median 2$\sigma$ constraint for a CDM-like model (with a halo mass cutoff at $10^{7}~{\rm M}_\odot$) is $m_\mathrm{DM} > 4.10 \, \mathrm{keV}$ (50% chance of constraining $m_{\rm DM}$ to be better than 4.10 keV). If, however, the dark matter is a warm particle of $m_\mathrm{DM}=2.2 \, \mathrm{keV}$, our 'Approximate Bayesian Computation' method would result in a median estimate of $m_\mathrm{DM}$ between 1.43 and 3.21 keV. Our method can be extended to the large samples of strong lenses that will be observed by future telescopes, and could potentially rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).}, added-at = {2023-02-08T14:53:41.000+0100}, author = {He, Qiuhan and Robertson, Andrew and Nightingale, James and Cole, Shaun and Frenk, Carlos S. and Massey, Richard and Amvrosiadis, Aristeidis and Li, Ran and Cao, Xiaoyue and Etherington, Amy}, biburl = {https://www.bibsonomy.org/bibtex/24a4212cf617ac2975748456ff198b712/intfxdx}, description = {A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses}, doi = {10.1093/mnras/stac191}, interhash = {d34de74c247e1d88d68f850c25abf72b}, intrahash = {4a4212cf617ac2975748456ff198b712}, keywords = {astrophysics cosmology dark_matter hep-ph machine_learning phd}, note = {cite arxiv:2010.13221Comment: Accepted by MNRAS. Comments welcome}, timestamp = {2023-02-08T14:53:41.000+0100}, title = {A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses}, url = {http://arxiv.org/abs/2010.13221}, year = 2020 }