lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer
base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.
Joint analyses of small-scale cosmological structure probes are relatively unexplored and promise to advance measurements of microphysical dark matter properties using heterogeneous data. Here, we present a multidimensional analysis of dark matter su
bstructure using strong gravitational lenses and the Milky Way (MW) satellite galaxy population, accounting for degeneracies in model predictions and using covariances in the constraining power of these individual probes for the first time. We simultaneously infer the projected subhalo number density and the half-mode mass describing the suppression of the subhalo mass function in thermal relic warm dark matter (WDM), $M_{mathrm{hm}}$, using the semianalytic model $mathrm{texttt{Galacticus}}$ to connect the subhalo population inferred from MW satellite observations to the strong lensing host halo mass and redshift regime. Combining MW satellite and strong lensing posteriors in this parameter space yields $M_{mathrm{hm}}<10^{7.0} M_{mathrm{odot}}$ (WDM particle mass $m_{mathrm{WDM}}>9.7 mathrm{keV}$) at $95%$ confidence and disfavors $M_{mathrm{hm}}=10^{7.4} M_{mathrm{odot}}$ ($m_{mathrm{WDM}}=7.4 mathrm{keV}$) with a 20:1 marginal likelihood ratio, improving limits on $m_{mathrm{WDM}}$ set by the two methods independently by $sim 30%$. These results are marginalized over the line-of-sight contribution to the strong lensing signal, the mass of the MW host halo, and the efficiency of subhalo disruption due to baryons and are robust to differences in the disruption efficiency between the MW and strong lensing regimes at the $sim 10%$ level. This work paves the way for unified analyses of next-generation small-scale structure measurements covering a wide range of scales and redshifts.
The free streaming length of dark matter particles determines the abundance of structure on sub-galactic scales. We present a statistical technique, amendable to any parameterization of subhalo density profile and mass function, to probe dark matter
on these scales with quadrupole image lenses. We consider a warm dark matter particle with a mass function characterized by a normalization and free streaming scale $m_{rm{hm}}$. We forecast bounds on dark matter warmth for 120-180 lenses, attainable with future surveys, at typical lens (source) redshifts of 0.5 (1.5) in early-type galaxies with velocity dispersions of 220-270 km/sec. We demonstrate that limits on $m_{rm{hm}}$ deteriorate rapidly with increasing uncertainty in image fluxes, underscoring the importance of precise measurements and accurate lens models. For our forecasts, we assume the deflectors in the lens sample do not exhibit complex morphologies, so we neglect systematic errors in their modeling. Omitting the additional signal from line of sight halos, our constraints underestimate the true power of the method. Assuming cold dark matter, for a low normalization, corresponding the destruction of all subhalos within the host scale radius, we forecast $2sigma$ bounds on $m_{rm{hm}}$ (thermal relic mass) of $10^{7.5} (5.0)$, $10^{8} (3.6)$, and $10^{8.5} (2.7) M_{odot} left(rm{keV}right)$ for flux errors of $2%$, $4%$, and $8%$. With a higher normalization, these constraints improve to $10^{7.2} (6.6)$, $10^{7.5} (5.3) $, and $10^{7.8} (4.3) M_{odot} left(rm{keV}right)$ with 120 systems. We are also able to measure the normalization of the mass function, which has implications for baryonic feedback models and tidal stripping.
