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Direct tests of General Relativity under screening effect with galaxy-scale strong lensing systems

Yujie Lian11affiliation: Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing 102206, China; [email protected] 22affiliation: Department of Astronomy, Beijing Normal University, Beijing 100875, China; , Shuo Cao11affiliation: Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing 102206, China; [email protected] 2$\ast$2$\ast$affiliationmark: , Tonghua Liu33affiliation: School of Physics and Optoelectronic, Yangtze University, Jingzhou 434023, China; , Marek Biesiada44affiliation: National Centre for Nuclear Research, Pasteura 7, 02-093 Warsaw, Poland , and Zong-Hong Zhu11affiliation: Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing 102206, China; [email protected] 22affiliation: Department of Astronomy, Beijing Normal University, Beijing 100875, China;
Abstract

Observations of galaxy-scale strong gravitational lensing (SGL) systems have enabled unique tests of nonlinear departures from general relativity (GR) on the galactic and supergalactic scales. One of the most important cases of such tests is constraints on the gravitational slip between two scalar gravitational potentials. In this paper, we use a newly compiled sample of strong gravitational lenses to test the validity of GR, focusing on the screening effects on the apparent positions of lensed sources relative to the GR predictions. This is the first simultaneous measurement of the Post-Newtonian (PN) parameter (γPN\gamma_{PN}) and the screening radius (Λ\Lambda) without any assumptions about the contents of the Universe. Our results suggest that the measured PPN is marginally consistent with GR (γPN=1\gamma_{PN}=1) with increasing screening radius (Λ=10300\Lambda=10-300kpc), although the choice of lens models may have a significant influence on the final measurements. Based on a well-defined sample of 5000 simulated strong lenses from the forthcoming LSST, our methodology will provide a strong extragalactic test of GR with the accuracy of 0.5%, assessed up to scales of Λ300\Lambda\sim 300 kpc. For the current and future observations of available SGL systems, there is no noticeable evidence indicating some specific cutoff scales on kpc-Mpc scales, beyond which new gravitational degrees of freedom are expressed.

Subject headings:
gravitational lensing: strong - galaxies: structure - cosmology: observations

1. Introduction

As a successful theory of a dynamical space time where general coordinate invariance acts an essential role, Einstein’s Theory of General Relativity (GR) has passed all observational test so far (Ashby, 2002; Bertotti et al., 2003), from the millimetre scale in the laboratory to the Solar System tests and consistency with gravitational wave emission by binary pulsars (Dyson et al., 1920; Pound & Rebka, 1960; Shapiro, 1964; Taylor et al., 1979; Shapiro, 2004; Williams et al., 2004). A recent review about the status of experimental tests of GR and of theoretical frameworks for analyzing them can be found in Will (2014). Extrapolating to the cosmological scales, GR seems to precisely characterize the history and evolution of the Universe, as well as the large scale structure of space-time and matter. On the other hand, a mysterious component with negative pressure, dubbed dark energy (DE) (Copeland et al., 2006) responsible for the accelerating expansion of the Universe should be invoked in the framework of GR, and the simplest candidate of DE is the cosmological constant (introduced for different reasons at the early years of the GR). However, the inconsistency between the observed value of this constant and the theoretical value of zero-point energy predicted by quantum field theory is considerable (Weinberg, 1989), which is a fairly challenging problem in theoretical physics and opens up a discussion of whether the GR could fail at larger, cosmological scales (Koyama, 2016). Therefore, besides presenting possible solutions to the cosmological constant problem (Sahni & Starobinsky, 2000; Padmanabhan, 2003; Peebles & Ratra, 2003; Mukohyama & Randall, 2004; Nojiri & Odintsov, 2006; Padilla, 2015), it would be equally important to formulate and quantitatively interpret the tests of GR on the extra-galactic scale with high precision, and the parameterized post-Newtonian (PPN) framework (Thorne & Will, 1971) offers an interesting approach to test the departure from GR. The post-Newtonian parameter denoted by γPN\gamma_{PN}, which measures the amount of spatial curvature per unit mass, can be used to probe the deviations from GR (i.e. from the value γPN=1\gamma_{PN}=1) on significantly larger length scales due to the scale independence.

Recently, applying time-delay measurements of strongly lensed quasars, Jyoti et al. (2019) proposed a new phenomenological model of gravitational screening as a step discontinuity in the measurement of γPN\gamma_{PN} at a cutoff scale Λ\Lambda to test the nonlinear departure from GR. In this model, Jyoti et al. (2019) made use of two key characteristics of most modified gravity (MG) theories: gravitational slip, meaning the difference between the gravitational potential created by temporal and spatial metric components (Daniel et al., 2008), and screening, which can restore GR on small scales but may lead to distinct signatures in the large scale structure of the universe (Vainshtein, 1972; Jain & Khoury, 2010; Joyce et al., 2015; Ferreira, 2019). It is worthy to note that the constraint given by Jyoti et al. (2019) is |γPN1|0.2×(Λ/100kpc)|\gamma_{\rm PN}-1|\leq 0.2\times(\Lambda/100\,\rm kpc), with screening length Λ=10200\Lambda=10-200 kpc, which is limited by 10110210^{-1}\sim 10^{-2} precision of the strong lensing time delay measurements using quasars. More recently, (Abadi & Kovetz, 2021) proposed to use strongly lensed fast radio burst time-delay measurements to put constraints on γPN\gamma_{PN}, and yield constraints as tight as |γPN1|0.04×(Λ/100kpc)×[N/10]1/2\left|\gamma_{PN}-1\right|\leqslant 0.04\times(\Lambda/100\;kpc)\times[N/10]^{-1/2} (NN denoting the sample size), which indicates that ten events alone could place constraints at a level of 10% in the range of Λ=10300kpc\Lambda=10-300\;kpc. Therefore, extra cosmological probes are also required to make complementary and more precise studies of the screened MG model featuring a gravitational slip.

In the last decade, strong gravitational lensing (SGL) of galaxies have become powerful and promising probes to study astrophysical issues (Treu, 2010), such as the structure and evolution of galaxies, the parameters that characterize the geometry, content, and expansion of the Universe. In recent years, great efforts have been made in estimating cosmological parameters through SGL (Zhu, 2000; Chae, 2003; Chae et al., 2004; Mitchell et al., 2005; Grillo et al., 2008; Oguri et al., 2008; Zhu & Sereno, 2008a; Zhu et al., 2008b; Cao & Zhu, 2012; Cao, Covone & Zhu, 2012; Cao et al., 2012; Cao, Zhu & Zhao, 2012; Biesiada, 2006; Biesiada, Piórkowska, & Malec, 2010; Collett & Auger, 2014; Cardone et al., 2016; Amante et al., 2020), in measuring the Hubble constant H0H_{0} (Fassnacht et al., 2002; Bonvin et al., 2016; Suyu et al., 2017; Birrer et al., 2019; Wong et al., 2020), the cosmic curvature (Räsänen et al., 2015; Xia et al., 2017; Qi et al., 2019; Liu et al., 2020; Zhou & Li, 2020), and the distribution of matter in massive galaxies acting as lenses (Zhu & Wu, 1997; Mao & Schneider, 1998; Jin et al., 2000; Keeton, 2001; Kochanek & White, 2001; Ofek et al., 2003; Treu et al., 2006; Chen et al., 2019; Geng et al., 2021). It should be stressed that all of the studies mentioned above have been carried out under the assumption of GR. On account of some controversies in the framework of GR, for instance the aforementioned cosmological constant problem, and the Hubble tension between different cosmological probes, we are prompted to test GR in this paper. Moreover, SGL by galaxies provide a unique opportunity to test GR in kpc scales (Bolton et al., 2006, 2008; Smith, 2009; Schwab et al., 2010; Cao et al., 2017; Collett et al., 2018; Yang et al., 2020; Liu et al., 2022), with reasonable prior assumptions and independent measurements of background cosmology and appropriate descriptions of the internal structure of lensing galaxies. Inspired by Schwab et al. (2010) and Cao et al. (2017), who used galaxy-scale SGL systems with measured stellar velocity dispersions to test GR, we utilize the recently combined sample of 158 SGL systems, which is summarized in Cao et al. (2015); Shu et al. (2017). Based on this sample we investigate gravitational slip with screening effect in a phenomenological model. Considering the limitation of the sample size of available SGL systems, we also take advantage of the simulated future LSST SGL data to assess the expected improvement in precision and study the degeneracy between γPN\gamma_{PN} and Λ\Lambda.

In this paper, we focus on demonstrating the possibilities of testing gravitational slip at super-galactic screening scales Λ\Lambda by the observed velocity dispersion of SGL systems. As already mentioned, our approach to test gravitational slip at super-galactic screening scales Λ\Lambda, invokes a combination of lensing and stellar kinematics. Two different lens models, with a power law density profiles for the total mass density and luminous density (i.e. stellar mass density) assumed, are used to illustrate the influence of the lens mass distribution on the PPN parameter γPN\gamma_{PN}. It would be interesting to figure out if the constraints from the current and future SGL systems reveal any specific cutoff scales, beyond which MG is relevant, or GR is still valid with high precision up to scales of Λ300\Lambda\sim 300 kpc. Following the simplifying assumptions presented by (Jyoti et al., 2019), we also assume that the screening radius Λ\Lambda is bigger than the Einstein radius of the lens galaxy, and thus the stellar dynamics within the lens are not changed, but the departure from GR at larger radius would impact the photon path. This assumption will allow us to investigate the regime of super-galactic screeing, complementary to the discussions in (Bolton et al., 2006; Smith, 2009; Collett et al., 2018), where the screening effects occur within the galaxy and the post-Newtonian parameter γPN\gamma_{PN} is constrained through two different mass measurements: the dynamical mass measured from the stellar kinematics of the deflector galaxy, and the lensing mass inferred from the lensing image.

This paper is organized as follows. In Sec. 2, we give a brief introduction of the model we used to evaluate the velocity dispersion of lensing galaxies. In Sec. 3, all the observational and simulated data sets are introduced. We perform a Markov chain Monte Carlo (MCMC) analysis using different data sets, and discuss our results in Sec.4. Finally, conclusions are summarized in Sec. 5.

2. The model

The post-Newtonian variables are applied to quantify the behavior of gravity and deviations from GR, and we adopt the notation and conventions of Ma & Bertschinger (1995) to express the perturbed Robertson-Walker metric

ds2=a2(η)[(1+2Φ)dη2+(12Ψ)dx2],ds^{2}=a^{2}(\eta)\left[-(1+2\Phi)d\eta^{2}+(1-2\Psi)d\vec{x}^{2}\right], (1)

to characterize the cosmological space-time, where a(η)a(\eta) is the cosmological scale factor (η\eta being the conformal time), Φ\Phi and Ψ\Psi are Newtonian and longitudinal gravitational potentials. In the weak-field limit GR predicts Φ=Ψ\Phi=\Psi and a gravitational slip manifested as ΦΨ\Phi\neq\Psi occurs in MG theories, for instance scalar-tensor theories (Schimd et al., 2005), f(R)f(R) theories (Chiba et al., 2003; Sotiriou, 2010), Dvali-Gabadadze-Porrati (DGP) model (Dvali et al., 2000; Sollerman et al., 2009), and massive gravity (Dubovsky, 2004). We remark here that for a modified gravity theory where the Newtonian and longitudinal gravitational potentials are different, i.e. the gravitational slip is not zero, the Poisson equation 2Φ=4πGa2ρ\nabla^{2}\Phi=4\pi Ga^{2}\rho usually is modified into a form 2Φ=4πGμa2ρ\nabla^{2}\Phi=4\pi G\mu a^{2}\rho which is different from that of GR, where the modified gravity parameters are included in the μ\mu term (Koyama, 2016). Rigorous approach would thus require to focus on a specific theory of modified gravity. Similar to Jyoti et al. (2019) we are taking a heuristic approach assuming that the screening mechanism recovers GR exactly in the central parts of lensing galaxies, so that standard spherical Jeans equation is sufficient to recover the dynamical mass inside the Einstein radius.

The deviation from GR is quantified by the ratio γPN=Ψ/Φ\gamma_{PN}=\Psi/\Phi, and γPN=1\gamma_{PN}=1 represents the GR. Gravitational screening mechanisms can restore GR in regions of high density, potential or curvature, such as galaxies, but allow modification of gravity at cosmological scales, which corresponds to suppressing the additional gravitational degrees of freedom within a certain region. Recent reviews about screening mechanisms and the experimental tests of such theories are available (Joyce et al., 2015; Ferreira, 2019). In order to model the effect of screening, we follow the notations in Jyoti et al. (2019) and suppose the gravitational slip to be a step-wise function, discontinuous at a screening radius Λ\Lambda, which is assumed to be larger than the (physical) Einstein radius, Λ>RE=DLθE\Lambda>R_{E}=D_{L}\theta_{E}.

Photon geodesics feel the sum of Newtonian and longitudinal gravitational potentials, which is defined as ΣΦ+Ψ\Sigma\equiv\Phi+\Psi. Then, for a spherically-systematic mass distribution, the departure from GR can be expressed as

Σ=[2+(γPN1)Θ(rΛ)]Φ(r),\Sigma=[2+(\gamma_{\rm PN}-1)\Theta(r-\Lambda)]\Phi(r), (2)

where r and Λ\Lambda are physical distances, and Θ\Theta is the Heaviside step function. For rΛr\leq\Lambda, Eq. (2) reduces to Σ=2Φ\Sigma=2\Phi as in GR. According to the definition in Narayan & Bartelmann (1996), the lensing potential is

ψ(θ)=1c2DLSDLDSΣ(DLθ,𝒵)𝑑𝒵\psi(\mathbf{\theta})=\frac{1}{c^{2}}\frac{D_{LS}}{D_{L}D_{S}}\int\Sigma(D_{L}\theta,\mathcal{Z})d\mathcal{Z}\, (3)

where DLD_{L}, DSD_{S}, DLSD_{LS} are the angular diameter distances from observer to the lens, the source, and between the lens and source, and 𝒵\mathcal{Z} represents the distance along the line of sight. Then, the lensing potential can be decomposed as

ψ=ψGR+(γPN1)Δψ,\psi=\psi_{\rm GR}+(\gamma_{\rm PN}-1)\Delta\psi, (4)

In this model, ψGR\psi_{\rm GR} and Δψ\Delta\psi, denote the lensing potential in GR and the correction due to screening, which can be expressed as

ψGR(θ)=2c2DLSDLDSΦ(DLθ,𝒵)𝑑𝒵\psi_{GR}(\mathbf{\theta})=\frac{2}{c^{2}}\frac{D_{LS}}{D_{L}D_{S}}\int\Phi(D_{L}\theta,\mathcal{Z})d\mathcal{Z}\, (5)
Δψ(θ)=1c2DLSDLDSΘ(rΛ)Φ(DLθ,𝒵)𝑑𝒵\Delta\psi(\mathbf{\theta})=\frac{1}{c^{2}}\frac{D_{LS}}{D_{L}D_{S}}\int\Theta(r-\Lambda)\Phi(D_{L}\theta,\mathcal{Z})d\mathcal{Z}\, (6)

respectively. Assuming the power-law density profiles for the total mass density for elliptical galaxies,

ρ(r)=ρ0(rr0)γ\rho(r)=\rho_{0}\left(\frac{r}{r_{0}}\right)^{-\gamma} (7)

where the constants: ρ0\rho_{0} and r0r_{0}, set the total mass of the lens and solving the Poisson equation 2Φ=4πGa2ρ\nabla^{2}\Phi=4\pi Ga^{2}\rho, one can get the Newtonian potential:

Φ=4πρ0r0γ(γ3)(γ2)r2γ.\Phi=\frac{4\pi\rho_{0}r_{0}^{\gamma}}{(\gamma-3)(\gamma-2)}\,r^{2-\gamma}. (8)

This will lead to the lensing potential in GR (Suyu, 2012; Jyoti et al., 2019; Abadi & Kovetz, 2021)

ψGR(θ)=θE,GRγ13γθ3γ,\psi_{\rm GR}(\theta)=\frac{\theta_{E,\rm GR}^{\gamma-1}}{3-\gamma}~{}\theta^{3-\gamma}, (9)

where the r0r_{0} and ρ0\rho_{0} parameters have been subsumed into θE,GR\theta_{E,\rm GR}, and θE,GR\theta_{E,\rm GR} in the Einstein radius of the lens inferred within GR. The deflection angle becomes

αGR(θ)=θψGR(θ)=θE,GRγ1θ2γ.\alpha_{\rm GR}(\theta)=\partial_{\theta}\psi_{\rm GR}(\theta)=\theta_{E,\rm GR}^{\gamma-1}\theta^{2-\gamma}. (10)

One can obtain the PPN correction to the lensing potential Ψ\Psi by integrating the potential in Eq. (6) (Jyoti et al., 2019):

Δψ(θ)\displaystyle\Delta\psi(\theta) =\displaystyle= cθE,GRγ13γ(DLΛ)γ3\displaystyle\frac{c^{\prime}\,\theta_{E,\rm GR}^{\gamma-1}\,}{3-\gamma}\left(\frac{D_{L}}{\Lambda}\right)^{\gamma-3}
×F12[12,γ32,γ12;(DLθΛ)2],\displaystyle~{}\times~{}{}_{2}F_{1}\left[\frac{1}{2},\frac{\gamma-3}{2},\frac{\gamma-1}{2};\left(\frac{D_{L}\theta}{\Lambda}\right)^{2}\right],

where F12{}_{2}F_{1} is the hypergeometric function,

c=12πΓ(γ21)Γ(γ12),c^{\prime}=\frac{1}{2\sqrt{\pi}}~{}\frac{\Gamma\left(\frac{\gamma}{2}-1\right)}{\Gamma\left(\frac{\gamma-1}{2}\right)}, (12)

and Γ\Gamma is the Euler’s Gamma function. Then, the correction to the deflection angle from Eq. (11) can be expressed as Δα=θΔψ\Delta\alpha=\partial_{\theta}\Delta\psi. It should be pointed out that the gravitational slip correction to the lensing potential Δψ\Delta\psi influences the lens parameters, such as time delay, image positions, observational Einstein radius ΘE,obs\Theta_{E,\rm obs}, and so on, which are deduced from the lensing observables (Jyoti et al., 2019). For each observed lensing event, one should carry out the Markov chain Monte Carlo (MCMC) method to analyze the entire set of lens parameters under the effect of γPN\gamma_{PN} at a screening radius Λ\Lambda, which is a costly process for a sample of SGL systems. Therefore, we follow the same procedure as adopted in (Jyoti et al., 2019), relating the unobservable GR Einstein angle θE,GR\theta_{E,\rm GR} and the observed θE,obs\theta_{E,\rm obs}, which would took into account the gravitational slip, through the lens equation (Narayan & Bartelmann, 1996)

β(θ)=θαGR(θ)(γPN1)Δα(θ),\beta(\theta)=\theta-\alpha_{\rm GR}(\theta)-(\gamma_{PN}-1)\Delta\alpha(\theta), (13)

where β\beta, θ\theta, as well as α\alpha are the source position, the angular position of lens images, and the deflection angle respectively. Setting β=0\beta=0 in the equation above, we obtain

θE,obs=αGR(θE,obs)+(γPN1)Δα(θE,obs).\theta_{E,\rm obs}=\alpha_{\rm GR}(\theta_{E,\rm obs})+(\gamma_{PN}-1)\Delta\alpha(\theta_{E,\rm obs}). (14)

According to Δα=θΔψ\Delta\alpha=\partial_{\theta}\Delta\psi, the exact solution for θE,GR\theta_{E,\rm GR} is found to be

θE,GR={θE,obs1γc2(γ1)(γPN1)(DLΛ)γ1×F12[32,γ12;γ+12;(DLθE,obsΛ)2]}11γ,\begin{split}\theta_{E,\rm GR}=&\left\{\theta_{E,\rm obs}^{1-{\gamma}}-\frac{c^{\prime}}{2(\gamma-1)}({\gamma_{\rm PN}}-1)\left(\frac{D_{L}}{\Lambda}\right)^{{\gamma}-1}\right.\\ &\left.\times{}_{2}F_{1}\left[\frac{3}{2},\frac{\gamma-1}{2};\frac{\gamma+1}{2};\left(\frac{D_{L}\theta_{E,\rm obs}}{\Lambda}\right)^{2}\right]\right\}^{\frac{1}{1-\gamma}},\end{split} (15)

where cc^{\prime} is given by Eq. (12), and γPN=1\gamma_{\rm PN}=1 will lead to θE,GR=θE,obs\theta_{E,\rm GR}=\theta_{E,\rm obs}. After the slip-term is introduced into the GR Einstein angle θE,GR\theta_{E,\rm GR}, we want to express the averaged observed velocity dispersion under the gravitational slip. According to the theory of gravitational lensing (Schneider et al., 1992; Schwab et al., 2010), the angular size of the Einstein radius corresponding to a point mass MEM_{E} (or the spherically symmetric mass distribution within the Einstein radius) is expressed as

θE,GR=(4GM(θE,GR)c2DLSDSDL)1/2,\theta_{E,\rm GR}=\left(\frac{4GM(\theta_{E,\rm GR})}{c^{2}}\frac{D_{LS}}{D_{S}D_{L}}\right)^{1/2}~{}~{}~{}, (16)

where M(θE,GR)M(\theta_{E,\rm GR}) is the mass enclosed within a radius of θE,GR\theta_{E,\rm GR} and should not be affected by the modification of GR. We can acquire a useful expression if we rearrange terms in Eq. (16) with RE=DLθE,GRR_{E}=D_{L}\theta_{E,\rm GR} (Schneider et al., 1992; Abadi & Kovetz, 2021):

GM(θE,GR)RE=c24DSDLSθE,GR.\frac{GM(\theta_{E,\rm GR})}{R_{E}}=\frac{c^{2}}{4}\frac{D_{S}}{D_{LS}}\theta_{E,\rm GR}~{}~{}~{}. (17)

One of the simplest models to describe the mass density of an elliptical galaxy is a scale-free model based on power-law density profiles for the total mass density, ρ\rho, and luminosity density, ν\nu, (Koopmans, 2006a):

ρ(r)\displaystyle\rho(r) =\displaystyle= ρ0(rr0)γ\displaystyle\rho_{0}\left(\frac{r}{r_{0}}\right)^{-\gamma} (18)
ν(r)\displaystyle\nu(r) =\displaystyle= ν0(rr0)δ\displaystyle\nu_{0}\left(\frac{r}{r_{0}}\right)^{-\delta} (19)

Here rr is the spherical radial coordinate from the lens center: r2=R2+𝒵2r^{2}=R^{2}+\mathcal{Z}^{2}. One can use the anisotropy parameter β\beta to characterize anisotropic distribution of three-dimensional velocity dispersion pattern, which can be written as:

β(r)=1σt2/σr2,\beta(r)=1-{\sigma^{2}_{t}}/{\sigma^{2}_{r}}, (20)

where σt2\sigma^{2}_{t} and σr2\sigma^{2}_{r} represent the tangential and radial components of the velocity dispersion. In our analysis, β\beta is assumed to be independent of rr, and two cases will be considered: isotropic dispersion β=0\beta=0 and anisotropic dispersion β=const.0\beta=const.\neq 0. Applying the spherical Jeans equation (Binney, 1980), the radial dispersion of luminous matter σr2(r)\sigma_{r}^{2}(r) of the early-type galaxies can be written as

σr2(r)=Gr𝑑rν(r)M(r)(r)2β2r2βν(r),\sigma^{2}_{r}(r)=\frac{G\int_{r}^{\infty}dr^{\prime}\ \nu(r^{\prime})M(r^{\prime})(r^{\prime})^{2\beta-2}}{r^{2\beta}\nu(r)}~{}~{}~{}, (21)

where β\beta is the anisotropy parameter. Applying the mass density profile in Eq. (18), one can obtain the mass contained within a spherical radius rr and the total mass MEM_{E}:

M(r)=2πλ(γ)(rRE)3γME,M(r)=\frac{2}{\sqrt{\pi}\lambda(\gamma)}\left(\frac{r}{R_{E}}\right)^{3-\gamma}M_{E}~{}~{}~{}, (22)

where λ(x)=Γ(x12)/Γ(x2)\lambda(x)=\Gamma\left(\frac{x-1}{2}\right)/\Gamma\left(\frac{x}{2}\right) denotes the ratio of Euler’s gamma functions. Following the same formulae with the notation used in (Koopmans, 2006a) : ξ=δ+γ2\xi=\delta+\gamma-2, we acquire the radial velocity dispersion by scaling the dynamical mass to the Einstein radius:

σr2(r)=[GMERE]2π(ξ2β)λ(γ)(rRE)2γ.\sigma^{2}_{r}(r)=\left[\frac{GM_{E}}{R_{E}}\right]\frac{2}{\sqrt{\pi}\left(\xi-2\beta\right)\lambda(\gamma)}\left(\frac{r}{R_{E}}\right)^{2-\gamma}. (23)

Additionally, (Schwab et al., 2010; Cao et al., 2016, 2017) have pointed out that the actually observed velocity dispersion is measured over the effective spectroscopic aperture θap\theta_{ap} and effectively averaged over the line-of-sight effects. Given the entanglement of the aperture with atmospheric blurring and luminosity-weighted averaging, the averaged observed velocity dispersion takes the form:

σ¯2\displaystyle\bar{\sigma}_{*}^{2} =\displaystyle= [c24DsDlsθE,GR]2π(2σ~atm2/θE,GR2)1γ/2(ξ2β)\displaystyle\left[\frac{c^{2}}{4}\frac{D_{s}}{D_{ls}}\theta_{E,\rm GR}\right]\frac{2}{\sqrt{\pi}}\frac{(2\tilde{\sigma}_{\rm atm}^{2}/\theta_{E,\rm GR}^{2})^{1-\gamma/2}}{(\xi-2\beta)} (24)
×[λ(ξ)βλ(ξ+2)λ(γ)λ(δ)]Γ(3ξ2)Γ(3δ2).\displaystyle\times\left[\frac{\lambda(\xi)-\beta\lambda(\xi+2)}{\lambda(\gamma)\lambda(\delta)}\right]\frac{\Gamma(\frac{3-\xi}{2})}{\Gamma(\frac{3-\delta}{2})}~{}~{}~{}.

where σ~atmσatm1+χ2/4+χ4/40\tilde{\sigma}_{\rm atm}\approx\sigma_{\rm atm}\sqrt{1+\chi^{2}/4+\chi^{4}/40} and χ=θap/σatm\chi=\theta_{\rm ap}/\sigma_{\rm atm} (Schwab et al., 2010), the detailed expression of θE,GR\theta_{E,\rm GR} is given by Eq. (15), σatm\sigma_{\rm atm} is the seeing recorded by the spectroscopic guide cameras during observing sessions, and specific values of seeing for different surveys have been summarized in (Cao et al., 2016). According to Eq. (24), one can probe the PPN parameter γPN\gamma_{PN} and the screening radius Λ\Lambda on a sample of lenses with available information about measured redshifts of the lens and the source, velocity dispersion, as well as the Einstein radius θE,obs\theta_{E,obs}. In this work, a flat ΛCDM\Lambda CDM model is assumed as a fiducial cosmology with H0=67.36kms1Mpc1H_{0}=67.36\mathrm{~{}km~{}s^{-1}~{}Mpc^{-1}} and Ωm=0.315\Omega_{m}=0.315 (Planck Collaboration et al., 2018). In light of some evidence (Koopmans et al., 2006b; Ruff et al., 2011; Bolton et al., 2012; Sonnenfeld et al., 2013; Cao et al., 2016) supporting that the total density profile γ\gamma for massive galaxies shows a trend of the cosmic evolution, we consider the lens models where the total mass density evolves as a function of redshift for two cases: γ=δ\gamma=\delta and γδ\gamma\neq\delta. Meanwhile, we can examine the possible influence of different lens models on the constraints of γPN\gamma_{PN} and Λ\Lambda. As for the anisotropy parameter, β=0\beta=0 is assumed in the case γ=δ\gamma=\delta, and β\beta is being marginalized using a Gaussian prior with β=0.18±0.13\beta=0.18\pm 0.13 for the case γδ\gamma\neq\delta (Gerhard et al., 2001; Schwab et al., 2010; Cao et al., 2016).

Refer to caption
Figure 1.— The scatter plot of the current strong lensing sample of 99 intermediate mass early type galaxy. A fair coverage of redshifts can be noted in this combined sample. Velocity dispersion σap\sigma_{ap} is expressed in (km/skm/s).

3. Method and data

In this section, we present the details of observational and simulated SGL systems which are used to put constraints on the post-Newtonian slip parameter γPN\gamma_{PN} and the screening radius Λ\Lambda.

3.1. The observational and simulated strong gravitational lensing systems

Strong gravitational lensing by galaxies has been a powerful tool to probe both astrophysics and cosmology. Moreover, compared with late-type and unknown-type counterparts, early-type galaxies are more probable candidates for intervening lenses for the background sources, because most of the cosmic stellar mass of the Universe is included in such galaxies. Cao et al. (2015) compiled a sample comprising 118 early-type gravitational lenses observed in Sloan Lens ACS Survey (SLACS), the BOSS emission-line lens survey (BELLS), Lenses Structure and Dynamics (LSD), and the Strong Lensing Legacy Survey (SL2S) surveys, which have been used to probe cosmological models, test the fundamental assumptions in cosmology and study the mass density dispersion in early-type galaxies. Applying the 118 SGL sample, Cao et al. (2016) showed that the intermediate-mass lenses (200km/s<σap<300km/s200\,km/s<\sigma_{ap}<300\,km/s) are suitable to minimize the possible discrepancy between Einstein mass and dynamical mass for the SIS model. Then Cao et al. (2017) followed this analysis to test the parametrized post-Newtonian gravity with 80 intermediate mass early-type lenses. In this paper, taking into account the sample compiled and summarized in Cao et al. (2015) and Shu et al. (2017), including 158 early-type SGL systems, we also tend to test GR with a mass-selected sample of SGL systems by restricting the velocity dispersion of lensing galaxies to the intermediate range: 200km/s<σap<300km/s200\,km/s<\sigma_{ap}<300\,km/s, where 61 lenses are taken from SLACS, 22 lenses from SL2S, 13 lenses from BELLS, and 3 lenses from LSD. Fig. 1 presents the scatter plot for this mass-selected sample.

Strong lensing systems offer a unique opportunity to conduct cosmological research, however, they still suffer from the limited sample size and may not achieve precise enough results on basic cosmological parameters compared with other popular cosmological probes, such as type Ia supernovae (SN Ia), the cosmic microwave background (CMB), and baryon acoustic oscillations (BAO). In the future, the lens sample size is reckoned to increase by orders of magnitude through the next generation of wide and deep sky surveys. Recent studies forecasted the number of galactic-scale lenses that could be discovered in spectroscopic (Serjeant, 2014) and photometric surveys (Collett, 2015), such as the LSST (Abell et al., 2009) and the Dark Energy Survey (DES) (Frieman et al., 2004), which are the future wide and deep surveys, broadly expected to revolutionize the strong lensing science by increasing the number of known galactic lenses. For instance, the forthcoming LSST survey is expected to discover 105\sim 10^{5} SGL systems (Collett, 2015) in the near future, and there have been studies to illustrate the performance of the forecasted yield of the LSST in cosmological studies (Cao et al., 2017, 2018; Qi et al., 2019; Ma et al., 2019; Cao et al., 2020; Liu et al., 2020). It would be promising to extend the current research on the gravitational slip under screening effects to a new regime, that is the ability to detect 5000\sim 5000 galaxy-scale lens population in the future LSST survey.

Taking advantage of the publicly available simulation programs 111https://github.com/tcollett/LensPop elaborately described in Collett (2015), we have simulated a realistic population of SGL systems with early-type galaxies serving as lenses to anticipate the yields of LSST. Following the appropriate assumptions in simulating procedures (Liu et al., 2019), spherically symmetric power-law distribution is adopted to model the mass distribution of lensing galaxies, meanwhile, the normalization and shape of the velocity dispersion function of early-type galaxies are not changing with redshift. These assumptions are in good agreement with the previous studies on lensing statistics (Chae, 2003).

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Figure 2.— The scatter plot of the simulated LSST forthcoming samples with 5000 SGL systems. Einstein radius is given in arcsecarcsec.
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Figure 3.— Left panel: Fractional uncertainty of the velocity dispersion (Δσv/σv\Delta\sigma_{v}/\sigma_{v}) as a function of the lens surface brightness (BB) for the SLACS sample, with the best-fitted correlation function denoted as the red solid line. Right panel: The distribution of the velocity dispersion uncertainty for the simulated sample with 5000 SGL systems.

Motivated by Koopmans et al. (2006b); Humphrey & Buote (2010) that indicates early-type galaxies are well characterized by power-law mass distributions in regions covered by the X-ray and lensing observations, we modeled the lensing galaxies with a power-law mass distribution (ρrγ\rho\sim r^{-\gamma}), which leads to

θE=4πσap2c2DlsDs(θEθap)2γf(γ),\theta_{E}=4\pi\frac{\sigma_{ap}^{2}}{c^{2}}\frac{D_{ls}}{D_{s}}\left(\frac{\theta_{E}}{\theta_{ap}}\right)^{2-\gamma}f(\gamma), (25)

where

f(γ)\displaystyle f(\gamma) =\displaystyle= 1π(52γ)(1γ)3γΓ(γ1)Γ(γ3/2)\displaystyle-\frac{1}{\sqrt{\pi}}\frac{(5-2\gamma)(1-\gamma)}{3-\gamma}\frac{\Gamma(\gamma-1)}{\Gamma(\gamma-3/2)} (26)
×\displaystyle\times [Γ(γ/21/2)Γ(γ/2)]2.\displaystyle\left[\frac{\Gamma(\gamma/2-1/2)}{\Gamma(\gamma/2)}\right]^{2}.

Moreover, the prior of the average logarithmic density slope for the total mass density is modeled as γ=2.01±1.24\gamma=2.01\pm 1.24, which is derived from the analysis of massive field early-type galaxies selected from SLACS survey (Koopmans, 2006a; Koopmans et al., 2006b). Now, it is important to emphasize some key considerations in our simulation. In order to test GR with the combination of strong lensing and stellar dynamics, additional information including spectroscopic redshift of lenses and sources (zlz_{l} and zsz_{s}), the Einstein radius (θE,obs\theta_{E,\rm obs}), and spectroscopic velocity dispersion (σap\sigma_{ap}) are demanded. In view of the substantial cost of the dedicated follow-up observations and subsequent endeavors for a sample containing 10510^{5} SGL systems, concentrating on a carefully selected subset of LSST lenses is more realistic and proper, as has been proposed in recent discussion of multi-object and single-object spectroscopic follow-up to enhance Dark Energy Science from LSST (Chae, 2003). Therefore, referring to the selection criteria presented in (Liu et al., 2019, 2020), the final simulated sample is limited to 5000 elliptical galaxies with velocity dispersion of 200kms1<σap<300kms1200~{}km~{}s^{-1}<\sigma_{ap}<300~{}km~{}s^{-1}. Following the investigation of intermediate-mass lenses to relieve the possible disagreement between gravitational mass and dynamical mass for the SIS model (Cao et al., 2016), systems with the Einstein radius θE,obs>0.5arcsec\theta_{E,\rm obs}>0.5arcsec, and the i-band magnitude mi<22m_{i}<22 might be difficult to observe precisely. Fig. 2 presents the scatter plot of the simulated lensing systems, where a fair coverage of lens and source redshifts can be noticed. In our analysis, we also considered three sub-samples defined by different redshift ranges: z0.3z\leq 0.3, 0.3<z<0.650.3<z<0.65, z0.65z\geq 0.65, to expound any noticeable differences in the constraints displayed by lenses from different redshift bins. It should be pointed out that such an approach guarantees that enough data points are contained in each sub-sample and does not involve any physical aspects of the galaxy distribution in redshift.

As for the uncertainty investigation, we adopted the detailed procedure presented in (Liu et al., 2020). With regard to the observed Einstein radius, 32 SGL systems detected by SL2S show a possible correlation between the fractional uncertainty of the Einstein radius and θE,obs\theta_{E,\rm obs}, which means that the lenses with smaller Einstein radii would have larger uncertainty. In our analysis, the fractional uncertainty of θE,obs\theta_{E,\rm obs} is taken at the level of 8 percent, 5 percent, and 3 percent, respectively for small Einstein radii lenses (0.5arcsec<θE,obs<1.0arcsec0.5arcsec<\theta_{E,\rm obs}<1.0arcsec), intermediate Einstein radii lenses (1.0arcsec<θE,obs<1.5arcsec1.0arcsec<\theta_{E,\rm obs}<1.5arcsec), and large Einstein radii lenses (θE,obs>1.5arcsec\theta_{E,\rm obs}>1.5arcsec). It is worth noting that the fractional uncertainty of the Einstein radius may reach a level of 3 percent when all of our simulated LSST lenses will be observed with HST-like image quality (Hilbert et al., 2009). For the uncertainty of velocity dispersion, 70 SGL systems from SLACS survey (Bolton et al., 2008) have been utilized to quantify the correlation between the lens surface brightness in the i band and fractional uncertainty of the velocity dispersion Δσv/σv\Delta\sigma_{v}/\sigma_{v}. This was an appropriate sample to represent the observations that the future LSST survey might yield. From Fig. 3, one can see clearly strong evidence of anticorrelation between these two quantities. Then, we take advantage of the best-fitted correlation function derived from the 70 SGL systems to estimate the uncertainty of velocity dispersion for the discoverable lenses in future LSST survey, whose distribution is showed in Fig. 3.

3.2. Distance from type Ia Supernova observations

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Figure 4.— Left panel: The scatter plot of the 1048 SNe Ia pantheon sample. Observed apparent BB-band magnitude is plotted against the redshift. Right panel: The 1D probability distributions and 2D contours with 1σ\sigma and 2σ\sigma confidence levels for the parameters a1a_{1}, a2a_{2}, and M, obtained from 1048 SNe Ia pantheon sample.

In this paper, we assumed a flat concordance ΛCDM\Lambda CDM model to obtain the angular diameter distances DLD_{L}, DSD_{S}, and DLSD_{LS} as customary (Schwab et al., 2010; Cao et al., 2017). This was necessary to put constraints on the PPN parameter γPN\gamma_{PN}. Theoretically, however, ΛCDM\Lambda CDM is derived within the framework of GR. Therefore, it is important to come up with a model-independent approach, such as the distance reconstruction by standard candles, to derive these angular diameter distances and examine if the constraints would differ from that achieved by the fiducial cosmological model. Meanwhile, considering most current cosmological probes have a limited redshift coverage z<2.5z<2.5, it is necessary to contemplate the possible influence on constraints due to the absence of high redshift samples. Thereupon, besides deriving the cosmological distance for the simulated SGL systems by ΛCDM\Lambda CDM model, we will also adopt a model-independent method to achieve the angular diameter distances, through SN Ia Pantheon data set released by the Pan-STARRS1 (PS1) Medium Deep Survey (Scolnic et al., 2018), which allows us to perform an empirical fit to the luminosity distance measurements. Next, we will introduce the details of reconstruction of the luminosity distance DL(z)D_{L}(z) from SN Ia.

Containing 1048 SNe Ia measurements over a redshift range 0.01<z<2.30.01<z<2.3, the Pantheon catalogue has extended the Hubble diagram to z=2.26z=2.26 compared with high-z data from the SCP survey (Suzuki et al., 2012), the GOODS (Riess et al., 2007) as well as CANDELS/CLASH surveys (Rodney et al., 2014). This enables us to calibrate the SGL systems with zS<2.3z_{S}<2.3 in the forecasted LSST lenses by SN Ia. Thus, we generated 2619 simulated SGL systems suitable for this procedure. The observed distance modulus of each SN Ia is given by

μ=mBMB+αX1βC+ΔM+ΔB,\mu=m_{B}-M_{B}+\alpha\cdot X_{1}-\beta^{\ast}\cdot C+\Delta M+\Delta B, (27)

where mBm_{B} denotes the apparent BB-band magnitude, MBM_{B} is the absolute BB-band magnitude, C represents the color parameter describing the relation between luminosity and color, X1X_{1} is the light-curve shape parameter quantifying the relation between luminosity and stretch, as well as distance correlations based on the host-galaxy mass (ΔM\Delta M) and predicted biases from simulations (ΔB\Delta B) are also considered. According to a new method known as BEAMS with bias corrections (Kessler & Scolnic, 2017), the nuisance parameters in Eq. (27) could be calibrated to zero. Then the observed distance modulus is simply reduced to

μobs=mBMB.\mu_{obs}=m_{B}-M_{B}. (28)

The theoretical distance modulus can be expressed as

μth=5log10(DL/Mpc)+25.\mu_{th}=5\log_{10}(D_{L}/\rm Mpc)+25. (29)

As can be seen from Fig. 4, we have presented the dependence of apparent BB-band magnitude on redshifts, drawn from 1048 SNe Ia observations. In order to achieve the angular diameter distance for SGL systems with zS<2.3z_{S}<2.3, we carry out an empirical fit to the luminosity distance measurements, modeled as a third-order logarithmic polynomial expression in log(1+z)\rm log(1+z) (Kessler & Scolnic, 2017),

DL(z)=ln(10)c/H0(x+a1x2+a2x3),D_{L}(z)=\ln(10)c/H_{0}(x+a_{1}x^{2}+a_{2}x^{3}), (30)

where x=log(1+z)x=\log(1+z), a1a_{1} and a2a_{2} are two constant parameters to be optimized and determined by apparent BB-band magnitude of SNe Ia data. The Markov Chain Monte Carlo (MCMC) method implemented in emcee package 222https://pypi.python.org/pypi/emcee (Foreman-Mackey et al., 2013) written in Python 3.7 was used to get the best-fit values and 1σ1\sigma uncertainties of the parameters a1a_{1} and a2a_{2}. From the Hubble diagram of SNe Ia, one can derive the parameters MBM_{B}, a1a_{1} and a2a_{2} by minimizing the χ2\chi^{2} objective function:

χSNe2=i=11048[miobsmith(MB,a1,a2)]2σSNe2,\chi_{SNe}^{2}=\sum_{i=1}^{1048}\frac{\left[m_{i}^{obs}-m_{i}^{th}(M_{B},a_{1},a_{2})\right]^{2}}{\sigma_{SNe}^{2}}, (31)

where σSNe\sigma_{SNe} is the error in SNe Ia observations propagated from the covariance matrix (Scolnic et al., 2018). The 1D probability distributions of each parameters and the 2D contours are presented in Fig. 4.

model data Λ(kpc)\Lambda\;(kpc) γPN\gamma_{PN} Galaxy structure parameters
γ=δ\gamma=\delta Current mass-selected SGL 99.5657.55+97.7099.56_{-57.55}^{+97.70} 0.3780.269+0.5220.378_{-0.269}^{+0.522} γ0=2.0440.038+0.045\gamma_{0}=2.044_{-0.038}^{+0.045}, γ1=0.0070.028+0.021\gamma_{1}=-0.007_{-0.028}^{+0.021}
Forecasted SGL (Full sample) 211.3095.28+63.44211.30_{-95.28}^{+63.44} 0.9980.007+0.0030.998_{-0.007}^{+0.003} γ0=2.0070.014+0.010\gamma_{0}=2.007_{-0.014}^{+0.010}, γ1=0.0140.028+0.033\gamma_{1}=0.014_{-0.028}^{+0.033}
Sub-sample (Logarithmic polynomial) 213.0890.48+61.59213.08_{-90.48}^{+61.59} 0.9810.020+0.0210.981_{-0.020}^{+0.021} γ0=2.0180.013+0.013\gamma_{0}=2.018_{-0.013}^{+0.013}, γ1=0.0140.025+0.038\gamma_{1}=0.014_{-0.025}^{+0.038}
Sub-sample (z0.3)(z\leq 0.3) 210.0490.90+62.49210.04_{-90.90}^{+62.49} 0.9980.025+0.0050.998_{-0.025}^{+0.005} γ0=2.0030.033+0.020\gamma_{0}=2.003_{-0.033}^{+0.020}, γ1=0.0430.087+0.104\gamma_{1}=0.043_{-0.087}^{+0.104}
Sub-sample (0.3<z<0.65)(0.3<z<0.65) 212.3190.53+62.10212.31_{-90.53}^{+62.10} 0.9990.013+0.0070.999_{-0.013}^{+0.007} γ0=2.0050.039+0.047\gamma_{0}=2.005_{-0.039}^{+0.047}, γ1=0.0240.078+0.103\gamma_{1}=0.024_{-0.078}^{+0.103}
Sub-sample (z0.65)(z\geq 0.65) 213.2189.65+62.26213.21_{-89.65}^{+62.26} 0.9990.097+0.0540.999_{-0.097}^{+0.054} γ0=2.0210.141+0.142\gamma_{0}=2.021_{-0.141}^{+0.142}, γ1=0.0430.153+0.195\gamma_{1}=0.043_{-0.153}^{+0.195}
γδ\gamma\neq\delta Current mass-selected SGL 142.92106.94+97.51142.92_{-106.94}^{+97.51} 0.9370.767+1.3840.937_{-0.767}^{+1.384} γ0=2.0080.068+0.069\gamma_{0}=2.008_{-0.068}^{+0.069}, γ1=0.0050.052+0.045\gamma_{1}=-0.005_{-0.052}^{+0.045}, δ=2.2200.165+0.168\delta=2.220_{-0.165}^{+0.168}
Forecasted SGL (Full sample) 213.6286.84+61.57213.62_{-86.84}^{+61.57} 0.9730.071+0.0270.973_{-0.071}^{+0.027} γ0=2.0080.019+0.012\gamma_{0}=2.008_{-0.019}^{+0.012}, γ1=0.0070.054+0.033\gamma_{1}=0.007_{-0.054}^{+0.033}, δ=2.1480.112+0.107\delta=2.148_{-0.112}^{+0.107}
Sub-sample (Logarithmic polynomial) 203.1888.42+68.17203.18_{-88.42}^{+68.17} 0.9350.131+0.0510.935_{-0.131}^{+0.051} γ0=2.0120.025+0.018\gamma_{0}=2.012_{-0.025}^{+0.018}, γ1=0.0140.091+0.038\gamma_{1}=-0.014_{-0.091}^{+0.038}, δ=2.1860.123+0.100\delta=2.186_{-0.123}^{+0.100}
Sub-sample (z0.3)(z\leq 0.3) 205.6992.56+66.79205.69_{-92.56}^{+66.79} 0.7990.396+0.1710.799_{-0.396}^{+0.171} γ0=2.0140.035+0.022\gamma_{0}=2.014_{-0.035}^{+0.022}, γ1=0.0520.203+0.091\gamma_{1}=0.052_{-0.203}^{+0.091}, δ=2.1560.120+0.118\delta=2.156_{-0.120}^{+0.118}
Sub-sample (0.3<z<0.65)(0.3<z<0.65) 177.2592.24+83.75177.25_{-92.24}^{+83.75} 0.5470.369+0.3460.547_{-0.369}^{+0.346} γ0=2.0450.057+0.032\gamma_{0}=2.045_{-0.057}^{+0.032}, γ1=0.0360.066+0.046\gamma_{1}=0.036_{-0.066}^{+0.046}, δ=2.1370.174+0.158\delta=2.137_{-0.174}^{+0.158}
Sub-sample (z0.65)(z\geq 0.65) 163.49100.93+93.63163.49_{-100.93}^{+93.63} 0.5460.375+0.3940.546_{-0.375}^{+0.394} γ0=2.0420.131+0.103\gamma_{0}=2.042_{-0.131}^{+0.103}, γ1=0.0100.124+0.109\gamma_{1}=-0.010_{-0.124}^{+0.109}, δ=2.1590.253+0.159\delta=2.159_{-0.253}^{+0.159}
γδ\gamma\neq\delta Forecasted SGL (Full sample)1 212.6392.68+61.70212.63_{-92.68}^{+61.70} 0.8620.139+0.1150.862_{-0.139}^{+0.115} γ0=2.0240.074+0.082\gamma_{0}=2.024_{-0.074}^{+0.082}, γ1=0.0050.018+0.025\gamma_{1}=-0.005_{-0.018}^{+0.025}, δ=1.9380.149+0.133\delta=1.938_{-0.149}^{+0.133}
Sub-sample (Logarithmic polynomial)1 205.4988.36+67.64205.49_{-88.36}^{+67.64} 0.7910.205+0.1570.791_{-0.205}^{+0.157} γ0=2.0330.010+0.011\gamma_{0}=2.033_{-0.010}^{+0.011}, γ1=0.0070.020+0.036\gamma_{1}=0.007_{-0.020}^{+0.036}, δ=1.9090.171+0.150\delta=1.909_{-0.171}^{+0.150}
  • 1

    These two forecasted SGL samples are re-simulated when the gravitational slip with screening effects are considered in the fiducial model.

Table 1Summary of the best-fit values with their 1σ\sigma uncertainties concerning the screening radius Λ\Lambda, PPN parameter γPN\gamma_{PN}, and the galaxy structure parameters (γ0,γ1,δ\gamma_{0},\gamma_{1},\delta) in two different lens models. The results are obtained from the current 99 intermediate-mass lenses, 5000 forecasted LSST lenses, the sub-sample using the logarithmic polynomial cosmographic reconstruction through SNe Ia, as well as three sub-samples covering three different redshift ranges.

4. Results and discussion

In this paper, we used a mass-selected sample including 99 SGL systems, 5000 simulated SGL systems expected from future LSST survey and four sub-samples extracted from the forecasted samples with different selection criteria to probe the gravitational slip with screening effects and the parameters γ0,γ1,δ\gamma_{0},\gamma_{1},\delta characterizing the structure of elliptical galaxies. In order to determine these parameters, which are assumed to be the same for all lensing galaxies, we used MCMC method to sample their probability density distributions based on the likelihood exp(χ2/2){\cal L}\sim\exp{(-\chi^{2}/2)}, where

χ2=i=1N(σ¯,i(zl,i,zs,i,θE,i,θap,i,σatm;Λ,γPN,γ,δ)σap,iΔσ¯,i)2\chi^{2}=\sum_{i=1}^{N}\left(\frac{\bar{\sigma}_{*,i}(z_{l,i},z_{s,i},\theta_{E,i},\theta_{ap,i},\sigma_{atm};\Lambda,\gamma_{PN},\gamma,\delta)-{\sigma}_{ap,i}}{\Delta\bar{\sigma}_{*,i}}\right)^{2} (32)

was derived from the measured values of velocity dispersion σap\sigma_{ap}, and theoretical prediction Eq. (24) using the observed Einstein radius θE,obs\theta_{E,obs}, and aperture radius θap\theta_{ap}. For the term σatm\sigma_{atm}, which requires the information of seeing recorded by the spectroscopic guide cameras during observing sessions, we have used the seeing summarized in Cao et al. (2016) for the current intermediate-mass sample of SGL systems. For the simulated lensing systems from LSST, the median seeing in i-band, which is 0.75 arcsec reported in Collett (2015) was used. This value was also adopted to calculate the likely yield of observable gravitationally lensed quasars and supernovae based on the properties of LSST Oguri & Marshall (2010). The uncertainties of σap\sigma_{ap} and θE,obs\theta_{E,\rm obs} were propagated to the final uncertainty Δσ¯,i{\Delta\bar{\sigma}_{*,i}}. Following the SLACS team Bolton et al. (2008) and Cao et al. (2016), the fractional uncertainty of θE,obs\theta_{E,\rm obs} is taken as 5%, and the specific strategy for estimating the uncertainty of σap\sigma_{ap} and θE,obs\theta_{E,\rm obs} for the simulated LSST lenses can be found in section 3.1. As for the sub-sample of forecasted LSST lenses using the logarithmic polynomial cosmographic reconstruction through SNe Ia, the uncertainties of DL(z)D_{L}(z) reconstructed from SNe Ia are also propagated to the final uncertainty Δσ¯,i{\Delta\bar{\sigma}_{*,i}}. The numerical results for (Λ,γPN)(\Lambda,\gamma_{PN}) and the lens model parameters with 68.3% confidence level are summarized in Table 1, and the 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels are displayed in Figs. 5-9.

4.1. The case with γ=δ\gamma=\delta

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Figure 5.— The 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels for the screening radius Λ\Lambda, the PPN parameter γPN\gamma_{PN}, as well as the total-mass density parameters γ0\gamma_{0} and γ1\gamma_{1}, obtained from the current sample of 99 intermediate-mass strong gravitational lenses. The black dashed line represents the minimal screening radius at a typical Einstein radius value, GR, and SIS model (Λ=10kpc\Lambda=10\,kpc, γPN=1\gamma_{PN}=1, γ0=2\gamma_{0}=2, and γ1=0\gamma_{1}=0).
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Figure 6.— The 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels for the screening radius Λ\Lambda, the PPN parameter γPN\gamma_{PN}, as well as the total-mass density parameters γ0\gamma_{0} and γ1\gamma_{1}. The black dashed line represents the minimal screening radius at a typical Einstein radius value (Λ=10kpc\Lambda=10\,kpc), GR, and the γ\gamma prior used in the LSST simulation (γPN=1\gamma_{PN}=1, γ0=2.01\gamma_{0}=2.01, and γ1=0\gamma_{1}=0). Left panel: The constraints from the full 5000 simulated samples and logarithmic polynomial cosmographic reconstruction. Right panel: The constraints from the sub-samples of SGL systems by three different redshift bins.

In the first case, the luminosity density profile ν\nu is assumed to follow the total-mass density profile ρ\rho, i.e., γ=δ\gamma=\delta. Then, this common density slope is allowed to evolve with redshift:

γ(z)=γ0+γ1z,\gamma(z)=\gamma_{0}+\gamma_{1}z, (33)

where γ0\gamma_{0} is the current value and γ1\gamma_{1} represents the evolution of γ\gamma with redshift. We assumed that the stellar velocity anisotropy vanishes β=0\beta=0 in order to facilitate comparison with previous results. As can be seen from Fig. 5 and Table 1, the current intermediate-mass lenses do not provide stringent constraints on the PPN parameter γPN\gamma_{PN}, which will be improved significantly with the simulated full sample of SGL systems. The best-fitted value of γPN\gamma_{PN} obtained from the current sample of 99 lenses is 0.3780.269+0.5220.378_{-0.269}^{+0.522} with 1σ1\sigma confidence level and is still consistent with γPN=1\gamma_{PN}=1 within 2σ2\sigma. Fig. 5 shows that there is no evident degeneracy between γPN\gamma_{PN} and the screening scale Λ\Lambda, but the deviation from GR is more obvious within 1σ1\sigma range with the increasing of Λ\Lambda. Furthermore, the best-fitted value of the screening radius is Λ=99.5657.55+97.70kpc\Lambda=99.56_{-57.55}^{+97.70}\;kpc. This suggests that there may exist some characteristic scale for the current mass-selected sample, beyond which the modification of GR is possible. It is worth noting that a recent study (Liu et al., 2022) got the constraint γPN\gamma_{PN} (1.4550.127+0.1541.455_{-0.127}^{+0.154}) with the same parametrized models of γ\gamma, where the GR is excluded above 2σ2\sigma range. The constraints on the parameters γ0\gamma_{0} and γ1\gamma_{1} are tight: γ0=2.0440.038+0.045\gamma_{0}=2.044_{-0.038}^{+0.045}, γ1=0.0070.028+0.021\gamma_{1}=-0.007_{-0.028}^{+0.021}, which is consistent with the singular isothermal sphere model (γ0=2,γ1=0\gamma_{0}=2,\gamma_{1}=0) within 1σ1\sigma range and is also in agreement with similar results, obtained by the others from a mass-selected sample of 80 intermediate-mass lenses (γ0=2.115±0.072\gamma_{0}=2.115\pm 0.072, γ1=0.091±0.154\gamma_{1}=-0.091\pm 0.154; Cao et al. (2016)), Union2.1+Gamma ray burst(GRB)+SGL (γ0=2.040.06+0.08\gamma_{0}=2.04_{-0.06}^{+0.08}, γ1=0.0850.18+0.21\gamma_{1}=-0.085_{-0.18}^{+0.21}; Holanda et al. (2017)) and BAO+SGL (γ0=2.0940.056+0.053\gamma_{0}=2.094_{-0.056}^{+0.053}, γ1=0.0530.102+0.103\gamma_{1}=-0.053_{-0.102}^{+0.103}; Li et al. (2016)). In addition, our results indicate that the total density profile of the current early-type galaxies with intermediate velocity dispersions (200kms1<σap<300kms1200~{}km~{}s^{-1}<\sigma_{ap}<300~{}km~{}s^{-1}) have showed no significant evolution over the cosmic time (at least up to z1z\sim 1).

On the other hand, as can be seen from Fig. 6 and Table 1, the simulated full sample provides more stringent constraints on γPN\gamma_{PN} (0.9980.007+0.0030.998_{-0.007}^{+0.003}) with 0.5% precision, which is in perfect agreement with γPN=1\gamma_{PN}=1 assumed in simulations. One can see from Figs. 6-7 that much more stringent constraints on γPN\gamma_{PN} would be achieved using the strong lensing systems detectable in the future surveys. For comparison, our results are similar to the results γPN=1.0000.0023+0.0025\gamma_{PN}=1.000_{-0.0023}^{+0.0025} obtained in Cao et al. (2017) with 53000 simulated SGL systems meeting the redshift criteria 0<zl<zs1.4140<z_{l}<z_{s}\leq 1.414, where the galaxy structure parameters have been fixed: γ=δ=2,β=0\gamma=\delta=2,\beta=0. The constraint from the sub-sample combined with SNe Ia calibrated as standard candles is γPN=0.9810.020+0.021\gamma_{PN}=0.981_{-0.020}^{+0.021} with 2% precision. However, it is noticeable that the central value of γPN\gamma_{PN} deviates a bit more from γPN=1\gamma_{PN}=1, in comparison to the constraint obtained with the full sample of simulated SGL systems, which may signal some systematics present. In Table 1 and Fig. 6, we also display the results obtained on three sub-samples with different lens redshift bins. One may see that the values are in full agreement with each other and GR (γPN=1\gamma_{PN}=1) is still included within 1σ1\sigma range, which is consistent with the assumption that GR is valid when simulating the LSST SGL systems. Interestingly, the degeneracy between the PPN parameter and the screening scale Λ\Lambda showed in Fig. 6 is quite similar to the results from Jyoti et al. (2019); Abadi & Kovetz (2021), where the authors applied time delay in SGL to acquire the constraint on γPN\gamma_{PN} as a function of the physical cutoff scale Λ\Lambda rather than carrying out full MCMC analysis for the parameters (γPN,Λ\gamma_{PN},\,\Lambda, and γ\gamma). Furthermore, the minimal screening radius corresponding to 68% C.L. reaches 42 kpc and 120 kpc for the current and forecasted SGL samples respectively, which is notably larger than the minimal screening scale at a typical Einstein radius value, ΛRE10\Lambda\geq R_{E}\approx 10 kpc (Jyoti et al., 2019; Abadi & Kovetz, 2021). Considering that the screening length Λ\Lambda is bigger than the Einstein radius of the lensing galaxy, the advantage of the observed velocity dispersion of the intermediate-mass elliptical galaxies to probe gravitational slip under this screened MG is limited. It is still possible since gravitational lensing probes the projected mass i.e. a cylinder along the line of sight. Up to the scales of Λ300kpc\Lambda\sim 300\;kpc, we did not find any typical screening radius for the samples from simulated LSST lenses, beyond which MG is relevant. Besides, the constraints on the total density profile of early type galaxies indicate that the measurement precision of the current value is expected to be improved to 0.6% from the full simulated SGL sample, but the accuracy of γ1\gamma_{1} does not seem sensitive to the sample size. The SIS model is still included within 1σ1\sigma range for all samples derived from the simulated SGL systems, which is in good agreement with the prior γ=2.01±1.24\gamma=2.01\pm 1.24 used to generate the forecasted LSST SGL systems.

Refer to caption
Figure 7.— The 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels for the screening radius Λ\Lambda, the PPN parameter γPN\gamma_{PN}, the total-mass density parameters γ0\gamma_{0} and γ1\gamma_{1}, as well as the luminosity density parameter δ\delta, obtained from the current sample of 99 intermediate-mass strong gravitational lenses. The black dashed line indicates the minimal screening radius at a typical Einstein radius value, GR, and SIS model (Λ=10kpc\Lambda=10\,kpc, γPN=1\gamma_{PN}=1, γ0=2\gamma_{0}=2, and γ1=0\gamma_{1}=0).

4.2. The case with δγ\delta\neq\gamma

Refer to caption
Refer to caption
Figure 8.— The 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels for the screening radius Λ\Lambda, the PPN parameter γPN\gamma_{PN}, the total-mass density parameters γ0\gamma_{0} and γ1\gamma_{1}, as well as the luminosity density parameters δ\delta. The black dashed line represents the minimal screening radius at a typical Einstein radius value (Λ=10kpc\Lambda=10\,kpc), GR, and the γ\gamma prior used in the LSST simulation (γPN=1\gamma_{PN}=1, γ0=2.01\gamma_{0}=2.01, and γ1=0\gamma_{1}=0). Left panel: The constraints from the full 5000 simulated samples and logarithmic polynomial cosmographic reconstruction. Right panel: The constraints from the sub-samples of SGL systems by three different redshift bins.

In the second case, the luminosity density profile is allowed to be different from the total-mass density profile, i.e., δγ\delta\neq\gamma, and the stellar velocity anisotropy β\beta has been marginalized over a Gaussian distribution, β=0.18±0.13\beta=0.18\pm 0.13, which is also broadly applied in the literature (Gerhard et al., 2001; Bolton et al., 2006; Schwab et al., 2010; Cao et al., 2017). With the same assumption as above, the total density slope was allowed to evolve as a function of redshift, γ(z)=γ0+γ1z\gamma(z)=\gamma_{0}+\gamma_{1}z. All values of the estimated parameters in the screened MG model are displayed in Table 1 and illustrated in Figs. 7-9. For the current 99 intermediate-mass lenses, the constraint on γPN\gamma_{PN} is still very weak: γPN=0.9370.767+1.384\gamma_{PN}=0.937_{-0.767}^{+1.384}, but agrees with GR within 1σ1\sigma. In the case of δγ\delta\neq\gamma, it seems that there is no obvious degeneracy between γPN\gamma_{PN} and the screening radius Λ\Lambda, meanwhile, the constraint on Λ\Lambda displayed in Fig. 7 shows no characteristic scale of screening radius for the present SGL sample, compared with the result inferred in the case of δ=γ\delta=\gamma. Nevertheless, the degeneracy between γPN\gamma_{PN} and γ0\gamma_{0} (and to a smaller degree with γ1\gamma_{1}) is noticeable in Fig. 7, where a steeper present total mass density profile will contribute to a larger value for the PPN parameter. Performing fits on the current mass-selected sample, the 68% C.L. uncertainties on the three galaxy structure parameters are γ0=2.0080.068+0.069\gamma_{0}=2.008_{-0.068}^{+0.069}, γ1=0.0050.052+0.045\gamma_{1}=-0.005_{-0.052}^{+0.045}, δ=2.2200.165+0.168\delta=2.220_{-0.165}^{+0.168}. It is interesting to note that the constraints on the mass-density exponents are consistent with that derived in the case of δ=γ\delta=\gamma and the singular isothermal sphere is still favoured within 1σ1\sigma. This indicates that from the perspective of stellar dynamics, it is effectively similar to characterize the mass distribution in the lensing galaxies with intermediate velocity distributions (200kms1<σap<300kms1200~{}km~{}s^{-1}<\sigma_{ap}<300~{}km~{}s^{-1}) by both δ=γ\delta=\gamma and δγ\delta\neq\gamma models. Additionally, we also get a relatively smaller central value of the luminosity density profile, as compared to the results obtained from 53 SLACS lenses (δ=2.40±0.11\delta=2.40\pm 0.11; (Schwab et al., 2010)), as well as 80 intermediate mass lenses observed by SLACS, BELLS, LSD, and SL2S (δ=2.4851.3930.445\delta=2.485_{-1.393}^{0.445}; (Cao et al., 2017)). However, in view of no obvious cosmic evolution showed in the total mass density parameter γ\gamma (γ=γ0+γ1z2.008\gamma=\gamma_{0}+\gamma_{1}z\approx 2.008), a model where mass traces light (γ=δ\gamma=\delta) seems to be eliminated at ¿68% confidence level and our analysis partly suggests the presence of dark matter in the form of a mass component distributed differently from light.

The full sample of simulated SGL provides more stringent constraints on the PPN parameter γPN=0.9730.071+0.027\gamma_{PN}=0.973_{-0.071}^{+0.027}, lens models γ0=2.0080.019+0.012\gamma_{0}=2.008_{-0.019}^{+0.012}, γ1=0.0070.054+0.033\gamma_{1}=0.007_{-0.054}^{+0.033}, and δ=2.1480.112+0.107\delta=2.148_{-0.112}^{+0.107}, compared with the results generated from the current sample. As can be seen from Table 1, there is a good consistency between the current mass-selected SGL and the full sample of forecasted SGL. However, the parameter γPN\gamma_{PN} obtained from the cosmographic reconstruction, which is γPN=0.9350.131+0.051\gamma_{PN}=0.935_{-0.131}^{+0.051}, deviates a bit more from γPN=1\gamma_{PN}=1 within 1σ1\sigma in comparison to the constraint from the full simulated sample. It is worth noting that this slight deviation from GR is also in agreement with a similar result achieved in the case δ=γ\delta=\gamma (γPN=0.9810.020+0.021\gamma_{PN}=0.981_{-0.020}^{+0.021}). This illustrates the possibility that using the SNe Ia pantheon sample as a precise distance estimator, through the logarithmic polynomial cosmographic reconstruction, may provide a valuable supplement to the a priori assumed cosmology in probing gravitational slip over the redshift range 0<z<2.50<z<2.5. From the constraints acquired on three sub-samples showed in Fig. 8 and Table 1, the results on γPN\gamma_{PN} are different from that obtained in the case of δ=γ\delta=\gamma. Namely, one can see that in the samples of the simulated galaxies differing by redshift bin one has a different distribution of the PPN parameter: γPN=0.7990.396+0.171\gamma_{PN}=0.799_{-0.396}^{+0.171} for z0.3z\leq 0.3, γPN=0.5470.369+0.346\gamma_{PN}=0.547_{-0.369}^{+0.346} for 0.3<z<0.650.3<z<0.65, and γPN=0.5460.375+0.394\gamma_{PN}=0.546_{-0.375}^{+0.394} for z0.65z\geq 0.65, which display obvious deviation from γPN=1\gamma_{PN}=1 especially for the case of 0.3<z<0.650.3<z<0.65 and z0.65z\geq 0.65 but GR is still valid within 2σ2\sigma. It is interesting to note that such noticeable impact of two different lens models on γPN\gamma_{PN} is also present in the current SGL sample (γPN=0.3780.269+0.522\gamma_{PN}=0.378_{-0.269}^{+0.522} vs. γPN=0.9370.767+1.384\gamma_{PN}=0.937_{-0.767}^{+1.384}), which indicates that the constraints on the PPN parameter may be sensitive to the choice of the mass distribution of early-type galaxies. According to the constraints on screening radius presented in Fig. 8 and Table 1, we still do not find any characteristic cutoff scale for all the simulated SGL samples, and the minimal screening radius is lying in the range Λ[36,127]kpc\Lambda\in[36,127]\;kpc in the case of δγ\delta\neq\gamma, which is obviously larger than the minimal screening radius considered in the previous work i.e. ΛRE10\Lambda\geq R_{E}\approx 10 kpc; (Jyoti et al., 2019; Abadi & Kovetz, 2021). With respect to the mass density parameters γ0\gamma_{0}, γ1\gamma_{1}, and δ\delta, we can see clearly from Table 1 that the constraints from LSST lenses agree well with that from the current SGL sample within 68.3 percent. Furthermore, the future LSST lenses will improve the measuring precision of the present mass density parameter γ0\gamma_{0} to 0.7% and the luminosity density parameter δ\delta to 5% in comparison to the precision of γ0\gamma_{0} (3.4%) and δ\delta (7.5%) obtained from the current SGL sample.

There are several sources of systematics we do not consider in the above analysis. First of all, the validity of GR was assumed in the simulation of forecasted LSST sample. In order to further test the effectiveness of our method, we considered to re-simulate the LSST lenses with modified gravity effects present in the fiducial model. The correction due to the screening effect has been involved in the lensing potential (Eq. (4)), while the connection between the observed Einstein angle θE,obs\theta_{E,\rm obs} and the Einstein angle in GR θE,GR\theta_{E,\rm GR} has been expressed in Eq. (15). More specifically, in the simulation procedure, the numerical solution of θE,obs\theta_{E,\rm obs} can be solved through Eq. (15) and the expression of θE,GR\theta_{E,\rm GR} is modeled by Eq. (25)-(26). Furthermore, the priors of the screening scale and the deviation from GR are adopted in the fiducial model as Λ=100kpc\Lambda=100\;kpc and γPN=0.97±0.09\gamma_{PN}=0.97\pm 0.09, which is consistent with |γPN1|0.2×(Λ/100kpc)|\gamma_{\rm PN}-1|\leq 0.2\times(\Lambda/100\,\rm kpc) reported in (Collett et al., 2018; Jyoti et al., 2019). Based on the simulated 5000 strong lenses with modified gravity effect and extended power-law lens as fiducial models, we obtained the constraints on the parameters (Λ,γPN,γ0,γ1,δ)(\Lambda,\gamma_{PN},\gamma_{0},\gamma_{1},\delta) displayed in Table 1 and Fig. 9. Note that the PN parameter and lens parameters derived from the simulated strong lenses in our analysis, γPN=0.8620.139+0.115\gamma_{PN}=0.862_{-0.139}^{+0.115}, γ0=2.0240.074+0.082\gamma_{0}=2.024_{-0.074}^{+0.082}, γ1=0.0050.018+0.025\gamma_{1}=-0.005_{-0.018}^{+0.025}, and δ=1.9380.149+0.133\delta=1.938_{-0.149}^{+0.133} are in good agreement with the above priors on γPN\gamma_{PN} and lens mass density profile at 68.3% confidence level. The strong degeneracy between Λ\Lambda and γPN\gamma_{PN} could also be seen from Fig. 9. Secondly, in this study two power-law lens models have been adopted to connect the observed velocity dispersion to the gravitational slip under screening effects, which presents the direct test of GR within screening scales of Λ=10300kpc\Lambda=10-300\;kpc. As was noted in the previous works (Schwab et al., 2010; Chen et al., 2019; Cao et al., 2017; Liu et al., 2020; Wong et al., 2020; Liu et al., 2022), our analysis indicates that the lens mass modeling may have an apparent influence on the estimation of cosmological parameters such as the screening scale Λ\Lambda and PN parameter γPN\gamma_{PN}. Therefore, besides benefitting from the dramatically increasing number of SGL systems observed by future optical surveys, more appropriate modeling of the lens mass will also contribute to the understanding of lens parameters and reducing the uncertainty of derived cosmological parameters. For instance, although the effectiveness of power-law density profiles has been widely proved in describing the observed early-type galaxies within a few effective radii (Wang, 2018), the scatter of other galaxy density parameters could be an important source of systematic errors on the final results. An influential paper by Navarro et al. (1996, 1997) suggested that the Navarro-Frenk-White (NFW) profile can provide a good approximation to the the density profile of dark matter (DM) halos, which has found widespread astrophysical applications in the literature (Bullock et al., 2001; Komatsu et al., 2011; Koyama, 2016; Collett et al., 2018). Such spherically averaged density profile is well described by a double power-law relation, which resembles dark matter halo with r3r^{-3} in the outer regions and r1r^{-1} at small radii (Cardone et al., 2010). However, the joint strong lensing and dynamical analysis strongly support that the total mass profile is very close to isothermal (γ2\gamma\sim 2), although neither the stellar component nor dark matter halo is of a simple power law (Treu, 2010). Detailed solutions to the lensing and dynamical properties of lenses (such as the total mass, velocity dispersion and Einstein radius) and the gravitational slip under the NFW profile would be an independent work in our future studies.

5. Conclusions

Refer to caption
Figure 9.— The 1D probability distributions and 2D contours with 1σ1\sigma and 2σ2\sigma confidence levels for the screening radius Λ\Lambda, the PPN parameter γPN\gamma_{PN}, the total-mass density parameters γ0\gamma_{0} and γ1\gamma_{1}, as well as the luminosity density parameter δ\delta, obtained from the re-simulated 5000 LSST lenses with gravitational slip included in the fiducial model. The black dashed line indicates the Λ\Lambda, γPN\gamma_{PN}, and γ\gamma priors used in the LSST simulation (Λ=100kpc\Lambda=100\,kpc, γPN=0.97\gamma_{PN}=0.97, γ0=2.01\gamma_{0}=2.01, and γ1=0\gamma_{1}=0).

In this work, we have studied the gravitational slip under a phenomenological model of gravitational screening, where GR is maintained for small radii and the departures from GR take the form of a gravitational slip beyond the screening scale Λ\Lambda. Based on mass-selected galaxy-scale strong gravitational lenses from the SLACS, BELLS, LSD, as well as SL2S surveys and simulated future measurements of 5000 galaxy-scale SGL from the forthcoming Large Synoptic Survey Telescope (LSST) survey, we were able to evaluate this screened MG model with screening length Λ=10300kpc\Lambda=10-300\;kpc, which is broad enough to cover one typical massive galaxy. This is also the first attempt to use the stellar kinematics of SGL systems to assess constraints on the PPN parameter γPN\gamma_{PN}, screening radius Λ\Lambda within this screening range under two different lens models simultaneously. Here we summarize our main conclusions in more details.

Considering two different lens models where the total mass density and luminosity density of lensing galaxies are modeled as power-law density profiles ρ(r)=ρ0(r/r0)γ\rho(r)=\rho_{0}(r/r_{0})^{-\gamma} and ν(r)=ν0(r/r0)δ\nu(r)=\nu_{0}(r/r_{0})^{-\delta} respectively and γ\gamma is assumed to evolve with redshift, our results indicate that the current intermediate mass early-type galaxies are not able to provide tight constraints on the PPN parameter in this new theories of modified gravity, but GR (γPN=1\gamma_{PN}=1) is still valid with screening length Λ=10300kpc\Lambda=10-300\;kpc in both lens models. On the other hand, our work studies the complementary range ΛRE\Lambda\geq R_{E} compared with previous researches (Bolton et al., 2006; Smith, 2009; Schwab et al., 2010; Collett et al., 2018; Liu et al., 2022) where the screening is assumed to take place within the galaxy. Then, one interesting thing is to figure out if there exists any specific cutoff scale for galaxy-scale SGL systems beyond which MG is relevant. The constraint achieved in the case of γ=δ\gamma=\delta shows a significant value of Λ100\Lambda\sim 100 kpc, beyond which the departure from γPN\gamma_{PN} is noticeable within 1σ1\sigma confidence level. Nevertheless, in the case of γδ\gamma\neq\delta, the current sample shows no specific cutoff value in the range 10kpc<Λ<300kpc10\;kpc<\Lambda<300\;kpc. It supports the claim that the intermediate mass lenses may shed new light on testing the validity of GR under this screened MG model. In addition, the 68 % confidence level constraints on Λ\Lambda and γPN\gamma_{PN} are quite different depending on the lens model. Setting the luminosity profile of elliptical galaxies as a free parameter, we obtained larger best fit values of Λ=142.92106.94+97.51kpc\Lambda=142.92_{-106.94}^{+97.51}\;kpc and γPN=0.9370.767+1.384\gamma_{PN}=0.937_{-0.767}^{+1.384} compared with Λ=99.5657.55+97.70kpc\Lambda=99.56_{-57.55}^{+97.70}\;kpc and γPN=0.3780.269+0.522\gamma_{PN}=0.378_{-0.269}^{+0.522} in the case of γ=δ\gamma=\delta. Moreover, in this paper, we assessed the constraints for the total mass-profile and light-profile shapes of elliptical galaxies. Allowing for the cosmic evolution of the total mass density profile exponent in the form of γ=γ0+γ1z\gamma=\gamma_{0}+\gamma_{1}z, there is no obvious evidence suggesting that the total density profile of intermediate mass early-type galaxies has become steeper over cosmic time (up to z1z\sim 1), and the singular isothermal sphere model is well favored by the current mass-selected sample in both lens models.

Furthermore, we elaborated what kind of results one could acquire making use of the future measurements of a well-selected sample containing 5000 LSST lenses. The final results imply that much more severe constraints on the γPN\gamma_{PN} will be achieved with 10210310^{-2}\sim 10^{-3} precision in the regime of screening radii Λ=10300kpc\Lambda=10-300\;kpc. Benefiting from LSST’s wide field of view and sensitivity, the SGL systems detectable in the future can be very helpful for testing GR on kpc-Mpc scales in modified theories of gravity. Interestingly, the degeneracy between the screening scale Λ\Lambda and the PPN parameter γPN\gamma_{PN} derived from the full simulated SGL samples and four sub-samples is very similar to the results presented in Jyoti et al. (2019); Abadi & Kovetz (2021), where the authors do not carry out full MCMC analysis in parameter space. Meanwhile, we still do not find any particular cutoff scale for these simulated SGL samples, which is consistent with the assumption that GR is valid and no screening effects are involved in simulating the LSST SGL systems. With the increasing number of available galaxy-scale lenses, our results imply that it would be advantageous to use velocity dispersion measurements of the intermediate-mass elliptical galaxies to probe departures from GR under the screened MG model, where the gravitational slip is modeled as a step-wise discontinuous phenomenon with the screening radius Λ\Lambda. Other cosmological probes, such as strongly lensed fast radio bursts (FRBs), lensed gravitational-wave signals and so on would be beneficial complementary probes in the case ΛRE\Lambda\geq R_{E}. Besides, the SIS model (γ0=2,γ1=0\gamma_{0}=2,\gamma_{1}=0) is included within 1σ1\sigma for all simulated samples selected with different criteria, which is consistent with the prior γ=2.01±1.24\gamma=2.01\pm 1.24 used to model the total mass density of the forecasted LSST lenses.

In this work, we also considered four sub-samples derived from the well-defined sample of 5000 LSST lenses and applied the logarithmic polynomial cosmographic reconstruction of distances based on the SNe Ia pantheon sample. It should be noted that the slight deviation from γPN=1\gamma_{PN}=1 (γPN=0.9810.020+0.021\gamma_{PN}=0.981_{-0.020}^{+0.021} for γ=δ\gamma=\delta, and γPN=0.9350.131+0.051\gamma_{PN}=0.935_{-0.131}^{+0.051} for γδ\gamma\neq\delta) is a bit more noticeable than in the case of the full simulated SGL sample (γPN=0.9980.007+0.003\gamma_{PN}=0.998_{-0.007}^{+0.003} for γ=δ\gamma=\delta, and γPN=0.9730.071+0.027\gamma_{PN}=0.973_{-0.071}^{+0.027} for γδ\gamma\neq\delta) in both lens models. This indicates the possibility that over the redshift range 0<z<2.50<z<2.5, the SNe Ia pantheon sample serving as standard candles may provide a valuable supplement to the assumed fiducial cosmology in testing departure from GR. For the sub-samples defined by different redshift ranges, the constraints on γPN\gamma_{PN} became more diverse in the lens model where γδ\gamma\neq\delta is assumed. Significant departures from γPN=1\gamma_{PN}=1 are present, which are γPN=0.5470.369+0.346\gamma_{PN}=0.547_{-0.369}^{+0.346} and γPN=0.5460.375+0.394\gamma_{PN}=0.546_{-0.375}^{+0.394} corresponding to the sub-samples 0.3<z<0.650.3<z<0.65 and z0.65z\geq 0.65, respectively. However, in the case of γ=δ\gamma=\delta, the same sub-samples gave γPN=0.9990.013+0.007\gamma_{PN}=0.999_{-0.013}^{+0.007} and γPN=0.9990.097+0.054\gamma_{PN}=0.999_{-0.097}^{+0.054} respectively. Therefore, the lens mass model seems to have a great influence on the limits on the PPN parameter with screening length Λ=10300kpc\Lambda=10-300\;kpc, which also can be concluded from the current intermediate mass SGL systems. Additionally, we have re-simulated the LSST samples with modified gravity effect present in the fiducial model and our results demonstrate the effectiveness of our methodology. In this paper, we just adopted a power-law profile to characterize the distribution of the luminous component. On the other hand, there are other more complicated but also more realistic descriptions of the luminosity density profiles in the literature (Hernquist, 1990; Navarro et al., 1997). It should be emphasized that more appropriate and accurate modeling of the structure of lensing galaxies will contribute to the precision and accuracy of testing the validity of GR using the SGL systems, and future systematic surveys such as LSST (Abell et al., 2009), DES (Frieman et al., 2004), and Euclid survey (Pocino et al., 2021) will greatly conduce to such studies.

This work was supported by the National Natural Science Foundation of China under Grants Nos. 12021003, 11633001, and 11920101003; the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDB23000000; the Interdiscipline Research Funds of Beijing Normal University; the China Manned Space Project (Nos. CMS-CSST-2021-B01 and CMS-CSST-2021-A01); and the CAS Project for Young Scientists in Basic Research under Grant No. YSBR-006. M.B. was supported by Foreign Talent Introducing Project and Special Fund Support of Foreign Knowledge Introducing Project in China (No. G2021111001L). He is also grateful for support from Polish Ministry of Science and Higher Education through the grant DIR/WK/2018/12.

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