arXiv subjects

Sangjun Cha

Publications and source records attributed to Sangjun Cha.

21 records · Page 2Linked to original sources

arXiv2023

Weak-lensing mass bias in merging galaxy clusters

Although weak lensing (WL) is a powerful method to estimate a galaxy cluster mass without any dynamical assumptions, a model bias can arise when the cluster density profile departs from the assumed model profile. In a merging system, the bias is expected to become most severe because the constituent halos undergo significant structural changes. In this study, we investigate WL mass bias in binary cluster mergers using a suite of idealized hydrodynamical simulations. Realistic WL shear catalogs are generated by matching the source galaxy properties, such as intrinsic shape dispersion, measurement noise, source densities, etc., to those from Subaru and {\it Hubble Space Telescope} observations. We find that, with the typical mass-concentration ($M$-$c$) relation and the Navarro-Frenk-White (NFW) profile, the halo mass bias depends on the time since the first pericenter passage and increases with the mass of the companion cluster. The time evolution of the mass bias is similar to that of the concentration, indicating that, to first order, the mass bias is modulated by the concentration change. For a collision between two $\sim10^{15}~M_{\odot}$ clusters, the maximum bias amounts to $\sim60\%$. This suggests that previous WL studies may have significantly overestimated the mass of the clusters in some of the most massive mergers. Finally, we apply our results to three merger cases: Abell 2034, MACS J1752.0+4440, and ZwCl 1856.8+6616, and report their mass biases at the observed epoch, as well as their pre-merger masses, utilizing their merger shock locations as tracers of the merger phases.

astro-ph.CO↗

arXiv2022

MARS: A New Maximum Entropy-Regularized Strong Lensing Mass Reconstruction Method

Free-form strong-lensing (SL) mass reconstructions typically suffer from overfitting, which manifest itself as false-positive small-scale fluctuations. We present a new free-form MAximum-entropy ReconStruction (${\tt MARS}$) method without the assumption that light traces mass (LTM). The ${\tt MARS}$ algorithm enables us to achieve excellent convergence in source positions ~0.001$"$, minimize spurious small-scale fluctuations, and provide a quasi-unique solution independently of initial conditions. Our method is tested with the publicly available synthetic SL data ${\tt FF-SIMS}$ and the comparison with the truth shows that the reconstruction quality is on a par with those of the best-performing LTM methods published in the literature, which have been demonstrated to outperform the existing free-form methods. In terms of the radial mass profile reconstruction, we achieve $<1$% agreements with the truth for the regions constrained by the multiple images. Finally, we apply ${\tt MARS}$ to A1689 and find that the cluster mass in the SL regime is dominated by the primary halo centered on the brightest cluster galaxy and the weaker secondary halo also coincident with the bright cluster member ~160 kpc northeast. Within the SL field, the A1689 radial profile is well-described by a Navarro-Frenk-White (NFW) profile with $c_{200}=5.53\pm0.77$ and $r_s=538^{+90}_{-100}$ kpc and we find no evidence that A1689 is over-concentrated.

astro-ph.CO↗

arXiv2021

Weak-lensing Mass Reconstruction of Galaxy Clusters with Convolutional Neural Network

We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through cosmological simulations. We control the noise level of the galaxy shear catalog such that it mimics the typical properties of the existing ground-based WL observations of galaxy clusters. We find that the mass reconstruction by our multi-layered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms the traditional reconstruction methods. The CNN method provides better pixel-to-pixel correlations with the truth, restores more accurate positions of the mass peaks, and more efficiently suppresses artifacts near the field edges. In addition, the CNN mass reconstruction lifts the mass-sheet degeneracy when applied to our projected cluster mass estimation from sufficiently large fields. This implies that this CNN algorithm can be used to measure cluster masses in a model-independent way for future wide-field WL surveys.

astro-ph.CO↗