arXiv subjects

Maverick S. H. Oh

Publications and source records attributed to Maverick S. H. Oh.

4 recordsLinked to original sources

arXiv2025

Joint Semi-Analytic Multipole Priors from Galaxy Isophotes and Constraints from Lensed Arcs

Flux-ratio anomalies in quadruply imaged quasars are sensitive to the imprint of low-mass dark-matter haloes. The reliability of detection depends on the robustness of the smooth mass model. Optical surveys show that massive early-type galaxies similar to galaxy-scale gravitational lenses depart from perfect ellipticity, exhibiting $m=3$ and $m=4$ multipole distortions. We construct the semi-analytic, five-dimensional joint population prior for the $m=3$ and $m=4$ amplitude and orientation as well as the axis ratio of the deflector, calibrated on the sample of 840 SDSS E/S0 galaxies. The parameters are fitted via hierarchical Bayesian modeling, minimizing a joint Jensen-Shannon divergence between model and data. We use this prior to model the mass distribution of mock lenses with HST quality data with different multipole amplitudes. We find that we robustly measure the true multipole amplitudes and orientations. Compared to fits that use only the four point-image positions, adding the lensed host-galaxy arcs tightens the 68 % credible regions of multipole parameters by factors of 3-12 and reduces the predicted flux-ratio uncertainties by a mean factor of ~6. This analysis does not include substructure or a complex source, and thus can be considered an upper limit on the expected improvement. The combination of arc information and realistic multipole priors therefore yields an order-of-magnitude improvement in smooth mass model precision, paving the way for more robust measurements of dark-matter substructure.

astro-ph.CO↗

arXiv2024

Turbocharging constraints on dark matter substructure through a synthesis of strong lensing flux ratios and extended lensed arcs

Strong gravitational lensing provides a purely gravitational means to infer properties of dark matter halos and thereby constrain the particle nature of dark matter. Strong lenses sometimes appear as four lensed images of a background quasar accompanied by spatially-resolved emission from the quasar host galaxy encircling the main deflector (lensed arcs). We present methodology to simultaneously reconstruct lensed arcs and relative image magnifications (flux ratios) in the presence of full populations of subhalos and line-of-sight halos. To this end, we develop a new approach for multi-plane ray tracing that accelerates lens mass and source light reconstruction by factors of $\sim 100-1000$. Using simulated data, we show that simultaneous reconstruction of lensed arcs and flux ratios isolates small-scale perturbations to flux ratios by dark matter substructure from uncertainties associated with the main deflector mass profile on larger angular scales. Relative to analyses that use only image positions and flux ratios to constrain the lens model, incorporating arcs strengthens likelihood ratios penalizing warm dark matter (WDM) with a suppression scale $m_{\rm{hm}} / M_{\odot}$ in the range $\left[10^7 - 10^{7.5}\right]$, $\left[10^{7.5} - 10^{8}\right]$, $\left[10^8 - 10^{8.5}\right]$, $\left[10^{8.5} - 10^{9}\right]$ by factors of $1.3$, $2.5$, $5.6$, and $13.1$, respectively, for a cold dark matter (CDM) ground truth. The $95\%$ exclusion limit improves by 0.5 dex in $\log_{10} m_{\rm{hm}}$. The enhanced sensitivity to low-mass halos enabled by these methods pushes the observational frontier of substructure lensing to the threshold of galaxy formation, enabling stringent tests of any theory that alters the properties of dark matter halos.

astro-ph.CO↗

arXiv2020

AdS/Deep-Learning made easy: simple examples

Deep learning has been widely and actively used in various research areas. Recently, in the gauge/gravity duality, a new deep learning technique so-called the AdS/Deep-Learning (DL) has been proposed [1, 2]. The goal of this paper is to describe the essence of the AdS/DL in the simplest possible setups, for those who want to apply it to the subject of emergent spacetime as a neural network. For prototypical examples, we choose simple classical mechanics problems. This method is a little different from standard deep learning techniques in the sense that not only do we have the right final answers but also obtain a physical understanding of learning parameters.

physics.class-ph↗

arXiv2020

Boosting Vector Calculus with the Graphical Notation

Learning vector calculus techniques is one of the major missions to be accomplished by physics undergraduates. However, beginners report various difficulties dealing with the index notation due to its bulkiness. Meanwhile, there have been graphical notations for tensor algebra that are intuitive and effective in calculations and can serve as a quick mnemonic for algebraic identities. Although they have been introduced and applied in vector algebra in the educational context, to the best of our knowledge, there have been no publications that employ the graphical notation to three-dimensional Euclidean vector calculus, involving differentiation and integration of vector fields. Aiming for physics students and educators, we introduce such "graphical vector calculus," demonstrate its pedagogical advantages, and provide enough exercises containing both purely mathematical identities and practical calculations in physics. The graphical notation can readily be utilized in the educational environment to not only lower the barriers in learning and practicing vector calculus but also make students interested and self-motivated to manipulate the vector calculus syntax and heuristically comprehend the language of tensors by themselves.

physics.ed-ph↗