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

Read the paperDOI: 10.48550/arXiv.2403.03253

TL;DR

Imagine you're looking at a distant flashlight through a glass marble — the marble bends the light and creates multiple distorted images of the flashlight. Now imagine tiny invisible lumps scattered around the marble. Those lumps would subtly warp the images in ways we can measure. That's gravitational lensing! Dark matter forms these invisible lumps (called subhalos), and different theories of what dark matter IS predict different sizes and numbers of these lumps. This paper combines two ways of studying those warped images — the brightness of the multiple images AND the smeared arc of light from the galaxy around the flashlight — to get a much sharper picture of those tiny lumps. They also built a mathematical shortcut that makes the calculations 100 to 1000 times faster. The upshot: they can now test whether dark matter clumps exist down to sizes smaller than has ever been probed before, helping us rule out certain types of dark matter particles.

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.

  • 1A new methodology is presented for simultaneously reconstructing lensed arcs and flux ratios in the presence of full populations of subhalos and line-of-sight halos.
  • 2A new multi-plane ray tracing approach accelerates lens mass and source light reconstruction by factors of ~100-1000.
  • 3Combining lensed arcs with flux ratios isolates small-scale dark matter substructure perturbations from main deflector mass profile uncertainties on larger angular scales.
  • 4Incorporating arcs strengthens likelihood ratios penalizing warm dark matter by factors of 1.3, 2.5, 5.6, and 13.1 across successive half-decade bins in halo mass suppression scale.
  • 5The 95% exclusion limit on the warm dark matter halo mass suppression scale improves by 0.5 dex, pushing substructure lensing to the threshold of galaxy formation.
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The 2026 World Cup's grass is an engineering problem

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Monthly Notices of the Royal Astronomical Society·

Remarks on the disproof of the unit distance conjecture

Imagine you scatter a bunch of dots on a piece of paper. The question is: how many pairs of those dots can be exactly 1 inch apart? The Erdős unit distance conjecture asked whether there's a specific mathematical formula that limits how often this can happen as you add more and more dots. Think of it like asking how many friendships can exist in a town where friends are defined as people who live exactly one mile apart — there's a suspected maximum, and Erdős guessed what that maximum should be. For decades, no one could prove or disprove his guess. Now, an AI apparently found a specific arrangement of dots (a 'counterexample') that breaks the expected limit, proving Erdős's conjecture was wrong. A team of elite mathematicians then checked and explained the AI's work in this paper.