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Register a new userExtracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
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Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. It is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a method to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring. Training in this way allows the network to learn the gravitational lensing of light and it is then able to accurately detect entire populations of substructure, even far from the Einstein ring. In images with a single subhalo and without noise, the network detects subhalos of mass $10^6 M_{odot}$ 62% of the time and 78% of these detected subhalos are predicted in the correct mass bin. The detection accuracy increases for heavier masses. When random noise at the level of 1% of the mean brightness of the image is included (which is a realistic approximation HST, for sources brighter than magnitude 20), the network loses sensitivity to the low-mass subhalos; with noise, the $10^{8.5}M_{odot}$ subhalos are detected 86% of the time, but the $10^8 M_{odot}$ subhalos are only detected 38% of the time. The false-positive rate is around 2 false subhalos per 100 images with and without noise, coming mostly from masses $leq10^8 M_{odot}$. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over five mass bins from $10^8 M_{odot}$ to $10^{10} M_{odot}$ the SMF slope is recovered with an error of 14.2 (16.3)% for 10 images, and this improves to 2.1 (2.6)% for 1000 images without (with 1%) noise.
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We develop a machine learning model to detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On a test set of noiseless simulated images with a single subhalo, the network is able to locate subhalos with a mass of $10^{8} M_{odot}$ and place them in the correct or adjacent mass bin, effectively detecting them 97% of the time. For this test set, the network detects subhalos down to masses of $10^{6} M_{odot}$ at 61% accuracy. However, noise limits the sensitivity to light subhalo masses. With 1% noise (with this level of noise, the distribution of signal-to-noise in the image pixels approximates that of images from the Hubble Space Telescope for sources with magnitude $< 20$), a subhalo with mass $10^{8.5}M_{odot}$ is detected 86% of the time, while subhalos with masses of $10^{8}M_{odot}$ are only detected 38% of the time. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses, subhalos far from the Einstein ring, or more than one large smooth lens.
Warm dark matter has recently become increasingly constrained by observational inferences about the low-mass end of the subhalo mass function, which would be suppressed by dark matter free streaming in the early Universe. In this work, we point out that a constraint can be placed on ultralight bosonic dark matter (often referred to as fuzzy dark matter) based on similar considerations. Recent limits on warm dark matter from strong gravitational lensing of quasars and from fluctuations in stellar streams separately translate to a lower limit of $sim 2.1 times 10^{-21}$ eV on the mass of an ultralight boson comprising all dark matter. These limits are complementary to constraints on ultralight dark matter from the Lyman-$alpha$ forest and are subject to a completely different set of assumptions and systematic uncertainties. Taken together, these probes strongly suggest that dark matter with a mass $sim 10^{-22}$ eV is not a viable way to reconcile differences between cold dark matter simulations and observations of structure on small scales.
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.
We present a new approach in the study of the Initial Mass function (IMF) in external galaxies based on quasar microlensing observations. We use measurements of quasar microlensing magnifications in 24 lensed quasars to estimate the average mass of the stellar population in the lens galaxies without any a priori assumption on the shape of the IMF. The estimated mean mass of the stars is $langle M rangle =0.16^{+0.05}_{-0.08} M_odot$ (at 68% confidence level). We use this average mass to put constraints into two important parameters characterizing the IMF of lens galaxies: the low-mass slope, $alpha_2$, and the low-mass cutoff, $M_{low}$. Combining these constraints with prior information based on lensing, stellar dynamics, and absorption spectral feature analysis, we calculate the posterior probability distribution for the parameters $M_{low}$ and $alpha_2$. We estimate values for the low-mass end slope of the IMF $langle alpha_2rangle=-2.6pm 0.9$ (heavier than that of the Milky Way) and for the low-mass cutoff $langle M_{low}rangle=0.13pm0.07$. These results are in good agreement with previous studies on these parameters and remain stable against the choice of different suitable priors.
We report on the initial results obtained with an image convolution/deconvolution computer code that we developed and used to study the image formation capabilities of the solar gravitational lens (SGL). Although the SGL of a spherical Sun creates a greatly blurred image, knowledge of the SGLs point-spread function (PSF) makes it possible to reconstruct the original image and remove the blur by way of deconvolution. We discuss the deconvolution process, which can be implemented either with direct matrix inversion or with the Fourier quotient method. We observe that the process introduces a ``penalty in the form of a reduction in the signal-to-noise ratio (SNR) of a recovered image, compared to the SNR at which the blurred image data is collected. We estimate the magnitude of this penalty using an analytical approach and confirm the results with a series of numerical simulations. We find that the penalty is substantially reduced when the spacing between image samples is large compared to the telescope aperture. The penalty can be further reduced with suitable noise filtering, which can yield ${cal O}(10)$ or better improvement for low-quality imaging data. Our results confirm that it is possible to use the SGL for imaging purposes. We offer insights on the data collection and image processing strategies that could yield a detailed image of an exoplanet within image data collection times that are consistent with the duration of a realistic space mission.
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