skip to main content

This content will become publicly available on May 5, 2023

Title: Finding quadruply imaged quasars with machine learning – I. Methods
ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 more » multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. « less
Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; « less
Award ID(s):
1906976
Publication Date:
NSF-PAR ID:
10337831
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
513
Issue:
2
Page Range or eLocation-ID:
2407 to 2421
ISSN:
0035-8711
Sponsoring Org:
National Science Foundation
More Like this
  1. ABSTRACT We report the results of the STRong lensing Insights into the Dark Energy Survey (STRIDES) follow-up campaign of the late 2017/early 2018 season. We obtained spectra of 65 lensed quasar candidates with ESO Faint Object Spectrograph and Camera 2 on the NTT and Echellette Spectrograph and Imager on Keck, confirming 10 new lensed quasars and 10 quasar pairs. Eight lensed quasars are doubly imaged with source redshifts between 0.99 and 2.90, one is triply imaged (DESJ0345−2545, z = 1.68), and one is quadruply imaged (quad: DESJ0053−2012, z = 3.8). Singular isothermal ellipsoid models for the doubles, based on high-resolution imaging frommore »SAMI on Southern Astrophysical Research Telescope or Near InfraRed Camera 2 on Keck, give total magnifications between 3.2 and 5.6, and Einstein radii between 0.49 and 1.97 arcsec. After spectroscopic follow-up, we extract multi-epoch grizY photometry of confirmed lensed quasars and contaminant quasar + star pairs from DES data using parametric multiband modelling, and compare variability in each system’s components. By measuring the reduced χ2 associated with fitting all epochs to the same magnitude, we find a simple cut on the less variable component that retains all confirmed lensed quasars, while removing 94 per cent of contaminant systems. Based on our spectroscopic follow-up, this variability information improves selection of lensed quasars and quasar pairs from 34-45 per cent to 51–70 per cent, with most remaining contaminants being star-forming galaxies. Using mock lensed quasar light curves we demonstrate that selection based only on variability will over-represent the quad fraction by 10 per cent over a complete DES magnitude-limited sample, explained by the magnification bias and hence lower luminosity/more variable sources in quads.« less
  2. ABSTRACT We report the result of searching for globular clusters (GCs) around 55 Milky Way (MW) satellite dwarf galaxies within the distance of 450 kpc from the Galactic Centre except for the Large and Small Magellanic Clouds and the Sagittarius dwarf. For each dwarf, we analyse the stellar distribution of sources in Gaia DR2, selected by magnitude, proper motion, and source morphology. Using the kernel density estimation of stellar number counts, we identify 11 possible GC candidates. Cross-matched with existing imaging data, all 11 objects are known either GCs or galaxies and only Fornax GC 1–6 among them are associated withmore »the targeted dwarf galaxy. Using simulated GCs, we calculate the GC detection limit $M_{\rm V}^{\rm lim}$ that spans the range from $M_{\rm V}^{\rm lim}\sim -7$ for distant dwarfs to $M_{\rm V}^{\rm lim}\sim 0$ for nearby systems. Assuming a Gaussian GC luminosity function, we compute that the completeness of the GC search is above 90 per cent for most dwarf galaxies. We construct the 90 per cent credible intervals/upper limits on the GC specific frequency SN of the MW dwarf galaxies: 12 < SN < 47 for Fornax, SN < 20 for the dwarfs with −12 < MV < −10, SN < 30 for the dwarfs with −10 < MV < −7, and SN < 90 for the dwarfs with MV > −7. Based on SN, we derive the probability of galaxies hosting GCs given their luminosity, finding that the probability of galaxies fainter than MV = −9 to host GCs is lower than 0.1.« less
  3. ABSTRACT

    We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblendingmore »capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.

    « less
  4. ABSTRACT The free-streaming length of dark matter depends on fundamental dark matter physics, and determines the abundance and concentration of dark matter haloes on sub-galactic scales. Using the image positions and flux ratios from eight quadruply imaged quasars, we constrain the free-streaming length of dark matter and the amplitude of the subhalo mass function (SHMF). We model both main deflector subhaloes and haloes along the line of sight, and account for warm dark matter free-streaming effects on the mass function and mass–concentration relation. By calibrating the scaling of the SHMF with host halo mass and redshift using a suite ofmore »simulated haloes, we infer a global normalization for the SHMF. We account for finite-size background sources, and marginalize over the mass profile of the main deflector. Parametrizing dark matter free-streaming through the half-mode mass mhm, we constrain the thermal relic particle mass mDM corresponding to mhm. At $95 \, {\rm per\, cent}$ CI: mhm < 107.8 M⊙ ($m_{\rm {DM}} \gt 5.2 \ \rm {keV}$). We disfavour $m_{\rm {DM}} = 4.0 \,\rm {keV}$ and $m_{\rm {DM}} = 3.0 \,\rm {keV}$ with likelihood ratios of 7:1 and 30:1, respectively, relative to the peak of the posterior distribution. Assuming cold dark matter, we constrain the projected mass in substructure between 106 and 109 M⊙ near lensed images. At $68 \, {\rm per\, cent}$ CI, we infer $2.0{-}6.1 \times 10^{7}\, {{\rm M}_{\odot }}\,\rm {kpc^{-2}}$, corresponding to mean projected mass fraction $\bar{f}_{\rm {sub}} = 0.035_{-0.017}^{+0.021}$. At $95 \, {\rm per\, cent}$ CI, we obtain a lower bound on the projected mass of $0.6 \times 10^{7} \,{{\rm M}_{\odot }}\,\rm {kpc^{-2}}$, corresponding to $\bar{f}_{\rm {sub}} \gt 0.005$. These results agree with the predictions of cold dark matter.« less
  5. ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecturemore »(ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.« less