arXiv subjects

R. Cañameras

Publications and source records attributed to R. Cañameras.

At least 19 recordsLinked to original sources

arXiv2026

Strong Lensing Model and Dust Extinction Maps of the Host Galaxy of Type Ia Supernova H0pe

Strong gravitational lensing by massive galaxy clusters offers rare opportunities to observe multiple images of distant ($z \gtrsim 2$) Type Ia supernovae (SNe) and to resolve the properties of their host galaxies. A recent outstanding example is the Type Ia SN H0pe ($z = 1.78$), which the James Webb Space Telescope (JWST) discovered in NIRCam images, when the galaxy cluster PLCK G165.7+67.0 (G165, $z = 0.35$) still produced three images of it. In this work, we build a new strong lensing model of G165, first using only the positions of multiple images of background galaxies. We then significantly increase the number of constraints around the position of SN H0pe by modeling the extended surface brightness of the SN host galaxy. Including extended image information reduces the average uncertainty on mass model parameters by more than an order of magnitude. We also study the spatial distribution of dust in the arc to estimate the dust extinction at the position of SN H0pe. We find good statistical agreement of the extinction estimate, at $\lesssim 1σ$, with three fully independent methods based on spectral energy distribution fitting. Moreover, our extended-image lens model of G165 allows us to map the dust distribution of the host galaxy from the image plane to the source plane. Supernova H0pe exploded in a region with a relatively high extinction ($A_V \approx 0.9$ mag) at around $\sim 1$ kpc from its host center. This work shows that extended image modeling in lensing clusters simultaneously reduces the uncertainty on lens model parameters and enables spatially resolved analyses of lensed transients' host galaxies. Such modeling advances are expected to play an important role in future cosmological analyses using strongly lensed SNe.

astro-ph.GA↗

arXiv2026

Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Lens model comparison and H0 measurement

MACS J0138-2155 is the only known cluster to strongly lens two supernovae (SNe), Requiem and Encore, from the same host galaxy at z=1.949. We present seven independent mass models of the galaxy cluster built using six software packages. By conducting a blind analysis (no exchanges of results between modeling teams), we quantified uncertainties due to modeling and software. Through HST, JWST and MUSE observations, we assembled high-quality data products, including eight "gold" lensed image systems consisting of 23 images with secure spectroscopic redshifts, and one "silver" system with a likely redshift value. Restricting to the gold images, we obtain overall consistent model predictions of the positions, magnifications and time delays of SN Encore and SN Requiem images, especially for models with $χ^2 \leq 25$. We predict the appearance of the next images of SNe Encore and Requiem with a time delay of >~3000 days and of ~3700 to 4000 days, respectively, based on a fiducial cosmological model of $H_0 = 70 {\rm\ km\ s^{-1}\ Mpc^{-1}}$ and $Ω_{\rm m} = 0.3$. We obtain relations between $H_0$ and the time delays of SNe Encore and Requiem. In particular, for $H_0 = 73 {\rm\ km\ s^{-1}\ Mpc^{-1}}$, the four lowest $χ^2$ models predict SN Requiem to reappear in ~Apr-Dec 2026; for $H_0 = 67 {\rm\ km\ s^{-1}\ Mpc^{-1}}$, in ~Mar-Nov 2027. Using the newly measured time delay between the two detected images of SN Encore by Pierel et al. (2026) and our mass models, we jointly infer $H_0 = {\rm 66.9^{+11.2}_{-8.1}\ km\ s^{-1}\ Mpc^{-1}}$, where the uncertainty is dominated by that of the time delay. The long delays of the next-appearing SN Requiem and SN Encore images provide excellent opportunities to measure $H_0$ with an uncertainty of 2-3%. Our mass models form the basis for cosmological inference from this unique lens cluster with two strongly lensed SNe. (Abridged)

astro-ph.CO↗

arXiv2026

HOLISMOKES XX. Lens models of binary lens galaxies with five images of Supernova Winny

Strongly lensed supernovae (SNe) provide a powerful way to study cosmology, SNe and galaxies. Modelling the lens system is key to extracting astrophysical and cosmological information. We present adaptive-optics-assisted high-resolution images of SN Winny (SN 2025wny) in the J and K filters obtained with the Large Binocular Telescope (LBT). The LBT imaging confirms the presence of a fifth point source, whose colour is consistent with that of the other SN images at similar phases, while lens modelling robustly supports its interpretation as an additional image of SN~Winny. We measure the positions of the five SN images with uncertainties varying between 1 and 14 milliarcseconds. We build the first mass models using lenstronomy and GLEE, and explore three classes of mass models for the two lens galaxies G1 and G2. The optimal model class of the three is a singular isothermal ellipsoid for G1, a singular isothermal sphere for G2, and an external shear. We infer the enclosed masses within the Einstein radius as 4.61^{+0.06}_{-0.04} \times 10^{11}\,M_\odot for G1 and 1.01\pm0.02 \times 10^{11}\,M_\odot for G2. The lensing configuration by the two lens galaxies can produce two additional magnified SN images beyond the five observed ones; the exclusion of such model configurations further constrains the lens model parameters. Our model fits to the observed image positions with an RMS of ~0.0012" - 0.0025", within the observed positional uncertainties. The predicted magnifications of the multiple images vary between ~1.6 (for the faintest fifth image E) to ~10 (for the brightest image A). The predicted relative lensing magnifications of the multiple images do not match that of the observed within 2σuncertainties. The differences in the relative magnifications could be due to millilensing/microlensing. Our mass models form the basis for future analyses of this unique system. (abridged)

astro-ph.GA↗

arXiv2025

Cosmology with supernova Encore in the strong lensing cluster MACS J0138$-$2155: photometry, cluster members, and lens mass model

The strongly lensed Supernova (SN) Encore at a redshift of $z = 1.949$, discovered behind the galaxy cluster MACS J0138$-$2155 at $z=0.336$, provides a rare opportunity for time-delay cosmography and studies of the SN host galaxy, where previously another SN, called SN Requiem, had appeared. To enable these studies, we combine new James Webb Space Telescope (JWST) imaging, archival Hubble Space Telescope (HST) imaging, and new Very Large Telescope (VLT) spectroscopic data to construct state-of-the-art lens mass models that are composed of cluster dark-matter (DM) halos and galaxies. We determine the photometric and structural parameters of the galaxies across six JWST and five HST filters. We use the color-magnitude and color-color relations of spectroscopically-confirmed cluster members to select additional cluster members, identifying a total of 84 galaxies belonging to the galaxy cluster. We construct seven different mass models using a variety of DM halo mass profiles, and explore both multi-plane and approximate single-plane lens models. As constraints, we use the observed positions of 23 multiple images from eight multiply lensed sources at four distinct spectroscopic redshifts. In addition, we use stellar velocity dispersion measurements to obtain priors on the galaxy mass distributions. We find that six of the seven models fit well to the observed image positions. Mass models with cored-isothermal DM profiles fit well to the observations, whereas the mass model with a Navarro-Frenk-White cluster DM profile has an image-position $χ^2$ value that is four times higher. We build our ultimate model by combining four multi-lens-plane mass models and predict the image positions and magnifications of SN Encore and SN Requiem. Our work lays the foundation for building state-of-the-art mass models of the cluster for future cosmological analysis and SN host galaxy studies.

astro-ph.CO↗

arXiv2025

HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal

We have carried out a systematic search for galaxy-scale lenses exploiting multi-band imaging data from the third public data release of the Hyper Suprime-Cam (HSC) survey with the focus on false-positive removal, after applying deep learning classifiers to all 110 million sources with i-Kron radius above 0.8". To improve the performance, we tested the combination of multiple networks from our previous lens search projects and found the best performance by averaging the scores from five of our networks. Although this ensemble network leads already to a false-positive rate (FPR) of 0.01% at a true-positive rate (TPR) of 75% on known real lenses, we have elaborated techniques to further clean the network candidate list before visual inspection. In detail, we tested the rejection using SExtractor and the modeling network from HOLISMOKES IX, which resulted together in a candidate rejection of 29% without lowering the TPR. We carried out a comprehensive multi-stage visual inspection involving eight individuals and identified 95 grade A (average grade G >2.5) and 503 grade B (2.5 >G >1.5) lens candidates, including 92 discoveries reported for the first time. This inspection also incorporated a novel environmental characterization using histograms of photometric redshifts. We publicly release the average grades, mass model predictions, and environment characterization of all visually inspected candidates, while including references for previously discovered systems, which makes this catalog one of the largest compilation of known lenses. The results demonstrate that (1) the combination of multiple networks enhances the selection performance and (2) both automated masking tools as well as modeling networks, which can be easily applied to hundreds of thousands of network candidates, help reduce the number of false positives that is the main limitation in lens search to date.

astro-ph.IM↗

arXiv2025

Cosmology with Supernova Encore in the lensing cluster MACS J0138$-$2155 -- Spectroscopy with MUSE

We present a spectroscopic analysis of MACS J0138$-$2155, at $z=0.336$, the first galaxy cluster hosting two strongly-lensed supernovae (SNe), Requiem and Encore, providing us with a chance to obtain a reliable $H_0$ measurement from the time delays between the multiple images. We take advantage of new data from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope, covering a central $1 \rm \, arcmin^2$ of the lensing cluster, for a total depth of 3.7 hours, including 2.9 hours recently obtained by our Target of Opportunity programme. Our new spectroscopic catalogue contains reliable redshifts for 107 objects, including 50 galaxy cluster members with secure redshift values in the range $0.324 < z < 0.349$, and 13 lensed multiple images from four background sources between $0.767\leq z \leq 3.420$, including four images of the host galaxy of the two SNe. We exploit the MUSE data to study the stellar kinematics of 14 bright cluster members and two background galaxies, obtaining reliable measurements of their line-of-sight velocity dispersion. Finally, we combine these results with measurements of the total magnitude of the cluster members in the Hubble Space Telescope F160W band to calibrate the Faber-Jackson relation between luminosity and stellar velocity dispersion ($L \propto σ^{1/α}$) for the early-type cluster member galaxies, measuring a slope $α=0.25^{+0.05}_{-0.05}$. A pure and complete sample of cluster member galaxies and a reliable characterisation of their total mass structure are key to building accurate total mass maps of the cluster, mitigating the impact of parametric degeneracies, which is necessary for inferring the value of $H_0$ from the measured time delays between the lensed images of the two SNe.

astro-ph.GA↗

arXiv2024

HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging

In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We propose a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case the Hubble Space Telescope, HST) with lower-resolution multi-band images (in our case Legacy survey, LS) using machine learning. This methodology aims to simulate the operational strategies that will be employed by future missions, such as combining the images of Euclid and the Rubin Observatory's Legacy Survey of Space and Time (LSST). To compensate for the scarcity of lensed galaxy images for network training, we have generated mock lenses by superimposing arc features onto HST images, saved the lens parameters, and replicated the lens system in the LS images. We test four architectures based on ResNet-18: (1) using single-band HST images, (2) using three bands of LS images, (3) stacking these images after interpolating the LS images to HST pixel scale for simultaneous processing, and (4) merging a ResNet branch of HST with a ResNet branch of LS before the fully connected layer. We compare these architecture performances by creating Receiver Operating Characteristic (ROC) curves for each model and comparing their output scores. At a false-positive rate of $10^{-4}$, the true-positive rate is $\sim$0.41, $\sim$0.45, $\sim$0.51 and $\sim$0.55, for HST, LS, stacked images and merged branches, respectively. Our results demonstrate that models integrating images from both the HST and LS significantly enhance the detection of galaxy-scale lenses compared to models relying on data from a single instrument. These results show the potential benefits of using both Euclid and LSST images, as wide-field imaging surveys are expected to discover approximately 100,000 lenses.

astro-ph.IM↗

arXiv2024

Cosmography from accurate mass modeling of the lens group SDSS J0100+1818: five sources at three different redshifts

Systems where multiple sources at different redshifts are strongly lensed by the same deflector allow one to directly investigate the evolution of the angular diameter distances with redshift, and thus to learn about the geometry of the Universe. We present measurements of the values of the total matter density, $Ω_m$, and of the dark energy equation of state parameter, $w$, through a strong lensing analysis of SDSSJ0100+1818, a group-scale system at $z=0.581$ with five lensed sources, from $z=1.698$ to $4.95$. We use new MUSE data to securely measure the redshift of 65 sources, including the five multiply imaged background sources (lensed into a total of 18 multiple images) and 19 galaxies on the deflector plane (the brightest group galaxy, BGG, and 18 fainter members), all employed to build robust strong lensing models with the software GLEE. We measure $Ω_m = 0.14^{+0.16}_{-0.09}$ in a flat $Λ$ cold dark matter (CDM) model, and $Ω_m = 0.19^{+0.17}_{-0.10}$ and $w=-1.27_{-0.48}^{+0.43}$ in a flat $w$CDM model. We quantify, through a multi-plane approach, the impact of different sources angularly close in projection on the inferred values of the cosmological parameters. We obtain consistent median values, with uncertainties for only $Ω_m$ increasing by a factor of 1.5. We accurately measure a total mass of $(1.55 \pm 0.01) \times 10^{13}$ M$_\odot$ within 50 kpc and a stellar over total mass profile decreasing from $45.6^{+8.7}_{-8.3}\%$ at the BGG effective radius to $(6.6\pm 1.1)\%$ at $R\approx 77$ kpc. Our results confirm that SDSSJ0100+1818 is one of the most massive (lens) galaxies known at intermediate redshift and that group-scale systems that act as lenses for $\geq 3$ background sources at different redshifts enable to estimate the values of the cosmological parameters with an accuracy that is competitive with that obtained from lens galaxy clusters.

astro-ph.CO↗

arXiv2024

Lensed Type Ia Supernova "Encore" at z=2: The First Instance of Two Multiply-Imaged Supernovae in the Same Host Galaxy

A bright ($m_{\rm F150W,AB}$=24 mag), $z=1.95$ supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply-imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called "Requiem", and therefore the new SN is named "Encore". This makes the MACS J0138.0$-$2155 cluster the first known system to produce more than one multiply-imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a $\sim10$ year window to be $\approx3\%$. These observations have the potential to yield a Hubble Constant ($H_0$) measurement with $\sim10\%$ precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of $\sim2030$, providing an unprecedented baseline for time-delay cosmography.

astro-ph.CO↗

arXiv2023

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $θ_E$) and the external shear ($γ_{ext,1}$, $γ_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$σ$ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, while differences remain in predicting the external shear. From our tests, most likely the low image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations. Our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.

astro-ph.IM↗

arXiv2022

Reconstructing the extended structure of multiple sources strongly lensed by the ultra-massive elliptical galaxy SDSS J0100+1818

We study the total and baryonic mass distributions of the deflector SDSS J0100+1818 through a full strong lensing analysis. The system is composed by an ultra-massive early-type galaxy at $z=0.581$, with total stellar mass of $(1.5 \pm 0.3) 10^{12}$ M$_\odot$ and stellar velocity dispersion of ($450 \pm 40$) km s$^{-1}$, surrounded by ten multiple images of three background sources, two of which spectroscopically confirmed at $z=1.880$. We take advantage of high-resolution HST photometry and VLT/X-shooter spectroscopy to measure the positions of the multiple images and perform a strong lensing study with the software GLEE. We test different total mass profiles for the lens and model the background sources first as point-like and then as extended objects. We successfully predict the positions of the observed multiple images and reconstruct over approximately 7200 HST pixels the complex surface brightness distributions of the sources. We measure the cumulative total mass profile of the lens and find a total mass value of $(9.1 \pm 0.1) 10^{12}$ M$_\odot$, within the Einstein radius of approximately 42 kpc, and stellar-over-total mass fractions ranging from ($49 \pm 12$)%, at the half-light radius ($R_e = 9.3$ kpc) of the lens galaxy, to ($10 \pm 2$)%, in the outer regions ($R = 70$kpc). These results suggest that the baryonic mass component of SDSS J0100+1818 is very concentrated in its core and that the lens early-type galaxy/group is immersed in a massive dark matter halo. This is consistent with what found in other ultra-high mass candidates at intermediate redshift. We measure also the physical sizes of the distant sources, resolving them down to a few hundreds of parsec. Finally, we quantify and discuss a relevant source of systematic uncertainties on the reconstructed sizes of background galaxies, associated to the adopted lens total mass model.

astro-ph.GA↗

arXiv2022

Constraining the multi-scale dark-matter distribution in CASSOWARY 31 with strong gravitational lensing and stellar dynamics

We study the inner structure of the group-scale lens CASSOWARY 31 (CSWA 31) by adopting both strong lensing and dynamical modeling. CSWA 31 is a peculiar lens system. The brightest group galaxy (BGG) is an ultra-massive elliptical galaxy at z = 0.683 with a weighted mean velocity dispersion of $σ= 432 \pm 31$ km s$^{-1}$. It is surrounded by group members and several lensed arcs probing up to ~150 kpc in projection. Our results significantly improve previous analyses of CSWA 31 thanks to the new HST imaging and MUSE integral-field spectroscopy. From the secure identification of five sets of multiple images and measurements of the spatially-resolved stellar kinematics of the BGG, we conduct a detailed analysis of the multi-scale mass distribution using various modeling approaches, both in the single and multiple lens-plane scenarios. Our best-fit mass models reproduce the positions of multiple images and provide robust reconstructions for two background galaxies at z = 1.4869 and z = 2.763. The relative contributions from the BGG and group-scale halo are remarkably consistent in our three reference models, demonstrating the self-consistency between strong lensing analyses based on image position and extended image modeling. We find that the ultra-massive BGG dominates the projected total mass profiles within 20 kpc, while the group-scale halo dominates at larger radii. The total projected mass enclosed within $R_{eff}$ = 27.2 kpc is $1.10_{-0.04}^{+0.02} \times 10^{13}$ M$_\odot$. We find that CSWA 31 is a peculiar fossil group, strongly dark-matter dominated towards the central region, and with a projected total mass profile similar to higher-mass cluster-scale halos. The total mass-density slope within the effective radius is shallower than isothermal, consistent with previous analyses of early-type galaxies in overdense environments.

astro-ph.GA↗

arXiv2022

Strong lensing in UNIONS: Toward a pipeline from discovery to modeling

We present a search for galaxy-scale strong gravitational lenses in the initial 2 500 square degrees of the Canada-France Imaging Survey (CFIS). We designed a convolutional neural network (CNN) committee that we applied to a selection of 2 344 002 exquisite-seeing $r$-band images of color-selected luminous red galaxies (LRGs). Our classification uses a realistic training set where the lensing galaxies and the lensed sources are both taken from real data, namely the CFIS $r$-band images themselves and the Hubble Space Telescope (HST). A total of 9 460 candidates obtain a score above 0.5 with the CNN committee. After a visual inspection of the candidates, we find a total of 133 lens candidates, of which 104 are completely new. The set of false positives mainly contains ring, spiral, and merger galaxies, and to a lesser extent galaxies with nearby companions. We classify 32 of the lens candidates as secure lenses and 101 as maybe lenses. For the 32 highest quality lenses, we also fit a singular isothermal ellipsoid mass profile with external shear along with an elliptical Sersic profile for the lens and source light. This automated modeling step provides distributions of properties for both sources and lenses that have Einstein radii in the range $0.5\arcsec<θ_E<2.5\arcsec$. Finally, we introduce a new lens and/or source single-band deblending algorithm based on auto-encoder representation of our candidates. This is the first time an end-to-end lens-finding and modeling pipeline is assembled together, in view of future lens searches in a single band, as will be possible with Euclid.

astro-ph.CO↗

arXiv2021

Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks

We performed a search for strong lens galaxy-scale systems in the first data release of the Dark Energy Survey (DES), from a color-selected parent sample of 18~745~029 Luminous Red Galaxies (LRGs). Our search was based on a Convolutional Neural Network (CNN) to grade our LRG selection with values between 0 (non-lens) and 1 (lens). Our training set was data-driven, i.e. using lensed sources taken from HST COSMOS images and where the light distribution of the lens plane was taken directly from DES images of our LRGs. A total of 76~582 cutouts obtained a score above 0.9. These were visually inspected and resulted in two catalogs. The first one contains 405 lens candidates, where 90 present clear lensing features and counterparts, while the others 315 require more evidence, such as higher resolution images or spectra to be conclusive. A total of 186 candidates were totally new identified in this search. The second catalog includes 539 ring galaxy candidates that will be useful to train CNNs against this type of false positives. For the 90 best lens candidates we carried out color-based deblending of the lens and source light without fitting any analytical profile to the data. The method turned out to be very efficient in the deblending, even for very compact objects and for objects with very complex morphology. Finally, from the 90 best lens candidates we selected 52 systems having one single deflector, to test an automated modeling pipeline which successfully modeled 79\% of the sample within an acceptable amount of computing time.

astro-ph.GA↗

arXiv2021

Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

The redshifts of galaxies are a key attribute that is needed for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-$z$. We developed NetZ, a new method using a Convolutional Neural Network (CNN) to predict the photo-$z$ based on galaxy images, in contrast to previous methods which often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-$z$ range better than other methods on the same data. We obtain an accuracy $|z_\text{pred}-z_\text{ref}|$ of $σ= 0.12$ (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to $\sim$4. By limiting to smaller redshift ranges or to Luminous Red Galaxies (LRGs), we find a further notable improvement. We publish more than 34 million new photo-$z$ values predicted with NetZ here. This shows that the new method is very simple and fast to apply, and, importantly, covers a wide redshift range limited only by the available training data. It is broadly applicable and beneficial to imaging surveys, particularly upcoming surveys like the Rubin Observatory Legacy Survey of Space and Time which will provide images of billions of galaxies with similar image quality as HSC.

astro-ph.GA↗

arXiv2020

HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning

Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional MCMC techniques that are time consuming. We train a CNN on images of galaxy-scale lenses to predict the parameters of the SIE mass model ($x,y,e_x,e_y$, and $θ_E$). To train the network, we simulate images based on real observations from the HSC Survey for the lens galaxies and from the HUDF as lensed galaxies. We tested different network architectures, the effect of different data sets, and using different input distributions of $θ_E$. We find that the CNN performs well and obtain with the network trained with a uniform distribution of $θ_E$ $>0.5"$ the following median values with $1σ$ scatter: $Δx=(0.00^{+0.30}_{-0.30})"$, $Δy=(0.00^{+0.30}_{-0.29})" $, $Δθ_E=(0.07^{+0.29}_{-0.12})"$, $Δe_x = -0.01^{+0.08}_{-0.09}$ and $Δe_y = 0.00^{+0.08}_{-0.09}$. The bias in $θ_E$ is driven by systems with small $θ_E$. Therefore, when we further predict the multiple lensed image positions and time delays based on the network output, we apply the network to the sample limited to $θ_E>0.8"$. In this case, the offset between the predicted and input lensed image positions is $(0.00_{-0.29}^{+0.29})"$ and $(0.00_{-0.31}^{+0.32})"$ for $x$ and $y$, respectively. For the fractional difference between the predicted and true time delay, we obtain $0.04_{-0.05}^{+0.27}$. Our CNN is able to predict the SIE parameters in fractions of a second on a single CPU and with the output we can predict the image positions and time delays in an automated way, such that we are able to process efficiently the huge amount of expected lens detections in the near future.

astro-ph.GA↗

arXiv2020

HOLISMOKES -- I. Highly Optimised Lensing Investigations of Supernovae, Microlensing Objects, and Kinematics of Ellipticals and Spirals

We present the HOLISMOKES programme on strong gravitational lensing of supernovae as a probe of supernova (SN) physics and cosmology. We investigate the effects of microlensing on early-phase SN Ia spectra using four different SN explosion models, and find that within 10 rest-frame days after SN explosion, distortions of SN Ia spectra due to microlensing are typically negligible ($<$1% distortion within the 1$σ$ spread, and $\lesssim$10% distortion within the 2$σ$ spread). This shows great prospects of using lensed SNe Ia to obtain intrinsic early-phase SN spectra for deciphering SN Ia progenitors. As a demonstration of the usefulness of lensed SNe Ia for cosmology, we simulate a sample of mock lensed SN Ia systems that are expected to have accurate and precise time-delay measurements in the era of the Rubin Observatory Legacy Survey of Space and Time (LSST). Adopting realistic yet conservative uncertainties on their time-delay distances and lens angular diameter distances (of 6.6% and 5%, respectively), we find that a sample of 20 lensed SNe Ia would allow a constraint on the Hubble constant ($H_0$) with 1.3% uncertainty in the flat $Λ$CDM cosmology. We find a similar constraint on $H_0$ in an open $Λ$CDM cosmology, while the constraint degrades to $3\%$ in a flat $w$CDM cosmology. We anticipate lensed SNe to be an independent and powerful probe of SN physics and cosmology in the upcoming LSST era.

astro-ph.CO↗

arXiv2018

Testing strong line metallicity diagnostics at z~2

High-z galaxy gas-phase metallicities are usually determined through observations of strong optical emission lines with calibrations tied to the local universe. Recent debate has questioned if these calibrations are valid in the high-z universe. We investigate this by analysing a sample of 16 galaxies at z~2 available in the literature, and for which the metallicity can be robustly determined using oxygen auroral lines. The sample spans a redshift range of 1.4 < z < 3.6, has metallicities of 7.4-8.4 in 12+log(O/H) and stellar masses 10^7.5-10^11 Msun. We test commonly used strong line diagnostics (R23, O3, O2, O32, N2, O3N2 and Ne3O2 ) as prescribed by four different sets of empirical calibrations, as well as one fully theoretical calibration. We find that none of the strong line diagnostics (or calibration set) tested perform consistently better than the others. Amongst the line ratios tested, R23 and O3 deliver the best results, with accuracies as good as 0.01-0.04 dex and dispersions of ~0.2 dex in two of the calibrations tested. Generally, line ratios involving nitrogen predict higher values of metallicity, while results with O32 and Ne3O2 show large dispersions. The theoretical calibration yields an accuracy of 0.06 dex, comparable to the best strong line methods. We conclude that, within the metallicity range tested in this work, the locally calibrated diagnostics can still be reliably applied at z~2.

astro-ph.GA↗