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.Comment: 12 pages, 8 Figures, accepted for publication in MNRA

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 $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$\sigma$ 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.Comment: 16 pages, including 11 figures, accepted for publication by A&

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 $\theta_E$). 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 $\theta_E$. We find that the CNN performs well and obtain with the network trained with a uniform distribution of $\theta_E$ $>0.5"$ the following median values with $1\sigma$ scatter: $\Delta x=(0.00^{+0.30}_{-0.30})"$, $\Delta y=(0.00^{+0.30}_{-0.29})"$, $\Delta \theta_E=(0.07^{+0.29}_{-0.12})"$, $\Delta e_x = -0.01^{+0.08}_{-0.09}$ and $\Delta e_y = 0.00^{+0.08}_{-0.09}$. The bias in $\theta_E$ is driven by systems with small $\theta_E$. 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 $\theta_E>0.8"$. 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.Comment: 17 pages, 14 Figure

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 $\sigma = 432 \pm 31$ 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.Comment: 22 pages, 12 figures, 5 tables, submitted to Astronomy & Astrophysics. We welcome the comments from reader

Planck \u27s Dusty GEMS: VIII. Dense-gas reservoirs in the most active dusty starbursts at z ∼3

We present ALMA, NOEMA, and IRAM-30 m/EMIR observations of the high-density tracer molecules HCN, HCO+, and HNC in three of the brightest lensed dusty star-forming galaxies at z≲ 3-3.5, part of the Planck\u27s Dusty Gravitationally Enhanced subMillimetre Sources (GEMS), with the aim of probing the gas reservoirs closely associated with their exceptional levels of star formation. We obtained robust detections of ten emission lines between Jup = 4 and 6, as well as several additional upper flux limits. In PLCK_G244.8+54.9, the brightest source at z = 3.0, the HNC(5-4) line emission at 0.1″ resolution, together with other spatially-integrated line profiles, suggests comparable distributions of dense and more diffuse gas reservoirs, at least over the most strongly magnified regions. This rules out any major effect from differential lensing. This line is blended with CN(4-3) and in this source, we measure a HNC(5-4)/CN(4-3) flux ratio of 1.76 \ub10. 86. Dense-gas line profiles generally match those of mid-J CO lines, except in PLCK_G145.2+50.8, which also has dense-gas line fluxes that are relatively lower, perhaps due to fewer dense cores and more segregated dense and diffuse gas phases in this source. The HCO+/HCN 1 and HNC/HCN ∼ 1 line ratios in our sample are similar to those of nearby ultraluminous infrared galaxies (ULIRGs) and consistent with photon-dominated regions without any indication of important mechanical heating or active galactic nuclei feedback. We characterize the dense-gas excitation in PLCK_G244.8+54.9 using radiative transfer models assuming pure collisional excitation and find that mid-J HCN, HCO+, and HNC lines arise from a high-density phase with an H2 density of n ∼ 105-106 cm-3, although important degeneracies hinder a determination of the exact conditions. The three GEMS are consistent with extrapolations of dense-gas star-formation laws derived in the nearby Universe, adding further evidence that the extreme star-formation rates observed in the most active galaxies at z ∼ 3 are a consequence of their important dense-gas contents. The dense-gas-mass fractions traced by HCN/[CI] and HCO+/[CI] line ratios are elevated, but not exceptional as compared to other lensed dusty star-forming galaxies at z &gt; 2, and they fall near the upper envelope of local ULIRGs. Despite the higher overall gas fractions and local gas-mass surface densities observed at high redshift, the dense-gas budget of rapidly star-forming galaxies seems to have evolved little between z ∼ 3 and z ∼ 0. Our results favor constant dense-gas depletion times in these populations, which is in agreement with theoretical models of star formation

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human ’expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25% of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with g-band signal-to-noise less than ∼25 or Einstein radii less than ∼1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies

Planck’s dusty GEMS - IV. Star formation and feedback in a maximum starburst at z = 3 seen at 60-pc resolution

International audienceWe present an analysis of high-resolution ALMA interferometry of CO(4–3) line emission and dust continuum in the “Ruby” (PLCK_G244.8+54.9), a bright, gravitationally lensed galaxy at z = 3.0 discovered with the Planck all-sky survey. The Ruby is the brightest of Planck’s dusty GEMS, a sample of 11 of the brightest gravitationally lensed high-redshift galaxies on the extragalactic sub-mm sky. We resolve the high-surface-brightness continuum and CO line emission of the Ruby in several extended clumps along a partial, nearly circular Einstein ring with 1.4′′ diameter around a massive galaxy at z = 1.5. Local star-formation intensities are up to 2000 M⊙ yr-1 kpc-2, amongst the highest observed at high redshift, and clearly in the range of maximal starbursts. Gas-mass surface densities are a few × 104M⊙ pc-2. The Ruby lies at, and in part even above, the starburst sequence in the Schmidt-Kennicutt diagram, and at the limit expected for star formation that is self-regulated through the kinetic energy injection from radiation pressure, stellar winds, and supernovae. We show that these processes can also inject sufficient kinetic energy and momentum into the gas to explain the turbulent line widths, which are consistent with marginally gravitationally bound molecular clouds embedded in a critically Toomre-stable disk. The star-formation efficiency is in the range 1–10% per free-fall time, consistent with the notion that the pressure balance that sets the local star-formation law in the Milky Way may well be universal out to the highest star-formation intensities. AGN feedback is not necessary to regulate the star formation in the Ruby, in agreement with the absence of a bright AGN component in the infrared and radio regimes.Key words: galaxies: starburst / galaxies: high-redshift / submillimeter: galaxies / galaxies: evolution / galaxies: star formation / galaxies: ISM⋆ Based on ALMA data obtained with program 2015.1.01518.S

Photometric redshift estimation with a convolutional neural network: NetZ

Galaxy redshifts are a key characteristic 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 have 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 that 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 the training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range, where it fares better than other methods on the same data. We obtained a precision |zpred − zref| 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 ∼4. We carried out a comparison with a network trained on point-like sources, highlighting the importance of morphological information for our redshift estimation. By limiting the scope to smaller redshift ranges or to luminous red galaxies, we find a further notable improvement. We have published more than 34 million new photo-z values predicted with NetZ. This shows that the new method is very simple and swift in application, and, importantly, it covers a wide redshift range that is limited only by the available training data. It is broadly applicable, particularly with regard to upcoming surveys such as the Rubin Observatory Legacy Survey of Space and Time, which will provide images of billions of galaxies with similar image quality as HSC. Our HSC photo-z estimates are also beneficial to the Euclid survey, given the overlap in the footprints of the HSC and Euclid

Searching for Strong Gravitational Lenses

Acknowledgements: We thank the International Space Science Institute in Bern (ISSI) for their hospitality and the conveners for organizing the stimulating workshop on “Strong Gravitational Lensing”.Funder: EPFL LausanneAbstractStrong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered – a task that can be met with significant contamination by other astrophysical objects and asterisms. Here we review strong lens searches, covering various sources (quasars, galaxies, supernovae, FRBs, GRBs, and GWs), lenses (early- and late-type galaxies, groups, and clusters), datasets (imaging, spectra, and lightcurves), and wavelengths. We first present the physical characteristics of the lens and source populations, highlighting relevant details for constructing targeted searches. Search techniques are described based on the main lensing feature that is required for the technique to work, namely one of: (i) an associated magnification, (ii) multiple spatially-resolved images, (iii) multiple redshifts, or (iv) a non-zero time delay between images. To use the current lens samples for science, and for the design of future searches, we list several selection biases that exist due to these discovery techniques. We conclude by discussing the future of lens searches in upcoming surveys and the new population of lenses that will be discovered.</jats:p