ORCID Profile
0000-0003-4758-4501
Current Organisation
International Centre for Radio Astronomy Research
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Publisher: Oxford University Press (OUP)
Date: 05-02-2019
DOI: 10.1093/MNRAS/STZ363
Publisher: Oxford University Press (OUP)
Date: 16-05-2022
DOI: 10.1093/PASJ/PSAC023
Abstract: This paper presents a thousand passive spiral galaxy s les at z = 0.01–0.3 based on a combined analysis of the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) and the GALEX–SDSS–WISE Legacy Catalog (GSWLC-2). Among 54871 gri galaxy cutouts taken from the HSC-SSP PDR3 over 1072 deg2, we conducted a search with deep-learning morphological classification for candidates of passive spirals below the star-forming main sequence derived by ultraviolet to mid-infrared spectral energy distribution fitting in the GSWLC-2. We then classified the candidates into 1100 passive spirals and 1141 secondary s les based on visual inspections. Most of the latter cases are considered to be passive ringed S0 or pseudo-ringed galaxies. The remaining secondary s les have ambiguous morphologies, including two peculiar objects with diamond-shaped stellar wings. The selected passive spirals have a similar distribution to the general quiescent galaxies on the EWHδ–Dn4000 diagram and concentration indices. Moreover, we detected an enhanced passive fraction of spiral galaxies in X-ray clusters. Passive spirals in galaxy clusters are preferentially located in the midterm or late infall phase on the phase–space diagram, supporting the ram pressure scenario, which has been widely advocated in previous studies. The source catalog and gri-composite images are available on the HSC-SSP PDR3 website 〈hsc.mtk.nao.ac.jp/ssp/data-release/〉. Future updates, including integration with a citizen science project dedicated to the HSC data, will achieve more effective and comprehensive classifications.
Publisher: Oxford University Press (OUP)
Date: 22-03-2023
Abstract: Interactions between galaxies leave distinguishable imprints in the form of tidal features, which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features, so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training ∼6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle, and surface brightness (μlim = 26–35 mag arcsec−2). We obtain satisfactory results with accuracy, precision, and recall values of Acc = 0.84, P = 0.72, and R = 0.85 for the test s le. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87 per cent of the tidal streams these fractions are below 75 per cent for mergers, tidal tails, and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations, and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data.
Publisher: Oxford University Press (OUP)
Date: 24-05-2019
Publisher: Zenodo
Date: 2021
Publisher: Oxford University Press (OUP)
Date: 26-09-2023
DOI: 10.1093/PASJ/PSAD055
Publisher: Oxford University Press (OUP)
Date: 02-06-2022
Abstract: The importance of the post-merger epoch in galaxy evolution has been well documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of post-mergers (~1 per cent in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the ersity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) that has been trained and evaluated on realistic mock observations of simulated galaxies from the IllustrisTNG simulations, to galaxy images from the Canada France Imaging Survey, which is part of the Ultraviolet Near Infrared Optical Northern Survey. We present the characteristics of the galaxies with the highest CNN-predicted post-merger certainties, as well as a visually confirmed subset of 699 post-mergers. We find that post-mergers with high CNN merger probabilities [p(x) & 0.8] have an average star formation rate that is 0.1 dex higher than a mass- and redshift-matched control s le. The SFR enhancement is even greater in the visually confirmed post-merger s le, a factor of 2 higher than the control s le.
Publisher: Oxford University Press (OUP)
Date: 20-09-2022
Abstract: Post-starburst galaxies (PSBs) are defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity. Identifying the mechanism(s) causing a galaxy to experience a post-starburst phase therefore provides integral insight into the causes of rapid quenching. Galaxy mergers have long been proposed as a possible post-starburst trigger. Effectively testing this hypothesis requires a large spectroscopic galaxy survey to identify the rare PSBs as well as high-quality imaging and robust morphology metrics to identify mergers. We bring together these critical elements by selecting PSBs from the overlap of the Sloan Digital Sky Survey and the Canada–France Imaging Survey and applying a suite of classification methods: non-parametric morphology metrics such as asymmetry and Gini-M20, a convolutional neural network trained to identify post-merger galaxies, and visual classification. This work is therefore the largest and most comprehensive assessment of the merger fraction of PSBs to date. We find that the merger fraction of PSBs ranges from 19 per cent to 42 per cent depending on the merger identification method and details of the PSB s le selection. These merger fractions represent an excess of 3–46× relative to non-PSB control s les. Our results demonstrate that mergers play a significant role in generating PSBs, but that other mechanisms are also required. However, applying our merger identification metrics to known post-mergers in the IllustrisTNG simulation shows that 70 per cent of recent post-mergers (≲200 Myr) would not be detected. Thus, we cannot exclude the possibility that nearly all PSBs have undergone a merger in their recent past.
Publisher: Oxford University Press (OUP)
Date: 27-02-2020
Abstract: The current consensus on the formation and evolution of the brightest cluster galaxies is that their stellar mass forms early ($z$ ≳ 4) in separate galaxies that then eventually assemble the main structure at late times ($z$ ≲ 1). However, advances in observational techniques have led to the discovery of protoclusters out to $z$ ∼ 7. If these protoclusters assemble rapidly in the early Universe, they should form the brightest cluster galaxies much earlier than suspected by the late-assembly picture. Using a combination of observationally constrained hydrodynamical and dark-matter-only simulations, we show that the stellar assembly time of a sub-set of brightest cluster galaxies occurs at high redshifts ( $z$ & 3) rather than at low redshifts ($z$ & 1), as is commonly thought. We find, using isolated non-cosmological hydrodynamical simulations, that highly overdense protoclusters assemble their stellar mass into brightest cluster galaxies within ∼1 Gyr of evolution – producing massive blue elliptical galaxies at high redshifts ($z$ ≳ 1.5). We argue that there is a downsizing effect on the cluster scale wherein some of the brightest cluster galaxies in the cores of the most-massive clusters assemble earlier than those in lower mass clusters. In those clusters with $z$ = 0 virial mass ≥ 5 × 1014 M⊙, we find that $9.8{{\\ \\rm per\\ cent}}$ have their cores assembly early, and a higher fraction of $16.4{{\\ \\rm per\\ cent}}$ in those clusters above 1015 M⊙. The James Webb Space Telescope will be able to detect and confirm our prediction in the near future, and we discuss the implications to constraining the value of σ8.
Publisher: Zenodo
Date: 2019
Publisher: Oxford University Press (OUP)
Date: 29-06-2022
Abstract: Galaxy mergers are crucial to understanding galaxy evolution, therefore we must determine their observational signatures to select them from large IFU galaxy s les such as MUSE and SAMI. We employ 24 high-resolution idealized hydrodynamical galaxy merger simulations based on the ‘Feedback In Realistic Environment’ (FIRE-2) model to determine the observability of mergers to various configurations and stages using synthetic images and velocity maps. Our mergers cover a range of orbital configurations at fixed 1:2.5 stellar mass ratio for two gas rich spirals at low redshift. Morphological and kinematic asymmetries are computed for synthetic images and velocity maps spanning each interaction. We ide the interaction sequence into three: (1) the pair phase (2) the merging phase and (3) the post-coalescence phase. We correctly identify mergers between first pericentre passage and 500 Myr after coalescence using kinematic asymmetry with 66 per cent completeness, depending upon merger phase and the field of view of the observation. We detect fewer mergers in the pair phase (40 per cent) and many more in the merging and post-coalescence phases (97 per cent). We find that merger detectability decreases with field of view, except in retrograde mergers, where centrally concentrated asymmetric kinematic features enhances their detectability. Using a cut-off derived from a combination of photometric and kinematic asymmetry, we increase these detections to 89 per cent overall, 79 per cent in pairs, and close to 100 per cent in the merging and post-coalescent phases. By using this combined asymmetry cut-off we mitigate some of the effects caused by smaller fields of view subtended by massively multiplexed integral field spectroscopy programmes.
Publisher: Oxford University Press (OUP)
Date: 30-08-2019
Abstract: We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 with a convolutional neural network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete s le of $\\sim 12\\, 000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model classifies simulated galaxies in one of the four main classes with the same uncertainty as for observed galaxies. The mass–size relations of the simulated galaxies ided by morphological type also reproduce well the slope and the normalization of observed relations which confirms a reasonable ersity of optical morphologies in the TNG suite. However we find a weak correlation between optical morphology and Sersic index in the TNG suite as opposed to SDSS which might require further investigation. The stellar mass functions (SMFs) decomposed into different morphologies still show some discrepancies with observations especially at the high-mass end. We find an overabundance of late-type galaxies ($\\sim 50{{\\ \\rm per\\ cent}}$ versus $\\sim 20{{\\ \\rm per\\ cent}}$) at the high-mass end [log(M*/M⊙) 11] of the SMF as compared to observations according to the CNN classifications and a lack of S0 galaxies ($\\sim 20{{\\ \\rm per\\ cent}}$ versus $\\sim 40{{\\ \\rm per\\ cent}}$) at intermediate masses. This work highlights the importance of detailed comparisons between observations and simulations in comparable conditions.
Publisher: Oxford University Press (OUP)
Date: 19-03-2021
Abstract: The Canada–France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ∼5000 deg2 of the sky, representing a first-rate opportunity to identify recently merged galaxies. Because of the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training s les of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 per cent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger s les identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a s le that is only ∼6 per cent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini–M20 and asymmetry methods by an order of magnitude in post-merger s le purity on the mock survey data. Although the CNN outperforms the human classifiers on s le completeness, the purity of the post-merger s le identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.
Publisher: Oxford University Press (OUP)
Date: 03-02-2017
DOI: 10.1093/MNRAS/STX276
Publisher: Oxford University Press (OUP)
Date: 08-10-2020
Abstract: We investigate the spatial structure and evolution of star formation and the interstellar medium (ISM) in interacting galaxies. We use an extensive suite of parsec-scale galaxy-merger simulations (stellar mass ratio = 2.5:1), which employs the ‘Feedback In Realistic Environments-2’ model (fire-2). This framework resolves star formation, feedback processes, and the multiphase structure of the ISM. We focus on the galaxy-pair stages of interaction. We find that close encounters substantially augment cool (H i) and cold-dense (H2) gas budgets, elevating the formation of new stars as a result. This enhancement is centrally concentrated for the secondary galaxy, and more radially extended for the primary. This behaviour is weakly dependent on orbital geometry. We also find that galaxies with elevated global star formation rate (SFR) experience intense nuclear SFR enhancement, driven by high levels of either star formation efficiency (SFE) or available cold-dense gas fuel. Galaxies with suppressed global SFR also contain a nuclear cold-dense gas reservoir, but low SFE levels diminish SFR in the central region. Concretely, in the majority of cases, SFR enhancement in the central kiloparsec is fuel-driven (55 per cent for the secondary, 71 per cent for the primary) – while central SFR suppression is efficiency-driven (91 per cent for the secondary, 97 per cent for the primary). Our numerical predictions underscore the need of substantially larger, and/or merger-dedicated, spatially resolved galaxy surveys – capable of examining vast and erse s les of interacting systems – coupled with multiwavelength c aigns aimed to capture their internal ISM structure.
Publisher: Oxford University Press (OUP)
Date: 18-10-2019
Abstract: Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\\ \\rm per\\ cent}}$ compared to $79.6{{\\ \\rm per\\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\\ \\rm per\\ cent}}$ with r-only compared to $87.1{{\\ \\rm per\\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.
Publisher: Zenodo
Date: 2022
Publisher: Oxford University Press (OUP)
Date: 07-06-2022
Abstract: The most direct way to confront observed galaxies with those formed in numerical simulations is to forward-model simulated galaxies into synthetic observations. Provided that synthetic galaxy observations include similar constraints and limitations as real observations, they can be used to (1) carry out even-handed comparisons of observation and theory and (2) map the observable characteristics of simulated galaxies to their a priori known origins. In particular, integral field spectroscopy (IFS) expands the scope of such comparisons and mappings to an exceptionally broad set of physical properties. We therefore present RealSim-IFS, a tool for forward-modelling galaxies from hydrodynamical simulations into synthetic IFS observations. The core components of RealSim-IFS model the detailed spatial s ling mechanics of any fibre-bundle, image slicer, or lenslet array IFU and corresponding observing strategy, real or imagined, and support the corresponding propagation of noise adopted by the user. The code is highly generalized and can produce cubes in any light- or mass-weighted quantity (e.g. specific intensity, gas/stellar line-of-sight velocity, stellar age/metallicity, etc.). We show that RealSim-IFS exactly reproduces the spatial reconstruction of specific intensity and variance cubes produced by the MaNGA survey Data Reduction Pipeline using the calibrated fibre spectra as input. We then apply RealSim-IFS by producing a public synthetic MaNGA stellar kinematic survey of 893 galaxies with log (M⋆/M⊙) & 10 from the TNG50 cosmological hydrodynamical simulation.
Publisher: Oxford University Press (OUP)
Date: 16-12-2020
Abstract: Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here, we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subs le of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the small-scale morphological details of galaxies from the sky background. We are able to quantitatively identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is mostly driven by small, more spheroidal, and quenched galaxies that are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched discy galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.
Publisher: American Astronomical Society
Date: 06-2023
Abstract: We employ the Feedback In Realistic Environments (FIRE-2) physics model to study how the properties of giant molecular clouds (GMCs) evolve during galaxy mergers. We conduct a pixel-by-pixel analysis of molecular gas properties in both the simulated control galaxies and galaxy major mergers. The simulated GMC pixels in the control galaxies follow a similar trend in a diagram of velocity dispersion ( σ v ) versus gas surface density (Σ mol ) to the one observed in local spiral galaxies in the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey. For GMC pixels in simulated mergers, we see a significant increase of a factor of 5–10 in both Σ mol and σ v , which puts these pixels above the trend of PHANGS galaxies in the σ v versus Σ mol diagram. This deviation may indicate that GMCs in the simulated mergers are much less gravitationally bound compared with simulated control galaxies with virial parameters ( α vir ) reaching 10–100. Furthermore, we find that the increase in α vir happens at the same time as the increase in global star formation rate, which suggests that stellar feedback is responsible for dispersing the gas. We also find that the gas depletion time is significantly lower for high- α vir GMCs during a starburst event. This is in contrast to the simple physical picture that low- α vir GMCs are easier to collapse and form stars on shorter depletion times. This might suggest that some other physical mechanisms besides self-gravity are helping the GMCs in starbursting mergers collapse and form stars.
Publisher: IOP Publishing
Date: 10-03-2020
Publisher: Oxford University Press (OUP)
Date: 22-03-2023
Abstract: How does the host galaxy morphology influence a central quasar or vice versa? We address this question by measuring the asymmetries of 2424 SDSS quasar hosts at 0.2 & z & 0.8 using broad-band (grizy) images from the Hyper Suprime-Cam Subaru Strategic Program. Control galaxies (without quasars) are selected by matching the redshifts and stellar masses of the quasar hosts. A two-step pipeline is run to decompose the PSF and Sérsic components and then measure asymmetry indices (ACAS, Aouter, and Ashape) of each quasar host and control galaxy. We find a mild correlation between host asymmetry and AGN bolometric luminosity (Lbol) for the full s le (spearman correlation of 0.37) while a stronger trend is evident at the highest luminosities (Lbol & 45). This then manifests itself into quasar hosts being more asymmetric, on average, when they harbour a more massive and highly accreting black hole. The merger fraction also positively correlates with Lbol and reaches up to 35 per cent for the most luminous. Compared to control galaxies, quasar hosts are marginally more asymmetric (excess of 0.017 in median at 9.4σ level) and the merger fractions are similar ($\\sim 16.5~{{\\ \\rm per\\ cent}}$). We quantify the dependence of asymmetry on optical band that demonstrates that mergers are more likely to be identified with the bluer bands and the correlation between Lbol and asymmetry is also stronger in such bands. We stress that the band dependence, indicative of a changing stellar population, is an important factor in considering the influence of mergers on AGN activity.
Publisher: MDPI AG
Date: 08-02-2020
Abstract: Primary healthcare personnel show high levels of burnout. A new model of burnout has been developed to distinguish three subtypes: frenetic, under-challenged, and worn-out, which are characterized as overwhelmed, under-stimulated, and disengaged at work, respectively. The aim of this study was to assess the psychometric properties of the long/short Brazilian versions of the “Burnout Clinical Subtypes Questionnaire” (BCSQ-36/BCSQ-12) among Brazilian primary healthcare staff and its possible associations with other psychological health-related outcomes. An online cross-sectional study conducted among 407 Brazilian primary healthcare personnel was developed. Participants answered a Brazil-specific survey including the BCSQ-36/BCSQ-12, “Maslach Burnout Inventory-General Survey”, “Utrecht Work Engagement Scale”, “Hospital Anxiety/Depression Scale”, “Positive-Negative Affect Schedule”, and a Visual Analogue Scale of guilt at work. The bifactor was the model with the best fit to the data using the BCSQ-36, which allowed a general factor for each subtype. The three-correlated factors model fit better to the BCSQ-12. Internal consistence was appropriate, and the convergence between the long-short versions was high. The pattern of relationships between the burnout subtypes and the psychological outcomes suggested a progressive deterioration from the frenetic to the under-challenged and worn-out. In sum, the Brazilian BCSQ-36/BCSQ-12 showed appropriate psychometrics to be used in primary healthcare personnel.
Publisher: Springer Science and Business Media LLC
Date: 28-06-2023
Publisher: Oxford University Press (OUP)
Date: 24-12-2021
Abstract: One of the central challenges to establishing the role of mergers in galaxy evolution is the selection of pure and complete merger s les in observations. In particular, while large and reasonably pure interacting galaxy pair s les can be obtained with relative ease via spectroscopic criteria, automated selection of post-coalescence merger remnants is restricted to the physical characteristics of remnants alone. Furthermore, such selection has predominantly focused on imaging data – whereas kinematic data may offer a complimentary basis for identifying merger remnants. Therefore, we examine the theoretical utility of both the morphological and kinematic features of merger remnants in distinguishing galaxy merger remnants from other galaxies. Deep classification models are calibrated and evaluated using idealized synthetic images and line-of-sight stellar velocity maps of a heterogeneous population of galaxies and merger remnants from the TNG100 cosmological hydrodynamical simulation. We show that even idealized stellar kinematic data have limited utility compared to imaging and underperforms by $2.1 \\pm 0.5 {{\\ \\rm per\\ cent}}$ in completeness and $4.7 \\pm 0.4 {{\\ \\rm per\\ cent}}$ in purity for our fiducial model architecture. Combining imaging and stellar kinematics offers a small boost in completeness (by $1.8 \\pm 0.4 {{\\ \\rm per\\ cent}}$, compared to $92.7 \\pm 0.2 {{\\ \\rm per\\ cent}}$ from imaging alone) but no change in purity ($0.1\\pm 0.3 {{\\ \\rm per\\ cent}}$ improvement compared to $92.7 \\pm 0.2 {{\\ \\rm per\\ cent}}$, evaluated with equal numbers of merger remnant and non-remnant control galaxies). Classification accuracy of all models is particularly sensitive to physical companions at separations ≲ 40 kpc and to time-since-coalescence. Taken together, our results show that the stellar kinematic data have little to offer in compliment to imaging for merger remnant identification in a heterogeneous galaxy population.
Publisher: American Astronomical Society
Date: 04-2023
Abstract: The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i -band images of 3096 SDSS quasars and galaxies at 0.3 z 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control s le of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.
Publisher: Oxford University Press (OUP)
Date: 29-09-2023
Publisher: Oxford University Press (OUP)
Date: 10-01-2017
DOI: 10.1093/MNRAS/STX017
Publisher: American Astronomical Society
Date: 10-2022
Abstract: We use machine-learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction. The s le contains 4690 galaxies from the integral field spectroscopy survey SDSS-IV MaNGA and can be separated into 1060 merging galaxies and 3630 nonmerging or unclassified galaxies. For the merger s le, there are 468, 125, 293, and 174 galaxies (1) in the incoming pair phase, (2) in the first pericentric passage phase, (3) approaching or just passing the apocenter, and (4) in the final coalescence phase or post-mergers. With the information of projected separation, line-of-sight velocity difference, Sloan Digital Sky Survey (SDSS) gri images, and MaNGA H α velocity map, we are able to classify the mergers and their stages with good precision, which is the most important score to identify interacting galaxies. For the two-phase classification (binary nonmerger and merger), the performance can be high (precision 0.90) with LGBMClassifier . We find that s le size can be increased by rotation, so the five-phase classification (nonmerger, and merger stages 1, 2, 3, and 4) can also be good (precision 0.85). The most important features come from SDSS gri images. The contribution from the MaNGA H α velocity map, projected separation, and line-of-sight velocity difference can further improve the performance by 0%–20%. In other words, the image and the velocity information are sufficient to capture important features of galaxy interactions, and our results can apply to all the MaNGA data, as well as future all-sky surveys.
Publisher: Oxford University Press (OUP)
Date: 30-07-2021
Abstract: Quantitative morphologies, such as asymmetry and concentration, have long been used as an effective way to assess the distribution of galaxy starlight in large s les. Application of such quantitative indicators to other data products could provide a tool capable of capturing the two-dimensional distribution of a range of galactic properties, such as stellar mass or star-formation rate maps. In this work, we utilize galaxies from the Illustris and IllustrisTNG simulations to assess the applicability of concentration and asymmetry indicators to the stellar mass distribution in galaxies. Specifically, we test whether the intrinsic values of concentration and asymmetry (measured directly from the simulation stellar mass particle maps) are recovered after the application of measurement uncertainty and a point spread function (PSF). We find that random noise has a non-negligible systematic effect on asymmetry that scales inversely with signal-to-noise ratio (S/N), particularly at an S/N less than 100. We evaluate different methods to correct for the noise contribution to asymmetry at very low S/N, where previous studies have been unable to explore due to systematics. We present algebraic corrections for noise and resolution to recover the intrinsic morphology parameters. Using Illustris as a comparison data set, we evaluate the robustness of these fits in the presence of a different physics model, and confirm these correction methods can be applied to other data sets. Lastly, we provide estimations for the uncertainty on different correction methods at varying S/N and resolution regimes.
Publisher: Oxford University Press (OUP)
Date: 27-03-2019
DOI: 10.1093/MNRAS/STZ855
Location: Japan
Location: Australia
No related grants have been discovered for Connor Bottrell.