arXiv subjects

Connor Bottrell

Publications and source records attributed to Connor Bottrell.

At least 37 records · Page 2Linked to original sources

arXiv2024

Rich and diverse molecular gas environments of closely-separated dual quasars viewed by ALMA

We present a study of the molecular gas in five closely-spaced ($R_{\perp}<20$ kpc) dual quasars ($L_{\rm bol}\gtrsim10^{44}~\mathrm{erg~s}^{-1}$) at redshifts $0.4<z<0.8$ with the Atacama Large Millimeter/submillimeter Array. The dual quasar phase represents a distinctive stage during the interaction between two galaxies for investigating quasar fueling and feedback effects on the gas reservoir. The dual quasars were selected from the Sloan Digital Sky Survey and Subaru/Hyper Suprime-Cam Subaru Strategic Program, with confirmatory spectroscopic validation. Based on the detection of the CO J=2--1 emission line with Band 4, we derived key properties including CO luminosities, line widths, and molecular gas masses for these systems. Among the ten quasars of the five pairs, eight have line detections exceeding $5σ$. The detected sources prominently harbor substantial molecular gas reservoirs, with molecular gas masses ($M_{\text{molgas}}$) between $10^{9.6-10.5}~\mathrm{M_{\odot}}$, and molecular gas-to-stellar mass ratios ($μ_{\text{molgas}}$) spanning $18-97\%$. The overall $μ_{\text{molgas}}$ of these dual quasars agrees with that of inactive star-forming main-sequence galaxies at comparable redshifts, indicating no clear evidence of quenching. However, intriguing features in each individual system show possible evidence of AGN feedback, matter transfer, and compaction processes.

astro-ph.GA↗

arXiv2024

Predicting the Non-Thermal Pressure in Galaxy Clusters

We investigate the relationship between a galaxy cluster's hydrostatic equilibrium state, the entropy profile, $K$, of the intracluster gas, and the system's non-thermal pressure (NTP), within an analytic model of cluster structures. When NTP is neglected from the cluster's hydrostatic state, we find that the gas' logarithmic entropy slope, $k\equiv \mathrm{d}\ln K/\mathrm{d}\ln r$, converges at large halocentric radius, $r$, to a value that is systematically higher than the value $k\simeq1.1$ that is found in observations and simulations. By applying a constraint on these `pristine equilibrium' slopes, $k_\mathrm{eq}$, we are able to predict the required NTP that must be introduced into the hydrostatic state of the cluster. We solve for the fraction, $\mathcal{F}\equiv p_\mathrm{nt}/p$, of NTP, $p_\mathrm{nt}$, to total pressure, $p$, of the cluster, and we find $\mathcal{F}(r)$ to be an increasing function of halocentric radius, $r$, that can be parameterised by its value in the cluster's core, $\mathcal{F}_0$, with this prediction able to be fit to the functional form proposed in numerical simulations. The minimum NTP fraction, as the solution with zero NTP in the core, $\mathcal{F}_0=0$, we find to be in excellent agreement with the mean NTP predicted in non-radiative simulations, beyond halocentric radii of $r\gtrsim0.7r_{500}$, and in tension with observational constraints derived at similar radii. For this minimum NTP profile, we predict $\mathcal{F}\simeq0.20$ at $r_{500}$, and $\mathcal{F}\simeq0.34$ at $2r_{500}$; this amount of NTP leads to a hydrostatic bias of $b\simeq0.12$ in the cluster mass $M_{500}$ when measured within $r_{500}$. Our results suggest that the NTP of galaxy clusters contributes a significant amount to their hydrostatic state near the virial radius, and must be accounted for when estimating the cluster's halo mass using hydrostatic equilibrium approaches.

astro-ph.GA↗

arXiv2024

ERGO-ML: Comparing IllustrisTNG and HSC galaxy images via contrastive learning

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the ERGO-ML series (Extracting Reality from Galaxy Observables with Machine Learning), we utilize contrastive learning to directly compare a large sample of simulated and observed galaxies based on their stellar-light images. This eliminates the need to specify summary statistics and allows to exploit the whole information content of the observations. We produce survey-realistic galaxy mock datasets resembling real Hyper Suprime-Cam (HSC) observations using the cosmological simulations TNG50 and TNG100. Our focus is on galaxies with stellar masses between $10^9$ and $10^{12} M_\odot$ at $z=0.1-0.4$. This allows us to evaluate the realism of the simulated TNG galaxies in comparison to actual HSC observations. We apply the self-supervised contrastive learning method NNCLR to the images from both simulated and observed datasets (g, r, i - bands). This results in a 256-dimensional representation space, encoding all relevant observable galaxy properties. Firstly, this allows us to identify simulated galaxies that closely resemble real ones by seeking similar images in this multi-dimensional space. Even more powerful, we quantify the alignment between the representations of these two image sets, finding that the majority ($\gtrsim 70$ per cent) of the TNG galaxies align well with observed HSC images. However, a subset of simulated galaxies with larger sizes, steeper Sersic profiles, smaller Sersic ellipticities, and larger asymmetries appears unrealistic. We also demonstrate the utility of our derived image representations by inferring properties of real HSC galaxies using simulated TNG galaxies as the ground truth.

astro-ph.GA↗

arXiv2024

Predicting the Scaling Relations between the Dark Matter Halo Mass and Observables from Generalised Profiles II: Intracluster Gas Emission

We investigate the connection between a cluster's structural configuration and observable measures of its gas emission that can be obtained in X-ray and Sunyaev-Zeldovich (SZ) surveys. We present an analytic model for the intracluster gas density profile: parameterised by the dark matter halo's inner logarithmic density slope, $α$, the concentration, $c$, the gas profile's inner logarithmic density slope, $\varepsilon$, the dilution, $d$, and the gas fraction, $η$, normalised to cosmological content. We predict four probes of the gas emission: the emission-weighted, $T_\mathrm{X}$, and mean gas mass-weighted, $T_\mathrm{m_g}$, temperatures, and the spherically, $Y_\mathrm{sph}$, and cylindrically, $Y_\mathrm{cyl}$, integrated Compton parameters. Over a parameter space of clusters, we constrain the X-ray temperature scaling relations, $M_{200} - T_\mathrm{X}$ and $M_{500} - T_\mathrm{X}$, within $57.3\%$ and $41.6\%$, and $M_{200} - T_\mathrm{m_g}$ and $M_{500} - T_\mathrm{m_g}$, within $25.7\%$ and $7.0\%$, all respectively. When excising the cluster's core, the $M_{200} - T_\mathrm{X}$ and $M_{500} - T_\mathrm{X}$ relations are further constrained, to within $31.3\%$ and $17.1\%$, respectively. Similarly, we constrain the SZ scaling relations, $M_{200} - Y_\mathrm{sph}$ and $M_{500} - Y_\mathrm{sph}$, within $31.1\%$ and $17.7\%$, and $M_{200} - Y_\mathrm{cyl}$ and $M_{500} - Y_\mathrm{cyl}$, within $25.2\%$ and $22.0\%$, all respectively. The temperature observable $T_\mathrm{m_g}$ places the strongest constraint on the halo mass, whilst $T_\mathrm{X}$ is more sensitive to the parameter space. The SZ constraints are sensitive to the gas fraction, whilst insensitive to the form of the gas profile itself. In all cases, the halo mass is recovered with an uncertainty that suggests the cluster's structural profiles only contribute a minor uncertainty in its scaling relations.

astro-ph.GA↗

arXiv2024

Predicting the Scaling Relations between the Dark Matter Halo Mass and Observables from Generalised Profiles I: Kinematic Tracers

We investigate the relationship between a dark matter halo's mass profile and measures of the velocity dispersion of kinematic tracers within its gravitational potential. By predicting the scaling relation of the halo mass with the aperture velocity dispersion, $M_\mathrm{vir} - σ_\mathrm{ap}$, we present the expected form and dependence of this halo mass tracer on physical parameters within our analytic halo model: parameterised by the halo's negative inner logarithmic density slope, $α$, its concentration parameter, $c$, and its velocity anisotropy parameter, $β$. For these idealised halos, we obtain a general solution to the Jeans equation, which is projected over the line of sight and averaged within an aperture to form the corresponding aperture velocity dispersion profile. Through dimensional analysis, the $M_\mathrm{vir} - σ_\mathrm{ap}$ scaling relation is devised explicitly in terms of analytical bounds for these aperture velocity dispersion profiles: allowing constraints to be placed on this relation for motivated parameter choices. We predict the $M_{200} - σ_\mathrm{ap}$ and $M_{500} - σ_\mathrm{ap}$ scaling relations, each with an uncertainty of $60.5\%$ and $56.2\%$, respectively. These halo mass estimates are found to be weakly sensitive to the halo's concentration and mass scale, and most sensitive to the size of the aperture radius in which the aperture velocity dispersion is measured, the maximum value for the halo's inner slope, and the minimum and maximum values of the velocity anisotropy. Our results show that a halo's structural and kinematic profiles impose only a minor uncertainty in estimating its mass. Consequently, spectroscopic surveys aimed at constraining the halo mass using kinematic tracers can focus on characterising other, more complex sources of uncertainty and observational systematics.

astro-ph.GA↗

arXiv2024

The limitations (and potential) of non-parametric morphology statistics for post-merger identification

Non-parametric morphology statistics have been used for decades to classify galaxies into morphological types and identify mergers in an automated way. In this work, we assess how reliably we can identify galaxy post-mergers with non-parametric morphology statistics. Low-redshift (z<0.2), recent (t_post-merger < 200 Myr), and isolated (r > 100 kpc) post-merger galaxies are drawn from the IllustrisTNG100-1 cosmological simulation. Synthetic r-band images of the mergers are generated with SKIRT9 and degraded to various image qualities, adding observational effects such as sky noise and atmospheric blurring. We find that even in perfect quality imaging, the individual non-parametric morphology statistics fail to recover more than 55% of the post-mergers, and that this number decreases precipitously with worsening image qualities. The realistic distributions of galaxy properties in IllustrisTNG allow us to show that merger samples assembled using individual morphology statistics are biased towards low mass, high gas fraction, and high mass ratio. However, combining all of the morphology statistics together using either a linear discriminant analysis or random forest algorithm increases the completeness and purity of the identified merger samples and mitigates bias with various galaxy properties. For example, we show that in imaging similar to that of the 10-year depth of the Legacy Survey of Space and Time (LSST), a random forest can identify 89% of mergers with a false positive rate of 17%. Finally, we conduct a detailed study of the effect of viewing angle on merger observability and find that there may be an upper limit to merger recovery due to the orientation of merger features with respect to the observer.

astro-ph.GA↗

arXiv2024

The Spatial Distribution of Type Ia Supernovae within Host Galaxies

We study how type Ia supernovae (SNe Ia) are spatially distributed within their host galaxies, using data taken from the Sloan Digital Sky Survey (SDSS). This paper specifically tests the hypothesis that the SNe Ia rate traces the r-band light of the morphological component to which supernovae belong. A sample of supernovae is taken from the SDSS SN Survey, and host galaxies are identified. Each host galaxy is decomposed into a bulge and disk, and the distribution of supernovae is compared to the distribution of disk and bulge light. Our methodology is relatively unaffected by seeing. We find that in disk light dominated galaxies, SNe Ia trace light closely. The situation is less clear for bulges and ellipticals because of resolution effects, but the available evidence is also consistent with the hypothesis that bulge/elliptical SNe Ia follow light.

astro-ph.GA↗

arXiv2023

A rest-frame near-IR study of clumps in galaxies at 1 < z < 2 using JWST/NIRCam: connection to galaxy bulges

A key question in galaxy evolution has been the importance of the apparent `clumpiness' of high redshift galaxies. Until now, this property has been primarily investigated in rest-frame UV, limiting our understanding of their relevance. Are they short-lived or are associated with more long-lived massive structures that are part of the underlying stellar disks? We use JWST/NIRCam imaging from CEERS to explore the connection between the presence of these `clumps' in a galaxy and its overall stellar morphology, in a mass-complete ($log\,M_{*}/M_{\odot} > 10.0$) sample of galaxies at $1.0 < z < 2.0$. Exploiting the uninterrupted access to rest-frame optical and near-IR light, we simultaneously map the clumps in galactic disks across our wavelength coverage, along with measuring the distribution of stars among their bulges and disks. Firstly, we find that the clumps are not limited to rest-frame UV and optical, but are also apparent in near-IR with $\sim 60\,\%$ spatial overlap. This rest-frame near-IR detection indicates that clumps would also feature in the stellar-mass distribution of the galaxy. A secondary consequence is that these will hence be expected to increase the dynamical friction within galactic disks leading to gas inflow. We find a strong negative correlation between how clumpy a galaxy is and strength of the bulge. This firmly suggests an evolutionary connection, either through clumps driving bulge growth, or the bulge stabilizing the galaxy against clump formation, or a combination of the two. Finally, we find evidence of this correlation differing from rest-frame optical to near-IR, which could suggest a combination of varying formation modes for the clumps.

astro-ph.GA↗

arXiv2023

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

astro-ph.IM↗

arXiv2023

IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation

At fixed galaxy stellar mass, there is a clear observational connection between structural asymmetry and offset from the star forming main sequence, $Δ$SFMS. Herein, we use the TNG50 simulation to investigate the relative roles of major mergers (stellar mass ratios $μ\geq0.25$), minor ($0.1 \leq μ< 0.25$), and mini mergers ($0.01 \leq μ< 0.1$) in driving this connection amongst star forming galaxies (SFGs). We use dust radiative transfer post-processing with SKIRT to make a large, public collection of synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images of simulated TNG galaxies over $0.1\leq z \leq 0.7$ with $\log M_{\star} / \mathrm{M}_{\odot}\geq9$ ($\sim750$k images). Using their instantaneous SFRs, known merger histories/forecasts, and HSC-SSP asymmetries, we show (1) that TNG50 SFGs qualitatively reproduce the observed trend between $Δ$SFMS and asymmetry and (2) a strikingly similar trend emerges between $Δ$SFMS and the time-to-coalescence for mini mergers. Controlling for redshift, stellar mass, environment, and gas fraction, we show that individual mini merger events yield small enhancements in SFRs and asymmetries that are sustained on long timescales (at least $\sim3$ Gyr after coalescence, on average) -- in contrast to major/minor merger remnants which peak at much greater amplitudes but are consistent with controls only $\sim1$ Gyr after coalescence. Integrating the boosts in SFRs and asymmetries driven by $μ\geq0.01$ mergers since $z=0.7$ in TNG50 SFGs, we show that mini mergers are responsible for (i) $55$ per cent of all merger-driven star formation and (ii) $70$ per cent of merger-driven asymmetric structure. Due to their relative frequency and prolonged boost timescales, mini mergers dominate over their minor and major counterparts in driving star formation and asymmetry in SFGs.

astro-ph.GA↗

arXiv2023

${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ I: Neural Inference of Halo Mass from Galaxy Photometry and Morphology

We present ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$, a new machine learning approach for inferring the mass of host dark matter halos, $M_h$, from the photometry and morphology of galaxies. ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. (2023; arXiv:2308.14793) that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program (HSC-SSP) observations. We design ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ to infer $M_h$ and stellar mass, $M_*$, using $grizy$ band magnitudes, morphological properties quantifying characteristic size, concentration, and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ infers accurate and unbiased posteriors of $M_h$. Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining $M_h$. With magnitudes alone, we infer $M_h$ with $σ_{\log M_h} \sim 0.115$ and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of $M_h$ constraints, as does total satellite luminosity: $σ_{\log M_h} \sim 0.095$ and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve $M_h$ constraints by $\sim$40\%. In subsequent papers, we will validate and calibrate ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ with galaxy-galaxy lensing measurements on real observational data.

astro-ph.GA↗

arXiv2023

Galaxy mergers in Subaru HSC-SSP: a deep representation learning approach for identification and the role of environment on merger incidence

We take a deep learning-based approach for galaxy merger identification in Subaru HSC-SSP, specifically through the use of deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the HSC-SSP survey. We can use this merger sample to conduct studies on how mergers affect galaxy evolution. We use Zoobot, a deep learning representation learning model pre-trained on citizen science votes on Galaxy Zoo DeCALS images. We fine-tune Zoobot for the purpose of merger classification of images of SDSS and GAMA galaxies in HSC-SSP PDR 3. Fine-tuning is done using 1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then find merger probabilities on observed HSC images using the fine-tuned model. Using our merger probabilities, we examine the relationship between merger activity and environment. We find that our fine-tuned model returns an accuracy on the synthetic validation data of 76%. This number is comparable to those of previous studies where convolutional neural networks were trained with simulation images, but with our work requiring a far smaller number of training samples. For our synthetic data, our model is able to achieve completeness and precision values of 80%. In addition, our model is able to correctly classify both mergers and non-mergers of diverse morphologies and structures, including those at various stages and mass ratios, while distinguishing between projections and merger pairs. For the relation between galaxy mergers and environment, we find two distinct trends. Using stellar mass overdensity estimates for TNG simulations and observations using SDSS and GAMA, we find that galaxies with higher merger scores favor lower density environments on scales of 0.5 to 8 h^-1 Mpc. However, below these scales in the simulations, we find that galaxies with higher merger scores favor higher density environments.

astro-ph.GA↗

arXiv2023

GALAXY CRUISE: Deep Insights into Interacting Galaxies in the Local Universe

We present the first results from GALAXY CRUISE, a community (or citizen) science project based on data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The current paradigm of galaxy evolution suggests that galaxies grow hierarchically via mergers, but our observational understanding of the role of mergers is still limited. The data from HSC-SSP are ideally suited to improve our understanding with improved identifications of interacting galaxies thanks to the superb depth and image quality of HSC-SSP. We have launched a community science project, GALAXY CRUISE, in 2019 and collected over 2 million independent classifications of 20,686 galaxies at z < 0.2. We first characterize the accuracy of the participants' classifications and demonstrate that it surpasses previous studies based on shallower imaging data. We then investigate various aspects of interacting galaxies in detail. We show that there is a clear sign of enhanced activities of super massive black holes and star formation in interacting galaxies compared to those in isolated galaxies. The enhancement seems particularly strong for galaxies undergoing violent merger. We also show that the mass growth rate inferred from our results is roughly consistent with the observed evolution of the stellar mass function. The 2nd season of GALAXY CRUISE is currently under way and we conclude with future prospects. We make the morphological classification catalog used in this paper publicly available at the GALAXY CRUISE website, which will be particularly useful for machine-learning applications.

astro-ph.GA↗

arXiv2023

Detection of stellar light from quasar host galaxies at redshifts above 6

The detection of starlight from the host galaxies of quasars during the reionization epoch ($z>6$) has been elusive, even with deep HST observations. The current highest redshift quasar host detected, at $z=4.5$, required the magnifying effect of a foreground lensing galaxy. Low-luminosity quasars from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) mitigate the challenge of detecting their underlying, previously-undetected host galaxies. Here we report rest-frame optical images and spectroscopy of two HSC-SSP quasars at $z>6$ with JWST. Using NIRCam imaging at 3.6$μ$m and 1.5$μ$m and subtracting the light from the unresolved quasars, we find that the host galaxies are massive (stellar masses of $13\times$ and $3.4\times$ $10^{10}$ M$_{\odot}$, respectively), compact, and disk-like. NIRSpec medium-resolution spectroscopy shows stellar absorption lines in the more massive quasar, confirming the detection of the host. Velocity-broadened gas in the vicinity of these quasars enables measurements of their black hole masses ($1.4\times 10^9$ and $2.0\times$ $10^{8}$ M$_{\odot}$, respectively). Their location in the black hole mass - stellar mass plane is consistent with the distribution at low redshift, suggesting that the relation between black holes and their host galaxies was already in place less than a billion years after the Big Bang.

astro-ph.GA↗

arXiv2023

A machine learning approach to assessing the presence of substructure in quasar host galaxies using the Hyper Suprime-Cam Subaru Strategic Program

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 possess 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 which 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 sample 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 accurate characterization of the point spread function (PSF) thus demonstrating future science to come from the Rubin Observatory.

astro-ph.GA↗

arXiv2023

Morphological asymmetries of quasar host galaxies with Subaru Hyper Suprime-Cam

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 $0.2 45$). This then manifests itself into quasar hosts being more asymmetric, on average, when they harbor a more massive and highly accreting black hole. The merger fraction also positively correlates with $L_{\rm bol}$ and reaches up to 35\% for the most luminous. Compared to control galaxies, quasar hosts are marginally more asymmetric (excess of 0.017 in median at 9.4$σ$ level) and the merger fractions are similar ($\sim 16.5\%$). We quantify the dependence of asymmetry on optical band which demonstrates that mergers are more likely to be identified with the bluer bands and the correlation between $L_{\rm bol}$ 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.

astro-ph.GA↗

arXiv2023

Molecular gas and star formation in nearby starburst galaxy mergers

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 ($Σ_{\mathrm{mol}}$) 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 factor of 5 - 10 in both $Σ_{\mathrm{mol}}$ and $σ_v$, which puts these pixels above the trend of PHANGS galaxies in the $σ_v$ vs $Σ_{\mathrm{mol}}$ diagram. This deviation may indicate that GMCs in the simulated mergers are much less gravitationally bound compared with simulated control galaxies with virial parameter ($α_{\mathrm{vir}}$) reaching 10 - 100. Furthermore, we find that the increase in $α_{\mathrm{vir}}$ happens at the same time as the increase in global star formation rate (SFR), which suggests stellar feedback is responsible for dispersing the gas. We also find that the gas depletion time is significantly lower for high $α_{\mathrm{vir}}$ GMCs during a starburst event. This is in contrast to the simple physical picture that low $α_{\mathrm{vir}}$ 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.

astro-ph.GA↗

arXiv2022

SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning

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 sample contains 4,690 galaxies from the integral field spectroscopy survey SDSS-IV MaNGA, and can be separated to 1,060 merging galaxies and 3630 non-merging or unclassified galaxies. For the merger sample, there are 468, 125, 293, and 174 galaxies in (1) incoming pair phase, (2) first pericentric passage phase, (3) aproaching or just passing the apocenter, and (4) final coalescence phase or post-mergers. With the information of projected separation, line-of-sight velocity difference, SDSS gri images, and MaNGA Ha 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 2-phase classification (binary; non-merger and merger), the performance can be high (precision>0.90) with LGBMClassifier. We find that sample size can be increased by rotation, so the 5-phase classification (non-merger, 1, 2, 3, and 4 merger stages) can be also good (precision>0.85). The most important features come from SDSS gri images. The contribution from MaNGA Ha 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 the entire MaNGA data as well as future all-sky surveys.

astro-ph.GA↗