arXiv subjects

Robert W. Bickley

Publications and source records attributed to Robert W. Bickley.

13 recordsLinked to original sources

arXiv2026

A Comprehensive Analysis of WISE Mid-Infrared Colors for Obscured AGN Selection

In this paper, we investigate the robustness of WISE mid-IR color selection (W1-W2) for identifying obscured (Type 2) active galactic nuclei (AGNs) at low redshift (z<0.3), using a sample of ~360,000 SDSS galaxies classified via emission lines into Seyfert 2 (Sy2), LINER, and star-forming (BPT-SF) galaxies. We find that the K-correction is essential to remove non-AGN contamination, and once applied the simple W1-W2>0.5 selection emerges as optimal in terms of purity and completeness of AGN selection. However, we confirm that even this lenient cut selects only ~13% of Sy2 galaxies and that achieving W1-W2>0.5 requires AGN contributing >75% of the total infrared luminosity, which is uncommon. Although mid-IR-selected Sy2s tend to be luminous, the high [OIII] luminosity does not guarantee red W1-W2 (nor does any other tested global or NLR-scale parameter), suggesting the critical role of obscuration on smaller scales. <1% of BPT-SF systems (but making ~20% of all mid-IR selected galaxies) exhibit W1-W2>0.5 colors. Such colors cannot be reproduced by models of star-heated dust alone. Red BPT-SFs tend to have higher W4 luminosities than expected from SF, indicating true AGNs. Intriguingly, mid-IR AGNs in massive bulges ($M_{\mathrm{bulge}} \gtrsim 10^{10} M_{\odot}$) predominantly (84%) manifest themselves as BPT-AGNs, whereas those in low-mass bulges ($\lesssim 10^{10} M_{\odot}$) mostly (60%) manifest as BPT-SF. This BPT-AGN vs.\ BPT-SF dichotomy does not extend to total stellar mass. We conclude that although the mid-IR AGN selection is incomplete, its strength lies in identifying optically inconspicuous AGNs with low-mass bulges, regardless of the total mass.

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arXiv2026

Galaxy Mergers in UNIONS -- II: Predicting Timescales in the Post-Merger Regime

Galaxy mergers are critical events that influence galaxy evolution by driving processes such as enhanced star formation, quenching, and active galactic nucleus (AGN) activity. However, constraining the timescales over which these processes occur in the post-merger phase has remained a significant challenge. This study extends the MUlti-Model Merger Identifier (\textsc{Mummi}) framework to predict post-merger timescales ($T_{PM}$) for galaxies, leveraging machine learning models trained on realism-enhanced mock observations derived from the IllustrisTNG simulations. By classifying post-merger galaxies into four temporal bins spanning 0 to 1.76 Gyr after coalescence, \textsc{Mummi} achieves time classification accuracies exceeding 70 per cent. We apply this framework to the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), yielding a catalog of 8,716 post-merger galaxies with $T_{PM}$ predictions and stellar masses $\log(M_*/M_\odot) \geq 10$ at redshifts 0.03 < z < 0.3. These results provide a robust methodology to connect galaxy interaction timescales with physical processes, enabling detailed studies of galaxy evolution in the post-merger regime.

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arXiv2025

Unions with UNIONS: Using galaxy-galaxy lensing to probe galaxy mergers

We use galaxy-galaxy lensing to investigate how the dark matter (DM) haloes and stellar content of galaxies with $0.012 \leq z \leq 0.32$ and $10 \leq \log_{10}(M_\star/\mathrm{M}_\odot) \leq 12$ change as a result of the merger process. To this end, we construct two samples of galaxies obtained from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), comprising 1 623 post-mergers and $\sim$30 000 non-merging controls, that live in low-density environments to use as our lenses. These samples are weighted to share the same distributions of stellar mass, redshift, and geometric mean distance to a galaxy's three nearest neighbours to ensure differences in the lensing signal are due to the merger process itself. We do not detect a statistically significant difference in the excess surface density profile of post-mergers and non-merging controls with current data. Fitting haloes composed of a point-like stellar mass component and an extended DM structure described by a Navarro-Frenk-White profile to the lensing measurements yields, for both samples, halo masses of $M_\text{halo} \sim 4\times10^{12}\,\mathrm{M}_\odot$ and a moderately negative correlation between $M_\text{halo}$ and concentration $c$. This allows us to rule out, at the 95% confidence level, merger-induced starbursts in which more than 60% of the stellar mass is formed in the burst. The application of our methods to upcoming surveys that are able to provide samples $\sim$10$\times$ larger than our current catalogue are expected to detect the weak-lensing signatures of mergers and further constrain their properties.

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arXiv2024

The effect of image quality on galaxy merger identification with deep learning

Studies have shown that the morphologies of galaxies are substantially transformed following coalescence after a merger, but post-mergers are notoriously difficult to identify, especially in imaging that is shallow or low-resolution. We train convolutional neural networks (CNNs) to identify simulated post-merger galaxies in a range of image qualities, modelled after five real surveys: the Sloan Digital Sky Survey (SDSS), the Dark Energy Camera Legacy Survey (DECaLS), the Canada-France Imaging Survey (CFIS), the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), and the Legacy Survey of Space and Time (LSST). Holding constant all variables other than imaging quality, we present the performance of the CNNs on reserved test set data for each image quality. The success of CNNs on a given dataset is found to be sensitive to both imaging depth and resolution. We find that post-merger recovery generally increases with depth, but that limiting 5 sigma point-source depths in excess of ~25 mag, similar to what is achieved in CFIS, are only marginally beneficial. Finally, we present the results of a cross-survey inference experiment, and find that CNNs trained on a given image quality can sometimes be applied to different imaging data to good effect. The work presented here therefore represents a useful reference for the application of CNNs for merger searches in both current and future imaging surveys.

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arXiv2024

X-ray AGNs with SRG/eROSITA: Multi-wavelength observations reveal merger triggering and post-coalescence circumnuclear blowout

Major mergers between galaxies are predicted to fuel their central supermassive black holes (SMBHs), particularly after coalescence. However, determining the prevalence of active galactic nuclei (AGNs) in mergers remains a challenge, because AGN diagnostics are sensitive to details of the central structure (e.g., nuclear gas clouds, geometry and orientation of a dusty torus) that are partly decoupled from SMBH accretion. X-rays, expected to be ubiquitous among accreting systems, are detectable through non-Compton-thick screens of obscuring material, and thus offer the potential for a more complete assessment of AGNs in mergers. But, extant statistical X-ray studies of AGNs in mergers have been limited by either sparse, heterogeneous, or shallow on-sky coverage. We use new X-ray observations from the first SRG/eROSITA all-sky data release to characterize the incidence, luminosity, and observability of AGNs in mergers. Combining machine learning and visual classification, we identify 923 post-mergers in Dark Energy Camera Legacy Survey (DECaLS) imaging and select 4,565 interacting galaxy pairs (with separations <120 kpc and mass ratios within 1:10) from the Sloan Digital Sky Survey. We find that galaxies with X-ray AGNs are 2.0+/-0.24 times as likely to be identified as post-mergers compared to non-AGN controls, and that post-mergers are 1.8+/-0.1 times as likely to host an X-ray AGN as non-interacting controls. A multi-wavelength census of X-ray, optical, and mid-IR-selected AGNs suggests a picture wherein the underlying AGN fraction increases during pair-phase interactions, that galaxy pairs within ~20 kpc become heavily obscured, and that the obscuration often clears post-coalescence.

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arXiv2024

Galaxy Mergers in UNIONS -- I: A Simulation-driven Hybrid Deep Learning Ensemble for Pure Galaxy Merger Classification

Merging and interactions can radically transform galaxies. However, identifying these events based solely on structure is challenging as the status of observed mergers is not easily accessible. Fortunately, cosmological simulations are now able to produce more realistic galaxy morphologies, allowing us to directly trace galaxy transformation throughout the merger sequence. To advance the potential of observational analysis closer to what is possible in simulations, we introduce a supervised deep learning Convolutional Neural Network (CNN) and Vision Transformer (ViT) hybrid framework, Mummi (MUlti Model Merger Identifier). Mummi is trained on realism-added synthetic data from IllustrisTNG100-1, and is comprised of a multi-step ensemble of models to identify mergers and non-mergers, and to subsequently classify the mergers as interacting pairs or post-mergers. To train this ensemble of models, we generate a large imaging dataset of 6.4 million images targeting UNIONS with RealSimCFIS. We show that Mummi offers a significant improvement over many previous machine learning classifiers, achieving 95% pure classifications even at Gyr long timescales when using a jury-based decision making process, mitigating class imbalance issues that arise when identifying real galaxy mergers from $z=0$ to $0.3$. Additionally, we can divide the identified mergers into pairs and post-mergers at 96% success rate. We drastically decrease the false positive rate in galaxy merger samples by 75%. By applying Mummi to the UNIONS DR5-SDSS DR7 overlap, we report a catalog of 13,448 high confidence galaxy merger candidates. Finally, we demonstrate that Mummi produces powerful representations solely using supervised learning, which can be used to bridge galaxy morphologies in simulations and observations.

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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.

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arXiv2023

AGN in post-mergers from the Ultraviolet Near Infrared Optical Northern Survey

The kinematic disturbances associated with major galaxy mergers are known to produce gas inflows, which in turn may trigger accretion onto the supermassive black holes (SMBH) of the participant galaxies. While this effect has been studied in galaxy pairs, the frequency of active galactic nuclei (AGN) in fully coalesced post-merger systems is poorly constrained due to the limited size or impurity of extant post-merger samples. Previously, we combined convolutional neural network (CNN) predictions with visual classifications to identify a highly pure sample of 699 post-mergers in deep r-band imaging. In the work presented here, we quantify the frequency of AGN in this sample using three metrics: optical emission lines, mid-infrared (mid- IR) colour, and radio detection of low-excitation radio galaxies (LERGs). We also compare the frequency of AGN in post-mergers to that in a sample of spectroscopically identified galaxy pairs. We find that AGN identified by narrow-line optical emission and mid-IR colour have an increased incidence rate in post-mergers, with excesses of ~4 over mass- and redshift-matched controls. The optical and mid-IR AGN excesses in post-mergers exceed the values found for galaxy pairs, indicating that AGN activity in mergers peaks after coalescence. Conversely, we recover no significant excess of LERGs in post-mergers or pairs. Finally, we find that the [OIII] luminosity (a proxy for SMBH accretion rate) in post-mergers that host an optical AGN is ~0.3 dex higher on average than in non-interacting galaxies with an optical AGN, suggesting that mergers generate higher accretion rates than secular triggering mechanisms.

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arXiv2022

A New Physical Picture for AGNs Lacking Optical Emission Lines

In this work, we use ~500 low-redshift (z ~ 0.1) X-ray AGNs observed by XMM-Newton and SDSS to investigate the prevalence and nature of AGNs that apparently lack optical emission lines (``optically dull AGNs''). Although 1/4 of spectra appear absorption-line dominated in visual assessment, line extraction with robust continuum subtraction from the MPA/JHU catalog reveals usable [OIII] measurements in 98% of the sample, allowing us to study [OIII]-underluminous AGNs together with more typical AGNs in the context of the L$_{\mathrm{[OIII]}}$--L$_{X}$ relation. We find that ``optically dull AGNs'' do not constitute a distinct population of AGNs. Instead, they are the [OIII]-underluminous tail of a single, unimodal L$_{\mathrm{[OIII]}}$--L$_{X}$ relation that has substantial scatter (0.6 dex). We find the degree to which an AGN is underluminous in [OIII] correlates with the specific SFR or D$_{4000}$ index of the host, which are both linked to the molecular gas fraction. Thus the emerging physical picture for the large scatter seems to involve the gas content of the narrow-line region. We find no significant role for previously proposed scenarios for the presence of optically dull AGNs, such as host dilution or dust obscuration. Despite occasionally weak lines in SDSS spectra, >80% of X-ray AGNs are identified as such with the BPT diagram. >90% are classified as AGNs based only on [NII]/H$α$, providing more complete AGN samples when [OIII] or H$β$ are weak. X-ray AGNs with LINER spectra obey essentially the same \lxo\ relation as Seyfert 2s, suggesting their line emission is produced by AGN activity.

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arXiv2022

Galaxy mergers can rapidly shut down star formation

Galaxy mergers trigger both star formation and accretion onto the central supermassive black hole. As a result of subsequent energetic feedback processes, it has long been proposed that star formation may be promptly extinguished in galaxy merger remnants. However, this prediction of widespread, rapid quenching in late stage mergers has been recently called into question with modern simulations and has never been tested observationally. Here we perform the first empirical assessment of the long-predicted end phase in the merger sequence. Based on a sample of ~500 post-mergers identified from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), we show that the frequency of post-merger galaxies that have rapidly shutdown their star formation following a previous starburst is 30-60 times higher than expected from a control sample of non-merging galaxies. No such excess is found in a sample of close galaxy pairs, demonstrating that mergers can indeed lead to a rapid halt to star formation, but that this process only manifests after coalescence.

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arXiv2022

The merger fraction of post-starburst galaxies in UNIONS

Post-starburst (PSB) galaxies 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% to 42% depending on the merger identification method and details of the PSB sample selection. These merger fractions represent an excess of 3-46x relative to non-PSB control samples. 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% of recent post-mergers (<200 Myr) would not be detected. Thus, we cannot exclude the possibility that nearly all post-starburst galaxies have undergone a merger in their recent past.

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arXiv2022

Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)

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% in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the diversity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) which 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 (CFIS), which is part of the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). 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 sample. The SFR enhancement is even greater in the visually confirmed post-merger sample, a factor of two higher than the control sample.

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arXiv2021

Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples 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 percent, 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 samples identified by the CNN. Despite the CNN's good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ~6 percent 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 sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

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