arXiv subjects

V. Rodriguez-Gomez

Publications and source records attributed to V. Rodriguez-Gomez.

16 recordsLinked to original sources

arXiv2026

The major merger-active galactic nucleus connection up to the cosmic noon

Galaxy major mergers are a potential mechanism for triggering active galactic nuclei (AGN) activity, but their role remains debated, particularly beyond the local Universe. We aim to shed light on the merger-AGN connection at $z=0.5$-$2$, exploiting the multi-wavelength datasets and {\it James Webb Space Telescope} (JWST) observations in the COSMOS field. We construct a stellar mass-limited sample and identify AGN via mid-infrared (MIR) colours, X-ray detections, and spectral energy distribution (SED) fitting. We train convolutional neural networks to identify mergers with mock JWST observations. We create non-AGN and non-merger control samples matching the redshift, stellar mass, and star-formation rate distributions of the AGN and mergers. We find AGN to be moderately more frequent in mergers than in non-mergers, with excess ratios ranging from $\sim2.5$ (X-ray AGN) to $\sim1.3$ (MIR) and $\sim 1.1$-1.2 (SED AGN). Similarly, AGN galaxies show a higher merger fraction ($f_{merg}$) than non-AGN controls. We then study $f_{merg}$ as a function of relative and absolute AGN power, utilising the AGN fraction ($f_{AGN}$) and accretion disc luminosity (L$_{disc}$) parameters. We uncover a $f_{merg}$-$f_{AGN}$ relation with two regimes: $f_{merg}$ stays roughly flat for less-dominant AGN ($f_{AGN}<0.8$) but increases at $f_{AGN}>0.8$ for the MIR and X-ray AGN, and more gently for SED AGN, where mergers appear to be the main triggering mechanism. Additionally, $f_{merg}$ increases monotonically as a function of L$_{disc}$, for all AGN types, reaching $f_{merg}>50\%$ for the most luminous AGN (L$_{disc} \gtrsim 10^{46}\,{erg\,s^{-1}}$). Overall, our results suggest that major mergers can trigger AGN out to the cosmic noon at $z\sim2$. Furthermore, the role of major mergers shows a clear dependence on AGN luminosity and remains the principal mechanism for fuelling the most powerful AGN.

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arXiv2025

Observationally derived change in the star formation rate as mergers progress

Galaxy mergers can change the rate at which stars are formed. We can trace when these changes occur in simulations of galaxy mergers. However, for observed galaxies we do not know how the star formation rate (SFR) evolves along the merger sequence as it is difficult to probe the time before or after coalescence. We aim to derive how SFR changes in observed mergers throughout the merger sequence, from a statistical perspective. Merger times were estimated for observed galaxy mergers in the Kilo Degree Survey (KiDS) using a convolutional neural network (CNN). The CNN was trained on mock KiDS images created using IllustrisTNG data. The SFRs were derived from spectral energy density fitting to KiDS and VIKINGs data. To determine the change in SFR for the merging galaxies, each merging galaxy was matched and compared to ten comparable non-merging galaxies; matching each galaxy in redshift, stellar mass, and local density. Mergers see an increase in the SFR for galaxies from 300~Myr before the merger until coalescence, continuing until at least 200~Myr after the merger event. After this, there is a possibility that SFR activity in the mergers begins to decrease, but we need more data to better constrain our merger times and SFRs to confirm this. We find that more galaxies with higher stellar mass (M$_{\star}$) have greater SFR enhancement as they merge compared to lower-M$_{\star}$ galaxies. There is no clear trend of changing SFR enhancement as local density changes, but the least dense environments have the least SFR enhancement. The increasing SFR enhancement is likely due to the closer proximity of galaxies and the presence of more close passes as the time before the merger approaches 0~Myr, with the SFR slowing 200~Myr after the merger event.

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arXiv2025

Euclid Quick Data Release (Q1). First Euclid statistical study of the active galactic nuclei contribution fraction

Active galactic nuclei (AGN) play a key role in galaxy evolution but are challenging to identify due to their varied observational signatures. Furthermore, understanding their impact requires quantifying their strength relative to their host galaxies. We developed a deep learning (DL) model for identifying AGN in imaging data by deriving the contribution of the central point source. Trained on Euclidised mock galaxy images with injected AGN levels, in the form of varying contributions of the point-spread function (PSF), our model can precisely and accurately recover the injected AGN contribution fraction $f_{\rm PSF}$, with a mean difference between the predicted and true $f_{\rm PSF}$ of $-0.0078$ and an overall root mean square error (RMSE) of 0.051. This method moves beyond binary AGN classification, enabling precise AGN contribution measurements. Applying our model to a stellar-mass-limited sample ($M_{\ast} \ge 10^{9.8} M_{\odot}$, $0.5 \le z \le 2.0$) from the first \Euclid quick data release (Q1), we identify $48,840 \pm 78$ AGN over 63.1 deg$^2$ ($7.8\pm0.1$%) using a threshold of $f_{\rm PSF} > 0.2$. We compare our DL-selected AGN with those identified in X-ray, mid-infrared (MIR), and optical spectroscopy and investigate their overlapping fractions depending on different thresholds on the PSF contribution. We find that the overlap increases with increasing X-ray or bolometric AGN luminosity. The AGN luminosity in the $I_{\rm E}$ filter correlates with host galaxy stellar mass, suggesting faster supermassive black hole (SMBH) growth in more massive galaxies. Moreover, the mean relative contribution of the AGN is higher in quiescent galaxies than in star-forming ones. Starburst galaxies and the most massive galaxies (across the star-formation main sequence) tend to host the most luminous AGN, indicating concomitant assembly of the SMBH and the host galaxy.

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arXiv2025

Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei

Galaxy major mergers are a key pathway to trigger AGN. We present the first detection of major mergers in the Euclid Deep Fields and analyse their connection with AGN. We constructed a stellar-mass-complete ($M_*>10^{9.8}\,M_{\odot}$) sample of galaxies from the first quick data release (Q1), in the redshift range z=0.5-2. We selected AGN using X-ray data, optical spectroscopy, mid-infrared colours, and processing \IE observations with an image decomposition algorithm. We used CNNs trained on cosmological simulations to classify galaxies as mergers and non-mergers. We found a larger fraction of AGN in mergers compared to the non-merger controls for all AGN selections, with AGN excess factors ranging from 2 to 6. Likewise, a generally larger merger fraction ($f_{merg}$) is seen in active galaxies than in the non-active controls. We analysed $f_{merg}$ as a function of the AGN bolometric luminosity ($L_{bol}$) and the contribution of the point-source to the total galaxy light in the \IE-band ($f_{PSF}$) as a proxy for the relative AGN contribution fraction. We uncovered a rising $f_{merg}$, with increasing $f_{PSF}$ up to $f_{PSF}=0.55$, after which we observed a decreasing trend. We then derived the point-source luminosity ($L_{PSF}$) and showed that $f_{merg}$ monotonically increases as a function of $L_{PSF}$ at z<0.9, with $f_{merg}>$50% for $L_{PSF}>2\,10^{43}$ erg/s. At z>0.9, $f_{merg}$ rises as a function of $L_{PSF}$, though mergers do not dominate until $L_{PSF}=10^{45}$ erg/s. For X-ray and spectroscopic AGN, we computed $L_{bol}$, which has a positive correlation with $f_{merg}$ for X-ray AGN, while shows a less pronounced trend for spectroscopic AGN due to the smaller sample size. At $L_{bol}>10^{45}$ erg/s, AGN mostly reside in mergers. We concluded that mergers are strongly linked to the most powerful, dust-obscured AGN, associated with rapid supermassive black hole growth.

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arXiv2025

Galaxy mergers classification using CNNs trained on S\'ersic models, residuals and raw images

Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods such as Convolutional Neural Networks (CNNs) are essential for efficient detection. It is understood that CNNs classify mergers by identifying deviations from the regular, expected shapes of galaxies, particularly faint features that are indicative of a merger event. In this work, we present a novel investigation of the relative importance of different morphological components, namely faint residual features and position and spatial structure, in CNN-based binary classification of galaxies into merger and non-merger classes. Using mock images from the IllustrisTNG simulations processed to mimic Hyper Suprime-Cam (HSC) observations, we fit S\'ersic profiles to each galaxy and generate three datasets: original images, model images containing only smooth S\'ersic profiles, and residual images highlighting faint features after model subtraction. We train three identical CNNs on these datasets: CNN1 on original images, CNN2 on model images, and CNN3 on residual images. CNN1, trained on full images, achieves the highest accuracy of 74 percent. CNN2, using only shape information including source position, achieves 70 percent, while CNN3, using only faint residual features, achieves 68 percent. We find that galaxy merger classification is possible using either faint features or the position and S\'ersic profile information present in residual and model images, respectively. Our results demonstrate that not only faint features but also source position information play complementary roles in merger classification. This has important implications for the design and interpretation of machine learning methods for galaxy morphology, particularly in regimes where specific image components may be enhanced or suppressed.

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arXiv2024

Dust and Power: Unravelling the merger - active galactic nucleus connection in the second half of cosmic history

Galaxy mergers represent a fundamental physical process under hierarchical structure formation, but their role in triggering AGNs is still unclear. We aim to investigate the merger-AGN connection using state-of-the-art observations and novel methods in detecting mergers and AGNs. We selected stellar mass-limited samples at redshift z<1 from KiDS, focusing on the KiDS-N-W2 field with a wide range of multi-wavelength data. Three AGN types, selected in the MIR, X-ray, and via SED modelling, were analysed. To identify mergers, we used convolutional neural networks trained on two cosmological simulations. We created mass and redshift-matched control samples of non-mergers and non-AGNs. We observe a clear AGN excess (a factor of 2-3) in mergers with respect to non-mergers for the MIR AGNs, and a mild excess for the X-ray and SED AGNs, indicating that mergers could trigger all 3 types but are more connected with the MIR AGNs. About half of the MIR AGNs are in mergers but it is unclear whether mergers are the main trigger. For the X-ray and SED AGNs, mergers are unlikely to be the dominant trigger. We also explore the relation using the continuous AGN fraction $f_{AGN}$ parameter. Mergers exhibit a clear excess of high $f_{AGN}$ values relative to non-mergers, for all AGNs. We unveil the first merger fraction $f_{merg}-f_{AGN}$ relation with two distinct regimes. When the AGN is not dominant, the relation is only mildly increasing or even flat, with the MIR AGNs showing the highest $f_{merg}$. In the regime of very dominant AGNs ($f_{AGN}\geq0.8$), $f_{merg}$ shows a steeply rising trend with increasing $f_{AGN}$ for all AGN types. These trends are also seen when plotted against AGN bolometric luminosity. We conclude that mergers are most connected with dust-obscured AGNs (linked to a fast-growing phase of the SMBH) and are the main or even the sole fuelling mechanism of the most powerful AGNs.

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arXiv2024

Determining the time before or after a galaxy merger event

Aims. This work aims to reproduce the time before or after a merger event of merging galaxies from the IllustrisTNG cosmological simulation using machine learning. Methods. Images of merging galaxies were created in the u, g, r, and i bands from IllustrisTNG. The merger times were determined using the time difference between the last simulation snapshot where the merging galaxies were tracked as two galaxies and the first snapshot where the merging galaxies were tracked as a single galaxy. This time was then further refined using simple gravity simulations. These data were then used to train a residual network (ResNet50), a Swin Transformer (Swin), a convolutional neural network (CNN), and an autoencoder (using a single latent neuron) to reproduce the merger time. The full latent space of the autoencoder was also studied to see if it reproduces the merger time better than the other methods. This was done by reducing the latent space dimensions using Isomap, linear discriminant analysis (LDA), neighbourhood components analysis, sparse random projection, truncated singular value decomposition and uniform manifold approximation and projection. Results. The CNN is the best of all the neural networks. The performance of the autoencoder was close to the CNN, with Swin close behind the autoencoder. ResNet50 performed the worst. The LDA dimensionality reduction performed the best of the six methods used. The exploration of the full latent space produced worse results than the single latent neuron of the autoencoder. For the test data set, we found a median error of 190 Myr, comparable to the time separation between snapshots in IllustrisTNG. Galaxies more than $\approx$ 625 Myr before a merger have poorly recovered merger times, as well as galaxies more than $\approx$ 125 Myr after a merger event.

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arXiv2024

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1<z<0.3$, precision and recall range between $\sim$70\% and 80\%, both of which decrease with increasing $z$ as expected (by $\sim$5\% for precision and $\sim$10\% for recall at $0.76<z<1.0$). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by $\sim$20-40\% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, all methods agree well with visual labels of clear mergers but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the multi-class classification task to distinguish pre-, post- and non-mergers, none of the methods offer a good performance, which could be partly due to limitations in resolution and depth of the data. With the advent of better quality data (e.g. JWST and Euclid), it is important to improve our ability to detect mergers and distinguish between merger stages.

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arXiv2022

North Ecliptic Pole merging galaxy catalogue

We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg. North Ecliptic Pole over the redshift range $0.0 < z < 0.3$. To do this, imaging data from the Hyper Suprime-Cam are used along with morphological parameters derived from these same data. The catalogue was generated using a hybrid approach. Two neural networks were trained to perform binary merger non-merger classifications: one for galaxies with $z < 0.15$ and another for $0.15 \leq z < 0.30$. Each network used the image and morphological parameters of a galaxy as input. The galaxies that were identified as merger candidates by the network were then visually checked by experts. The resulting mergers will be used to calculate the merger fraction as a function of redshift and compared with literature results. We found that 86.3% of galaxy mergers at $z < 0.15$ and 79.0% of mergers at $0.15 \leq z < 0.30$ are expected to be correctly identified by the networks. Of the 34 264 galaxies classified by the neural networks, 10 195 were found to be merger candidates. Of these, 2109 were visually identified to be merging galaxies. We find that the merger fraction increases with redshift, consistent with literature results from observations and simulations, and that there is a mild star-formation rate enhancement in the merger population of a factor of $1.102 \pm 0.084$.

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arXiv2022

The Fornax3D project: The environmental impact on gas metallicity gradients in Fornax cluster galaxies

The role played by environment in galaxy evolution is a current debate in astronomy. The degree to which environment can alter, re-shape, or drive galaxy evolution is a topic of discussion in both fronts, observations and simulations. This paper analyses the gas metallicity gradients for a sample of 10 Fornax cluster galaxies observed with MUSE as part of the Fornax3D project. Detailed maps of emission lines allowed a precise determination of gas metallicity and metallicity gradients. The integrated gas metallicity of our Fornax cluster galaxies show slightly higher metallicities (~0.045 dex) in comparison to a control sample. In addition, we find signs of a mass and metallicity segregation from the center to the outskirts of the cluster. By comparing our Fornax cluster metallicity gradients with a control sample we find a general median offset of ~0.04 dex/Re, with 8 of our galaxies showing flatter or more positive gradients. We find no systematic difference between the gradients of recent and intermediate infallers when considering the projected distance of each galaxy to the cluster center. To identify the origin of the observed offset in the metallicity gradients, we perform a similar analysis with data from the TNG50 simulation. We identify 12 subhalos in Fornax-like clusters and compare their metallicity gradients with a control sample of field subhalos. This exercise also shows a flattening in the metallicity gradients for galaxies in Fornax-like halos, with a median offset of ~0.05 dex/Re We also analyse the merger history, Mach numbers (M), and ram pressure stripping of our TNG50 sample. We conclude that the observed flattening in metallicity gradients is likely due to a combination of galaxies traveling at supersonic velocities (M>1) that are experiencing high ram pressure stripping and flybys.

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arXiv2021

The building up of observed stellar scaling relations of massive galaxies and the connection to black hole growth in the TNG50 simulation

[abridged] We study how mock-observed stellar morphological and structural properties of massive galaxies are built up between $z=0.5$ and $z=3$ in the TNG50 cosmological simulation. We generate mock images with the properties of the CANDELS survey and derive Sersic parameters and optical rest-frame morphologies as usually done in the observations. Overall, the simulation reproduces the observed evolution of the abundances of different galaxy morphological types of star-forming and quiescent galaxies. The $\log{M_*}-\log R_e$ and $\log{M_*}-\logΣ_1$ relations of the simulated star-forming and quenched galaxies also match the observed slopes and zeropoints to within 1-$σ$. In the simulation, galaxies increase their observed central stellar mass density ($Σ_1$) and transform in morphology from irregular/clumpy systems to normal Hubble-type systems in the Star Formation Main Sequence at a characteristic stellar mass of $\sim 10^{10.5}~M_\odot$. This morphological transformation is connected to the activity of the central Super Massive Black Holes (SMBHs). At low stellar masses ($10^9$ < $M_*/M_\odot$ < $10^{10}$) SMBHs grow rapidly, while at higher mass SMBHs switch into the kinetic feedback mode and grow more slowly. During this low-accretion phase, SMBH feedback leads to the quenching of star-formation, along with a simultaneous growth in $Σ_1$. More compact massive galaxies grow their SMBHs faster than extended ones of the same mass and end up quenching earlier. In the TNG50 simulation, SMBHs predominantly grow via gas accretion before galaxies quench, and $Σ_1$ increases substantially after SMBH growth slows down. The simulation predicts therefore that quiescent galaxies have higher $Σ_1$ values than star-forming galaxies for the same SMBH mass, which disagrees with alternative models, and may potentially be in tension with some observations.

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arXiv2021

The stellar halos of ETGs in the IllustrisTNG simulations: II. Accretion, merger history, and dark halo connection

Stellar halos in early-type galaxies (ETGs) are shaped by their accretion and merger histories. We use a sample of 1114 ETGs in the TNG100 simulation with stellar masses $10^{10.3}\leq M_{*}/M_\odot\leq 10^{12}$, selected at z=0 within the range of g-r colour and lambda-ellipticity diagram populated by observed ETGs. We study how the rotational support and intrinsic shapes of the stellar halos depend on the fraction of accreted stars, overall and separately by major, minor, and mini mergers. Accretion histories in TNG100 ETGs as well as the radial distributions of ex-situ stars $f_{ex}(R)$ strongly correlate with stellar mass. Low-mass ETGs have characteristic peaked rotation profiles and near-oblate shapes with rounder halos that are completely driven by the in-situ stars. At high $f_{ex}$ major mergers decrease the in-situ peak in rotation velocity, flatten the $V_{*}/σ_{*}(R)$ profiles, and increase the triaxiality of the stellar halos. Kinematic transition radii do not trace the transition between in-situ and ex-situ dominated regions, but for systems with $M_{*}>10^{10.6}M_\odot$ the local rotational support decreases with the local ex-situ fraction $f_{ex}(R)$ and triaxiality increases with $f_{ex}$. These correlations are followed by fast and slow rotators alike with a continuous and overlapping sequence of properties. Merger events dynamically couple stars and dark matter: in high mass ETGs and at large radii where $f_{ex}\gtrsim0.5$, both components tend to have similar intrinsic shapes and rotational support, and nearly aligned principal axes and spin directions. Based on these results we suggest that extended photometry and kinematics of massive ETGs ($M_{*}>10^{10.6}M_\odot$) can be used to estimate the local fraction of ex-situ stars and to approximate the intrinsic shapes and rotational support of the co-spatial dark matter component. [abridged]

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arXiv2020

Towards a consistent framework of comparing galaxy mergers in observations and simulations

Aims. We aim to perform consistent comparisons between observations and simulations on the mass dependence of the galaxy major merger fraction at low redshift over an unprecedentedly wide range of stellar masses (10^9 to 10^12 solar masses). Methods. We first carry out forward modelling of ideal synthetic images of major mergers and non-mergers selected from the Next Generation Illustris Simulations (IllustrisTNG) to include major observational effects. We then train deep convolutional neural networks (CNNs) using realistic mock observations of galaxy samples from the simulations. Subsequently, we apply the trained CNNs to real the Kilo-Degree Survey (KiDS) images of galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. Based on the major merger samples, which are detected in a consistent manner in the observations and simulations, we determine the dependence of major merger fraction on stellar mass at z around 0.15 and make comparisons between the two. Results. The detected major merger fraction in the GAMA/KiDS observations has a fairly mild decreasing trend with increasing stellar mass over the mass range 10^9 < M_sun < M_star < 10^11.5 M_sun. There is good agreement in the mass dependence of the major merger fraction in the GAMA/KiDS observations and the IllustrisTNG simulations over 10^9.5 M_sun < M_star < 10^10.5 M_sun. However, the observations and the simulations show some differences at M_star > 10^10.5M_sun, possibly due to the supermassive blackhole feedback in its low-accretion state in the simulations which causes a sharp transition in the quenched fractions at this mass scale. The discrepancy could also be due to the relatively small volume of the simulations and/or differences in how stellar masses are measured in simulations and observations.

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arXiv2019

The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learning

We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $\sim12,000$ 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 finds that $\sim95\%$ of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite. However, the Stellar Mass Functions decomposed into different morphologies still show significant discrepancies with observations both at the low and high mass end. We find that the high mass end of the SMF is dominated in TNG by massive disk galaxies while early-type galaxies dominate in the observations according to the CNN classifications. The present work highlights the importance of detailed comparisons between observations and simulations in comparable conditions.

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arXiv2018

SDSS-IV MaNGA: Spatially resolved star-formation histories and the connection to galaxy physical properties

A key task of observational extragalactic astronomy is to determine where -- within galaxies of diverse masses and morphologies -- stellar mass growth occurs, how it depends on galaxy properties and what processes regulate star formation. Using spectroscopic indices derived from the stellar continuum at $\sim 4000$Å, we determine the spatially resolved star-formation histories of 980000 spaxels in 2404 galaxies in the SDSS-IV MaNGA IFU survey. We examine the spatial distribution of star-forming, quiescent, green valley, starburst and post-starburst spaxels as a function of stellar mass and morphology to see where and in what types of galaxy star formation is occurring. The spatial distribution of star-formation is dependent primarily on stellar mass, with a noticeable change in the distribution at \mstar$>10^{10}$\msun. Galaxies above this mass have an increasing fraction of regions that are forming stars with increasing radius, whereas lower mass galaxies have a constant fraction of star forming regions with radius. Our findings support a picture of inside-out growth and quenching at high masses. We find that morphology (measured via concentration) correlates with the fraction of star-forming spaxels, but not with their radial distribution. We find (post-)starburst regions are more common outside of the galaxy centre, are preferentially found in asymmetric galaxies, and have lower gas-phase metallicity than other regions, consistent with interactions triggering starbursts and driving low metallicity gas into regions at $<1.5R_e$.

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arXiv2016

The information content of stellar halos: Stellar population gradients and accretion histories in early-type Illustris galaxies

Long dynamical timescales in the outskirts of galaxies preserve the information content of their accretion histories, for example in the form of stellar population gradients. We present a detailed analysis of the stellar halo properties of a statistically representative sample of early-type galaxies from the Illustris simulation and show that stellar population gradients at large radii can indeed be used to infer basic properties of galactic accretion histories. We measure metallicity, age, and surface-brightness profiles in quiescent Illustris galaxies ranging from $\mathrm{M}_\star = 10^{10} - 2\times 10^{12}\;\mathrm{M}_\odot$ and show that they are in reasonable agreement with observations. At fixed mass, galaxies that accreted little of their stellar halo material tend to have steeper metallicity and surface-brightness profiles between 2 - 4 effective radii (R$_e$) than those with larger accreted fractions. Profiles of metallicity and surface-brightness in the stellar halo typically flatten from z = 1 to the present. This suggests that the accretion of stars into the stellar halo tends to flatten metallicity and surface-brightness profiles, a picture which is supported by the tight correlation between the two gradients in the stellar halo. We find no statistical evidence of additional information content related to accretion histories in stellar halo metallicity profiles beyond what is contained in surface-brightness profiles. Age gradients in the stellar halo do not appear to be sensitive to galactic accretion histories, and none of the stellar population gradients studied are strongly correlated with the mean merger mass-ratio. Future observations that reach large radii outside galaxies will have the best potential to constrain galactic accretion histories.

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