arXiv subjects

Brett H. Andrews

Publications and source records attributed to Brett H. Andrews.

At least 19 recordsLinked to original sources

arXiv2026

Optimizing Photometric Redshift Training Sets I: Efficient Compression of the Galaxy Color-Redshift Relation with UMAP

Spectroscopic datasets are essential for training and calibrating photometric redshift (photo-$z$) methods. However, spectroscopic redshifts (spec-$z$'s) constitute a biased and sparse sampling of the photometric galaxy population, which creates difficulties for the common grid-based approach for mapping color to redshift using self-organizing maps (SOMs). Instead, we utilized the uniform manifold approximation and projection (UMAP) algorithm to compress a Rubin--Roman-like $ugrizyJH$ color space into a thin and densely-sampled manifold. Crucially, the manifold varies continuously and monotonically in redshift and specific star formation rate in roughly orthogonal directions. Using COSMOS2020 many-band photo-$z$'s and compiled spec-$z$'s as representative and non-representative samples, respectively, we trained and tested redshift prediction from a SOM, from nearest neighbors in UMAP coordinates (UMAP-$k$NN-$z$), and directly from nearest neighbors in the color space to assess how well location in each space maps to redshift. For the representative training set, UMAP-$k$NN-$z$ exhibited smaller photo-$z$ scatter and fraction of outliers than SOM-based methods. When training with the highly-biased spec-$z$ sample, UMAP-$k$NN-$z$ maintained similar performance while SOM- and color-based methods predictions were significantly degraded, especially at $z>1.5$ where training sets are the most sparse. The physically-meaningful trends across the UMAP manifold allow for accurate redshift prediction even in such poorly-sampled regions of color space. This suggests that representative, spectroscopically-anchored training sets can be produced by interpolating between spectroscopic sources at the UMAP coordinates of photometric objects, maximizing the performance of photo-$z$ algorithms.

astro-ph.GA↗

arXiv2026

Robust Photometry for Roman High-Latitude Imaging Survey Cosmology Using Roman and Rubin Imaging

Accurate and precise photometry is essential for Roman cosmology because it affects photometric-redshift performance, galaxy and cluster selection, tomographic binning, and redshift-distribution characterization. Achieving robust photometry is challenging because Roman's depth leads to substantial source blending, particularly in joint Roman--Rubin analyses, where the two surveys have different spatial resolutions. In this work, we develop and validate slimfarmer, a model-fitting photometry pipeline designed for Roman High-Latitude Imaging Survey (HLIS) cosmological analyses. Building on The Farmer, slimfarmer introduces treatments of the correlated noise present in Roman images and of astronomical shot noise, together with model-fitting configurations tuned for Roman imaging. We validate the pipeline using Roman coadded simulated images and perform forced photometry on matched Rubin image simulations. We find that slimfarmer recovers galaxy colors with the mode of the color residuals within 20 millimag for Roman-based colors and within 70 millimag for Rubin-based colors. Properly accounting for correlated noise is essential for uncertainty quantification, as photometric uncertainties are otherwise underestimated by up to a factor of ~3. We also find that applying multi-object fitting consistently to both Roman and Rubin imaging substantially mitigates blending-induced color systematics. In crowded regions, single-object fitting produces environment-dependent color biases of up to ~0.5 mag, leading to environment-dependent degradation of photometric-redshift performance. Together, these results establish slimfarmer as a prototype photometric pipeline for Roman HLIS cosmology applications and identify the correlated-noise corrections, astronomical shot-noise treatment, and joint Roman--Rubin multi-object fitting required for robust photometric-redshift characterization.

astro-ph.IM↗

arXiv2026

Modeling Redshift Uncertainties in Roman Weak Lensing Cosmology

Cosmological constraints using weak gravitational lensing measurements from the Roman Space Telescope will require a powerful method for modelling uncertainties in the galaxy redshift distribution. In this work, we use an optimized version of the principal component analysis (PCA) to model uncertainties in the full shape of the redshift distributions, a method proposed by \cite{pca_method} and recently used in the Dark Energy Survey Y6 analysis. Here, we implement this new approach within the Roman High Latitude Imaging Survey (HLIS) Cosmology Project Infrastructure Team (PIT) pipeline, namely Cobaya-Cosmolike Joint Architecture (\texttt{CoCoA}). To validate the PCA in mitigating biases on cosmological parameters, $S_8$ and $Ω_m$, we use a set of redshift distributions from \texttt{Cardinal} generated for a variety of Roman configurations. Overall, when the simulated cosmic shear data vector is not strongly miscalibrated relative to the fiducial one, both the mean-shift and the PCA-based approaches produce consistent cosmological constraints when marginalizing over nuisance parameters. For mild to strong miscalibration, including additional PCs progressively mitigates biases in $S_8$ and $Ω_m$, and can achieve comparable performance with fewer parameters than the nine tomographic-bin mean-shift model.

astro-ph.CO↗

arXiv2026

Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS

Photometric redshifts (photo-$z$'s) will be crucial for studies of galaxy evolution, large-scale structure, and transients with the Nancy Grace Roman Space Telescope. Deep learning methods leverage pixel-level information from ground-based images to achieve the best photo-$z$'s for low-redshift galaxies, but their efficacy at higher redshifts with deep, space-based imaging remains largely untested. We used Hubble Space Telescope CANDELS optical and near-infrared imaging to evaluate fully-supervised, self-supervised, and semi-supervised deep learning photo-$z$ algorithms out to $z\sim3$. Compared to template-based and classical machine learning photometry methods, the fully-supervised and semi-supervised models achieved better performance. Our new semi-supervised model, PITA (Photo-$z$ Inference with a Triple-task Algorithm), outperformed all others by learning from unlabeled and labeled data through a three-part loss function that incorporates images and colors for all objects as well as redshifts when available. PITA produces a latent space that varies smoothly in magnitude, color, and redshift, resulting in the best photo-$z$ performance even when the redshift training set was significantly reduced. In contrast, the self-supervised approach produced a latent space with significant color and redshift fluctuations that hindered photo-$z$ inference. Looking forward to Roman, we recommend using semi supervised deep learning to take full advantage of the information contained in the hundreds of millions of high-resolution images and color measurements, together with the limited redshift measurements available, to achieve the most accurate photo-$z$ estimates for both faint and bright sources.

astro-ph.IM↗

arXiv2025

Deep Learning Improves Photometric Redshifts in All Regions of Color Space

Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space Telescope. Previous work has shown that image-based deep learning photo-$z$ methods produce smaller scatter than photometry-based classical machine learning (ML) methods on the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample, a testbed photo-$z$ dataset. However, global assessments can obscure local trends. To explore this possibility, we used a self-organizing map (SOM) to cluster SDSS galaxies based on their $ugriz$ colors. Deep learning methods achieve lower photo-$z$ scatter than classical ML methods for all SOM cells. The fractional reduction in scatter is roughly constant across most of color space with the exception of the most bulge-dominated and reddest cells where it is smaller in magnitude. Interestingly, classical ML photo-$z$'s suffer from a significant color-dependent attenuation bias, where photo-$z$'s for galaxies within a SOM cell are systematically biased towards the cell's mean spectroscopic redshift and away from extreme values, which is not readily apparent when all objects are considered. In contrast, deep learning photo-$z$'s suffer from very little color-dependent attenuation bias. The increased attenuation bias for classical ML photo-$z$ methods is the primary reason why they exhibit larger scatter than deep learning methods. This difference can be explained by the deep learning methods weighting redshift information from the individual pixels of a galaxy image more optimally than integrated photometry.

astro-ph.GA↗

arXiv2025

Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable $y$ given complex inputs $\mathbf{x}$. Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all $\mathbf{x}$, and when needed, to reshape the densities of $y$ toward "instance-wise" calibration. This paper introduces the LADaR (Local Amortized Diagnostics and Reshaping of Conditional Densities) framework and proposes a new computationally efficient algorithm ($\texttt{Cal-PIT}$) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs). $\texttt{Cal-PIT}$ learns a single interpretable local probability--probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where $\texttt{Cal-PIT}$ achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyses.

stat.ML↗

arXiv2024

DESI Massive Post-Starburst Galaxies at $\mathbf{z\sim1.2}$ have compact structures and dense cores

Post-starburst galaxies (PSBs) are young quiescent galaxies that have recently experienced a rapid decrease in star formation, allowing us to probe the fast-quenching period of galaxy evolution. In this work, we obtained HST WFC3/F110W imaging to measure the sizes of 171 massive ($\mathrm{log(M_{*}/M_{\odot})\sim\,11)}$ spectroscopically identified PSBs at $1<z<1.3$ selected from the DESI Survey Validation Luminous Red Galaxy sample. This statistical sample constitutes an order of magnitude increase from the $\sim20$ PSBs with space-based imaging and deep spectroscopy. We perform structural fitting of the target galaxies with \texttt{pysersic} and compare them to quiescent and star-forming galaxies in the 3D-HST survey. We find that these PSBs are more compact than the general population of quiescent galaxies, lying systematically $\mathrm{\sim\,0.1\,dex}$ below the established size-mass relation. However, their central surface mass densities are similar to those of their quiescent counterparts ($\mathrm{\,log(Σ_{1\,kpc}/(M_{\odot}/kpc^2))\sim\,10.1}$). These findings are easily reconciled by later ex-situ growth via minor mergers or a slight progenitor bias. These PSBs are round in projection ($b/a_{median}\sim0.8$), suggesting that they are primarily spheroids, not disks, in 3D. We find no correlation between time since quenching and light-weighted PSB sizes or central densities. This disfavors apparent structural growth due to the fading of centralized starbursts in this galaxy population. Instead, we posit that the fast quenching of massive galaxies at this epoch occurs preferentially in galaxies with pre-existing compact structures.

astro-ph.GA↗

arXiv2024

The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster Catalogue

Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly-lensed transients. We here present a new cluster-finding algorithm, CluMPR (Clusters from Masses and Photometric Redshifts), that exploits photometric redshifts (photo-z's) as well as photometric stellar mass measurements. CluMPR uses a 2-dimensional binary search tree to search for overdensities of massive galaxies with similar redshifts on the sky and then probabilistically assigns cluster membership by accounting for photo-z uncertainties. We leverage the deep DESI Legacy Survey grzW1W2 imaging over one-third of the sky to create a catalogue of ~ 300,000 galaxy cluster candidates out to z = 1, including tabulations of member galaxies and estimates of each cluster's total stellar mass. Compared to other methods, CluMPR is particularly effective at identifying clusters at the high end of the redshift range considered (z = 0.75-1), with minimal contamination from low-mass groups. These characteristics make it ideal for identifying strongly lensed high-redshift supernovae and quasars that are powerful probes of cosmology, dark matter, and stellar astrophysics. As an example application of this cluster catalogue, we present a catalogue of candidate wide-angle strongly-lensed quasars in Appendix C. The five best candidates identified from this sample include two known lensed quasar systems and a possible changing-look lensed QSO with SDSS spectroscopy. All code and catalogues produced in this work are publicly available (see Data Availability).

astro-ph.CO↗

arXiv2024

Emission Line Predictions for Mock Galaxy Catalogues: a New Differentiable and Empirical Mapping from DESI

We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H$α$, H$β$, [O II], and [O III]) from a galaxy's rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman's rank correlation coefficient $ρ_s > 0.87$ between predictions and observations for most lines. Using a non-linear dimensionality reduction technique (UMAP), we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterise and account for biases in the spectroscopic training sets used for training and calibration of photo-$z$'s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

astro-ph.GA↗

arXiv2024

Improving Photometric Redshift Estimates with Training Sample Augmentation

Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for LSST, so we seek methods to improve the photo-z estimates that arise from non-representative training samples. Spectroscopic training samples for photo-z's are biased towards redder, brighter galaxies, which also tend to be at lower redshift than the typical galaxy observed by LSST, leading to poor photo-z estimates with outlier fractions nearly 4 times larger than for a representative training sample. In this paper, we apply the concept of training sample augmentation, where we augment simulated non-representative training samples with simulated galaxies possessing otherwise unrepresented features. When we select simulated galaxies with (g-z) color, i-band magnitude and redshift outside the range of the original training sample, we are able to reduce the outlier fraction of the photo-z estimates for simulated LSST data by nearly 50% and the normalized median absolute deviation (NMAD) by 56%. When compared to a fully representative training sample, augmentation can recover nearly 70% of the degradation in the outlier fraction and 80% of the degradation in NMAD. Training sample augmentation is a simple and effective way to improve training samples for photo-z's without requiring additional spectroscopic samples.

astro-ph.IM↗

arXiv2023

The Gas-Phase Mass--Metallicity Relation for Massive Galaxies at $z\sim0.7$ with the LEGA-C Survey

The massive end of the gas-phase mass--metallicity relation (MZR) is a sensitive probe of active galactic nuclei (AGN) feedback that is a crucial but highly uncertain component of galaxy evolution models. In this paper, we extend the $z\sim0.7$ MZR by $\sim$0.5 dex up to log$(M_\star/\textrm{M}_\odot)\sim11.1$. We use extremely deep VLT VIMOS spectra from the Large Early Galaxy Astrophysics Census (LEGA-C) survey to measure metallicities for 145 galaxies. The LEGA-C MZR matches the normalization of the $z\sim0.8$ DEEP2 MZR where they overlap, so we combine the two to create an MZR spanning from 9.3 to 11.1 log$(M_\star/\textrm{M}_\odot)$. The LEGA-C+DEEP2 MZR at $z\sim0.7$ is offset to slightly lower metallicities (0.05-0.13 dex) than the $z\sim0$ MZR, but it otherwise mirrors the established power law rise at low/intermediate stellar masses and asymptotic flattening at high stellar masses. We compare the LEGA-C+DEEP2 MZR to the MZR from two cosmological simulations (IllustrisTNG and SIMBA), which predict qualitatively different metallicity trends for high-mass galaxies. This comparison highlights that our extended MZR provides a crucial observational constraint for galaxy evolution models in a mass regime where the MZR is very sensitive to choices about the implementation of AGN feedback.

astro-ph.GA↗

arXiv2023

DESI Survey Validation Spectra Reveal an Increasing Fraction of Recently Quenched Galaxies at $z\sim1$

We utilize $\sim17000$ bright Luminous Red Galaxies (LRGs) from the novel Dark Energy Spectroscopic Instrument Survey Validation spectroscopic sample, leveraging its deep ($\sim2.5$ hour/galaxy exposure time) spectra to characterize the contribution of recently quenched galaxies to the massive galaxy population at $0.4 1$) of our sample of recently quenched galaxies represents the largest spectroscopic sample of post-starburst galaxies at that epoch. At $0.4 11.2$) LRGs by measuring the fraction of stellar mass each galaxy formed in the Gyr before observation, $f_\mathrm{1 Gyr}$. Although galaxies with $f_\mathrm{1 Gyr}>0.1$ are rare at $z\sim0.4$ ($\lesssim 0.5\%$ of the population), by $z\sim0.8$ they constitute $\sim3\%$ of massive galaxies. Relaxing this threshold, we find that galaxies with $f_\mathrm{1 Gyr}>5\%$ constitute $\sim10\%$ of the massive galaxy population at $z\sim0.8$. We also identify a small but significant sample of galaxies at $z=1.1-1.3$ that formed with $f_\mathrm{1 Gyr}>50\%$, implying that they may be analogues to high-redshift quiescent galaxies that formed on similar timescales. Future analysis of this unprecedented sample promises to illuminate the physical mechanisms that drive the quenching of massive galaxies after cosmic noon.

astro-ph.GA↗

arXiv2023

Are Milky-Way-like galaxies like the Milky Way? A view from SDSS-IV/MaNGA

In this paper, we place the Milky Way (MW) in the context of similar-looking galaxies in terms of their star-formation and chemical evolution histories. We select a sample of 138 Milky-Way analogues (MWAs) from the SDSS-IV/MaNGA survey based on their masses, Hubble types, and bulge-to-total ratios. To compare their chemical properties to the detailed spatially-resolved information available for the MW, we use a semi-analytic spectral fitting approach, which fits a self-consistent chemical-evolution and star-formation model directly to the MaNGA spectra. We model the galaxies' inner and outer regions assuming that some of the material lost in stellar winds falls inwards. We also incorporate chemical enrichment from type II and Ia supernovae to follow the alpha-element abundance at different metallicities and locations. We find some MWAs where the stellar properties closely reproduce the distribution of age, metallicity, and alpha enhancement at both small and large radii in the MW. In these systems, the match is driven by the longer timescale for star formation in the outer parts, and the inflow of enriched material to the central parts. However, other MWAs have very different histories. These divide into two categories: self-similar galaxies where the inner and outer parts evolve identically; and centrally-quenched galaxies where there is very little evidence of late-time central star formation driven by material accreted from the outer regions. We find that, although selected to be comparable, there are subtle morphological differences between galaxies in these different classes, and that the centrally-quenched galaxies formed their stars systematically earlier.

astro-ph.GA↗

arXiv2023

Optimized Photometric Redshifts for the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS)

We present the first comprehensive release of photometric redshifts (photo-z's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile-Quantile (Q--Q) plot to identify biases and signatures of underestimated or overestimated errors in photo-z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group's PDF, we explore three methods of combining the different groups' PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f-divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo-z PDFs and point estimates are achieved with the minimum f-divergence using the best 4 PDFs for each object (mFDa4) and the Hierarchical Bayesian (HB4) methods, respectively. The HB4 photo-z point estimates produced $σ_{\rm NMAD} = 0.0227/0.0189$ and $|Δz/(1+z)| > 0.15$ outlier fraction = 0.067/0.019 for spectroscopic and 3D-HST redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo-z catalogs, which are available at https://archive.stsci.edu/hlsp/candels.

astro-ph.GA↗

arXiv2022

Empirically-Driven Multiwavelength K-corrections At Low Redshift

K-corrections, conversions between flux in observed bands to flux in rest-frame bands, are critical for comparing galaxies at various redshifts. These corrections often rely on fits to empirical or theoretical spectral energy distribution (SED) templates of galaxies. However, the templates limit reliable K-corrections to regimes where SED models are robust. For instance, the templates are not well-constrained in some bands (e.g., WISE W4), which results in ill-determined K-corrections for these bands. We address this shortcoming by developing an empirically-driven approach to K-corrections as a means to mitigate dependence on SED templates. We perform a polynomial fit for the K-correction as a function of a galaxy's rest-frame color determined in well-constrained bands (e.g., rest-frame (g-r)) and redshift, exploiting the fact that galaxy SEDs can be described as a one parameter family at low redshift (0.01 < z < 0.09). For bands well-constrained by SED templates, our empirically-driven K-corrections are comparable to the SED fitting method of Kcorrect and SED template fitting employed in the GSWLC-M2 catalogue (the updated medium-deep GALEX-SDSS-WISE Legacy Catalogue). However, our method dramatically outperforms the available SED fitting K-corrections for WISE W4. Our method also mitigates incorrect template assumptions and enforces the K-correction to be 0 at z = 0. Our K-corrected photometry and code are publicly available.

astro-ph.CO↗

arXiv2022

Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network

Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time (LSST) are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on $ugriz$ images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of $\sim$400,000 Sloan Digital Sky Survey (SDSS) galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets ($r\leq17.8$ and $z_{\mathrm{spec}}\leq0.4$) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a 2-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g., size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g., magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1 .

astro-ph.IM↗

arXiv2022

SDSS-IV MaNGA: How the stellar populations of passive central galaxies depend on stellar and halo mass

We analyze spatially resolved and co-added SDSS-IV MaNGA spectra with signal-to-noise ~100 from 2200 passive central galaxies (z~0.05) to understand how central galaxy assembly depends on stellar mass (M*) and halo mass (Mh). We control for systematic errors in Mh by employing a new group catalog from Tinker (2020a,b) and the widely-used Yang et al. (2007) catalog. At fixed M*, the strength of several stellar absorption features varies systematically with Mh. Completely model-free, this is one of the first indications that the stellar populations of centrals with identical M* are affected by the properties of their host halos. To interpret these variations, we applied full spectral fitting with the code alf. At fixed M*, centrals in more massive halos are older, show lower [Fe/H], and have higher [Mg/Fe] with 3.5 sigma confidence. We conclude that halos not only dictate how much M* galaxies assemble, but also modulate their chemical enrichment histories. Turning to our analysis at fixed Mh, high-M* centrals are older, show lower [Fe/H], and have higher [Mg/Fe] for Mh>10^{12}Msun/h with confidence > 4 sigma. While massive passive galaxies are thought to form early and rapidly, our results are among the first to distinguish these trends at fixed Mh. They suggest that high-M* centrals experienced unique early formation histories, either through enhanced collapse and gas fueling, or because their halos were early-forming and highly concentrated, a possible signal of galaxy assembly bias.

astro-ph.GA↗

arXiv2022

SDSS-IV MaNGA: Exploring the local scaling relations for N/O

We present, for the first time, the relationship between local stellar mass surface density, $\mathrm{Σ_{*}}$, and N/O derived from SDSS-IV MaNGA data, using a sample of $792765$ high signal-to-noise ratio star-forming spaxels. Using a combination of phenomenological modelling and partial correlation analysis, we find that $\mathrm{Σ_{*}}$ alone is insufficient to predict the N/O in MaNGA spaxels, and that there is an additional dependence on the local star formation rate surface density, $\mathrm{Σ_{SFR}}$. This effect is a factor of $3$ stronger than the dependence of 12+log(O/H) on $\mathrm{Σ_{SFR}}$. Surprisingly, we find that the local N/O scaling relations also depend on the total galaxy stellar mass at fixed $Σ_{*}$ as well as the galaxy size at fixed stellar mass. We find that more compact galaxies are more nitrogen rich, even when $\mathrm{Σ_{*}}$ and $\mathrm{Σ_{SFR}}$ are controlled for. We show that $\sim50\%$ of the variance of N/O is explained by the total stellar mass and size. Thus, the evolution of nitrogen in galaxies is set by more than just local effects and does not simply track the build up of oxygen in galaxies. The precise form of the N/O-O/H relation is therefore sensitive to the sample of galaxies from which it is derived. This result casts doubt on the universal applicability of nitrogen-based strong-line metallicity indicators derived in the local universe.

astro-ph.GA↗