Learning the relationship between galaxies spectra and their star formation histories using convolutional neural etworks and cosmological simulations

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train Convolutional Neural Networks to learn the relationship between synthetic galaxy spectra and high resolution SFHs from the EAGLE and Illustris models. To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low-error regime. On dust-attenuated spectra we achieve high test accuracy (median SMAPE = 10.5%). Including the effects of simulated observational noise increases the error (12.5%), however this is alleviated by including multiple realisations of the noise, which increases the training set size and reduces overfitting (10.9%). We also make estimates for the observational and modelling errors. To further evaluate the generalisation properties we apply models trained on one simulation to spectra from the other, which leads to only a small increase in the error (median SMAPE ∼15%⁠). We apply each trained model to SDSS DR7 spectra, and find smoother histories than in the VESPA catalogue. This new approach complements the results of existing SED fitting techniques, providing star formation histories directly motivated by the results of the latest cosmological simulations

AGN Feedback in SDSS-IV MaNGA: AGNs have Suppressed Central Star Formation Rates

Despite the importance of feedback from active galactic nuclei (AGNs) in models of galaxy evolution, observational constraints on the influence of AGN feedback on star formation remain weak. To this end, we have compared the star formation trends of 279 low-redshift AGN galaxies with 558 inactive control galaxies using integral field unit spectroscopy from the SDSS-IV MaNGA survey. With a Gaussian process-based methodology, we reconstruct nonparametric star formation histories in spatially resolved spaxels covering the face of each galaxy. Based on galaxy-wide star formation rates (SFRs) alone, we find no obvious signatures of AGN feedback. However, the AGN galaxies have significantly suppressed central (kiloparsec-scale) SFRs, lying up to a factor of $2$ below those of the control galaxies, providing direct observational evidence of AGN feedback suppressing star formation. The suppression of central SFRs in the AGN galaxies began in the central regions ${\sim} 6$ Gyr ago (redshift $z {\sim} 0.7$), taking place over a few gigayears. A small subset of the AGN galaxies were rapidly driven to quiescence shortly before being observed (in the last $500$ Myr), potentially indicating instances of AGN-driven feedback. More frequently, however, star formation continues in the AGN galaxies, with suppression primarily in the central regions. This is suggestive of a picture in which integrated (Gyr-timescale) AGN feedback can significantly affect central star formation, but may be inefficient in driving galaxy-wide quenching in low-redshift galaxies, instead leaving them in the green valley.Comment: 22 pages, 15 figures. Accepted for publication in Ap

A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship.Comment: 8 pages, 2 figures, accepted for ICML 2023 Workshop on Machine Learning for Astrophysic

Outshining by Recent Star Formation Prevents the Accurate Measurement of High-z Galaxy Stellar Masses

In this Letter, we demonstrate that the inference of galaxy stellar masses via spectral energy distribution (SED) fitting techniques for galaxies formed in the first billion years after the Big Bang carries fundamental uncertainties owing to the loss of star formation history (SFH) information from the very first episodes of star formation in the integrated spectra of galaxies. While this early star formation can contribute substantially to the total stellar mass of high-redshift systems, ongoing star formation at the time of detection outshines the residual light from earlier bursts, hampering the determination of accurate stellar masses. As a result, order of magnitude uncertainties in stellar masses can be expected. We demonstrate this potential problem via direct numerical simulation of galaxy formation in a cosmological context. In detail, we carry out two cosmological simulations with significantly different stellar feedback models which span a significant range in star formation history burstiness. We compute the mock SEDs for these model galaxies at z=7 via 3D dust radiative transfer calculations, and then backwards fit these SEDs with Prospector SED fitting software. The uncertainties in derived stellar masses that we find for z>7 galaxies motivate the development of new techniques and/or star formation history priors to model early Universe star formation.Comment: Submitted to ApJL, comments welcom

The SFR-M _∗ Correlation Extends to Low Mass at High Redshift

To achieve a fuller understanding of galaxy evolution, SED fitting can be used to recover quantities beyond stellar masses (M$_*$) and star formation rates (SFRs). We use Star Formation Histories (SFHs) reconstructed via the Dense Basis method of Iyer \& Gawiser (2017) for a sample of $17,873$ galaxies at $0.5<z<6$ in the CANDELS GOODS-S field to study the nature and evolution of the SFR-M$_*$ correlation. The reconstructed SFHs represent trajectories in SFR-M$_*$ space, enabling us to study galaxies at epochs earlier than observed by propagating them backwards in time along these trajectories. We study the SFR-M$_*$ correlation at $z=1,2,3,4,5,6$ using both direct fits to galaxies observed at those epochs and SFR-M$_*$ trajectories of galaxies observed at lower redshifts. The SFR-M$_*$ correlations obtained using the two approaches are found to be consistent with each other through a KS test. Validation tests using SFHs from semi-analytic models and cosmological hydrodynamical simulations confirm the sensitivity of the method to changes in the slope, normalization and shape of the SFR-M$_*$ correlation. This technique allows us to further probe the low-mass regime of the correlation at high-z by $\sim 1$ dex and over an effective volume of $\sim 10\times$ larger than possible with just direct fits. We find that the SFR-M$_*$ correlation is consistent with being linear down to M$_*\sim 10^7 M_\odot$ at $z>4$. The evolution of the correlation is well described by $\log SFR= (0.80\pm 0.029 - 0.017\pm 0.010\times t_{univ})\log M_*$ $- (6.487\pm 0.282-0.039\pm 0.008\times t_{univ})$, where $t_{univ}$ is the age of the universe in Gyr.Comment: 22 pages, 10 figures. Accepted for publication in Ap

IQ Collaboratory III: The Empirical Dust Attenuation Framework -- Taking Hydrodynamical Simulations with a Grain of Dust

We present the Empirical Dust Attenuation (EDA) framework -- a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color-magnitude relations, $(g-r) - M_r$ and $(FUV-NUV)-M_r$, of the simulations to a $M_r < -20$ and UV complete SDSS galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color-magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation-slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low $A_V$ since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations.Comment: 26 pages, 15 figure

The Diversity and Variability of Star Formation Histories in Models of Galaxy Evolution

Understanding the variability of galaxy star formation histories (SFHs) across a range of timescales provides insight into the underlying physical processes that regulate star formation within galaxies. We compile the SFHs of galaxies at $z=0$ from an extensive set of models, ranging from cosmological hydrodynamical simulations (Illustris, IllustrisTNG, Mufasa, Simba, EAGLE), zoom simulations (FIRE-2, g14, and Marvel/Justice League), semi-analytic models (Santa Cruz SAM) and empirical models (UniverseMachine), and quantify the variability of these SFHs on different timescales using the power spectral density (PSD) formalism. We find that the PSDs are well described by broken power-laws, and variability on long timescales ($\gtrsim1$ Gyr) accounts for most of the power in galaxy SFHs. Most hydrodynamical models show increased variability on shorter timescales ($\lesssim300$ Myr) with decreasing stellar mass. Quenching can induce $\sim0.4-1$ dex of additional power on timescales $>1$ Gyr. The dark matter accretion histories of galaxies have remarkably self-similar PSDs and are coherent with the in-situ star formation on timescales $>3$ Gyr. There is considerable diversity among the different models in their (i) power due to SFR variability at a given timescale, (ii) amount of correlation with adjacent timescales (PSD slope), (iii) evolution of median PSDs with stellar mass, and (iv) presence and locations of breaks in the PSDs. The PSD framework is a useful space to study the SFHs of galaxies since model predictions vary widely. Observational constraints in this space will help constrain the relative strengths of the physical processes responsible for this variability.Comment: 31 pages, 17 figures (+ appendix). Resubmitted to MNRAS after responding to referee's comments. Comments are welcome

Λ\LambdaCDM not dead yet: massive high-z Balmer break galaxies are less common than previously reported

Early JWST observations that targeted so-called double-break sources (attributed to Lyman and Balmer breaks at $z>7$), reported a previously unknown population of very massive, evolved high-redshift galaxies. This surprising discovery led to a flurry of attempts to explain these objects' unexpected existence including invoking alternatives to the standard $\Lambda$CDM cosmological paradigm. To test these early results, we adopted the same double-break candidate galaxy selection criteria to search for such objects in the JWST images of the CAnadian NIRISS Unbiased Cluster Survey (CANUCS), and found a sample of 19 sources over five independent CANUCS fields that cover a total effective area of $\sim60\,$arcmin$^2$ at $z\sim8$. However, (1) our SED fits do not yield exceptionally high stellar masses for our candidates, while (2) spectroscopy of five of the candidates shows that while all five are at high redshifts, their red colours are due to high-EW emission lines in star-forming galaxies rather than Balmer breaks in massive, evolved systems. Additionally, (3) field-to-field variance leads to differences of $\sim 1.5$ dex in the maximum stellar masses measured in the different fields, suggesting that the early single-field JWST observations may have suffered from cosmic variance and/or sample bias. Finally, (4) we show that the presence of even a single massive outlier can dominate conclusions from small samples such as those in early JWST observations. In conclusion, we find that the double-break sources in CANUCS are not sufficiently massive or numerous to warrant questioning the standard $\Lambda$CDM paradigm.Comment: V2: correction of display problem of Fig.1 in Chrome browser. Submitted to MNRAS, 10 pages (+4 in Appendix), 5 figures (+4), 1 table (+1