arXiv subjects

Marc Huertas-Company

Publications and source records attributed to Marc Huertas-Company.

At least 55 records · Page 3Linked to original sources

arXiv2024

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.

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arXiv2024

CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3<z<5$ CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM lightcone. We apply our models to observed CEERS galaxies at $3<z<5$. We find that our models correctly classify $\sim60-70\%$ of simulated merging and non-merging galaxies; better performance on the merger class comes at the expense of misclassifying more non-mergers. We could achieve more accurate classifications, as well as test for the dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified $40-60\%$ of mergers and non-mergers at $3<z<4$, but tended to classify most objects as non-mergers at $4<z<5$ (misclassifying $\sim70\%$ of visually-classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying $80-90\%$ of visually-classified non-mergers). We investigate what features the models find most useful, as well as characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

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arXiv2024

Constraints on the in-situ and ex-situ stellar masses in nearby galaxies with Artificial Intelligence

The hierarchical model of galaxy evolution suggests that the impact of mergers is substantial on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy's total stellar mass and its balance with in-situ star formation poses a persistent challenge, as it is neither directly observable nor easily inferred from observational properties. Here, we present theory-motivated predictions for the fraction of stellar mass originating from mergers in a statistically significant sample of nearby galaxies, using data from MaNGA. Employing a robust machine learning model trained on mock MaNGA analogs (MaNGIA) in turn obtained from a cosmological simulation (TNG50), we unveil that in-situ stellar mass dominates almost across the entire stellar mass spectrum (1e9Msun < M* < 1e12Msun). Only in more massive galaxies (M* > 1e11Msun) does accreted mass become a substantial contributor, reaching up to 35-40% of the total stellar mass. Notably, the ex-situ stellar mass in the nearby universe exhibits significant dependence on galaxy characteristics, with higher accreted fractions favored by elliptical, quenched galaxies and slow rotators, as well as galaxies at the center of more massive dark matter halos.

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arXiv2024

Deriving the star formation histories of galaxies from spectra with simulation-based inference

High-resolution galaxy spectra encode information about the stellar populations within galaxies. The properties of the stars, such as their ages, masses, and metallicities, provide insights into the underlying physical processes that drive the growth and transformation of galaxies over cosmic time. We explore a simulation-based inference (SBI) workflow to infer from optical absorption spectra the posterior distributions of metallicities and the star formation histories (SFHs) of galaxies (i.e. the star formation rate as a function of time). We generated a dataset of synthetic spectra to train and test our model using the spectroscopic predictions of the MILES stellar population library and non-parametric SFHs. We reliably estimate the mass assembly of an integrated stellar population with well-calibrated uncertainties. Specifically, we reach a score of $0.97\,R^2$ for the time at which a given galaxy from the test set formed $50\%$ of its stellar mass, obtaining samples of the posteriors in only $10^{-4}$\,s. We then applied the pipeline to real observations of massive elliptical galaxies, recovering the well-known relationship between the age and the velocity dispersion, and show that the most massive galaxies ($σ\sim300$ km/s) built up to 90\% of their total stellar masses within $1$\,Gyr of the Big Bang. The inferred properties also agree with the state-of-the-art inversion codes, but the inference is performed up to five orders of magnitude faster. This SBI approach coupled with machine learning and applied to full spectral fitting makes it possible to address large numbers of galaxies while performing a thick sampling of the posteriors. It will allow both the deterministic trends and the inherent uncertainties of the highly degenerated inversion problem to be estimated for large and complex upcoming spectroscopic surveys, such as DESI, WEAVE, or 4MOST.

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arXiv2024

AstroPT: Scaling Large Observation Models for Astronomy

This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 million $512 \times 512$ pixel $grz$-band galaxy postage stamp observations from the DESI Legacy Survey DR8. We train a selection of foundation models of increasing size from 1 million to 2.1 billion parameters, and find that AstroPT follows a similar saturating log-log scaling law to textual models. We also find that the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. We believe that collaborative community development paves the best route towards realising an open source `Large Observation Model' -- a model trained on data taken from the observational sciences at the scale seen in natural language processing. To this end, we release the source code, weights, and dataset for AstroPT under the MIT license, and invite potential collaborators to join us in collectively building and researching these models.

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arXiv2024

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

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

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arXiv2024

NGDEEP Epoch 1: Spatially Resolved H$α$ Observations of Disk and Bulge Growth in Star-Forming Galaxies at $z \sim$ 0.6-2.2 from JWST NIRISS Slitless Spectroscopy

We study the H$α$ equivalent width, EW(H$α$), maps of 19 galaxies at $0.6 < z < 2.2$ in the Hubble Ultra Deep Field (HUDF) derived from NIRISS slitless spectroscopy as part of the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey. Our galaxies mostly lie on the star-formation main sequence with a stellar mass range of $\mathrm{10^9 - 10^{11} M_\odot}$, characterized as "typical" star-forming galaxies at these redshifts. Leveraging deep HST and JWST broad-band images, spanning 0.4-4.8 $μ$m, we perform spatially-resolved fitting of the spectral energy distributions (SEDs) for these galaxies and construct specific star formation rate (sSFR) and stellar-mass-weighted age maps with a spatial resolution of $\sim$1 kpc. The pixel-to-pixel EW(H$α$) increases with increasing sSFR and with decreasing age. The average trends are slightly different from the relations derived from integrated fluxes of galaxies from the literature, suggesting complex evolutionary trends within galaxies. We quantify the radial profiles of EW(H$α$), sSFR, and age. The majority (84%) of galaxies show positive EW(H$α$) gradients in line with the inside-out quenching scenario. A few galaxies (16%) show inverse (and flat) trends possibly due to merging or starbursts. We compare the distributions of EW(H$α$) and sSFR to the star formation history models (SFHs) as a function of galactocentric radius. We argue that the central regions of galaxies have experienced, at least one, rapid star-formation episodes, which leads to the formation of the bulge, while their outer regions (e.g., disks) grow via more smoothly varying SFHs. These results demonstrate the ability to study resolved star formation in distant galaxies with JWST NIRISS.

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arXiv2024

Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS

The 3D geometry of high-redshift galaxies remains poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in JWST-CEERS observations with $\log M_*/M_{\odot}=9.0-10.5$ at $z=0.5-8.0$. We reproduce previous results from HST-CANDELS in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size and covariances with samples as small as $\sim50$ galaxies. We find high 3D ellipticities for all mass-redshift bins suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be $\sim1$ for $\log M_*/M_{\odot}=9.0-9.5$ dwarfs at $z>1$ (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a ``banana'' in the projected $b/a-\log a$ diagram with an excess of low $b/a$, large $\log a$ galaxies. The dwarf prolate fraction rises from $\sim25\%$ at $z=0.5-1.0$ to $\sim50-80\%$ at $z=3-8$. If these are disks, they cannot be axisymmetric but instead must be unusually oval (triaxial) unlike local circular disks. We simultaneously constrain the 3D size-mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar Sérsic indices ($n\sim1$), non-parametric morphological properties and specific star formation rates. Both tend to be visually classified as disks or irregular but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects and theoretical implications.

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arXiv2023

Evidence for a Shallow Evolution in the Volume Densities of Massive Galaxies at $z=4$ to $8$ from CEERS

We analyze the evolution of massive (log$_{10}$ [$M_\star/M_\odot$] $>10$) galaxies at $z \sim$ 4--8 selected from the JWST Cosmic Evolution Early Release Science (CEERS) survey. We infer the physical properties of all galaxies in the CEERS NIRCam imaging through spectral energy distribution (SED) fitting with dense basis to select a sample of high redshift massive galaxies. Where available we include constraints from additional CEERS observing modes, including 18 sources with MIRI photometric coverage, and 28 sources with spectroscopic confirmations from NIRSpec or NIRCam wide-field slitless spectroscopy. We sample the recovered posteriors in stellar mass from SED fitting to infer the volume densities of massive galaxies across cosmic time, taking into consideration the potential for sample contamination by active galactic nuclei (AGN). We find that the evolving abundance of massive galaxies tracks expectations based on a constant baryon conversion efficiency in dark matter halos for $z \sim$ 1--4. At higher redshifts, we observe an excess abundance of massive galaxies relative to this simple model. These higher abundances can be explained by modest changes to star formation physics and/or the efficiencies with which star formation occurs in massive dark matter halos, and are not in tension with modern cosmology.

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arXiv2023

The Complete CEERS Early Universe Galaxy Sample: A Surprisingly Slow Evolution of the Space Density of Bright Galaxies at z ~ 8.5-14.5

We present a sample of 88 candidate z~8.5-14.5 galaxies selected from the completed NIRCam imaging from the Cosmic Evolution Early Release Science (CEERS) survey. These data cover ~90 arcmin^2 (10 NIRCam pointings) in six broad-band and one medium-band imaging filter. With this sample we confirm at higher confidence early JWST conclusions that bright galaxies in this epoch are more abundant than predicted by most theoretical models. We construct the rest-frame ultraviolet luminosity functions at z~9, 11 and 14, and show that the space density of bright (M_UV=-20) galaxies changes only modestly from z~14 to z~9, compared to a steeper increase from z~8 to z~4. While our candidates are photometrically selected, spectroscopic followup has now confirmed 13 of them, with only one significant interloper, implying that the fidelity of this sample is high. Successfully explaining the evidence for a flatter evolution in the number densities of UV-bright z>10 galaxies may thus require changes to the dominant physical processes regulating star formation. While our results indicate that significant variations of dust attenuation with redshift are unlikely to be the dominant factor at these high redshifts, they are consistent with predictions from models which naturally have enhanced star-formation efficiency and/or stochasticity. An evolving stellar initial mass function could also bring model predictions into better agreement with our results. Deep spectroscopic followup of a large sample of early galaxies can distinguish between these competing scenarios.

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arXiv2023

A Milky Way-like barred spiral galaxy at a redshift of 3

The majority of massive disk galaxies in the local Universe show a stellar barred structure in their central regions, including our Milky Way. Bars are supposed to develop in dynamically cold stellar disks at low redshift, as the strong gas turbulence typical of disk galaxies at high redshift suppresses or delays bar formation. Moreover, simulations predict bars to be almost absent beyond $z = 1.5$ in the progenitors of Milky Way-like galaxies. Here we report observations of ceers-2112, a barred spiral galaxy at redshift $z_{\rm phot} \sim 3$, which was already mature when the Universe was only 2 Gyr old. The stellar mass ($M_{\star} = 3.9 \times 10^9 M_{\odot}$) and barred morphology mean that ceers-2112 can be considered a progenitor of the Milky Way, in terms of both structure and mass-assembly history in the first 2 Gyr of the Universe, and was the closest in mass in the first 4 Gyr. We infer that baryons in galaxies could have already dominated over dark matter at $z \sim 3$, that high-redshift bars could form in approximately 400 Myr and that dynamically cold stellar disks could have been in place by redshift $z = 4-5$ (more than 12 Gyrs ago).

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arXiv2023

CEERS Key Paper I: An Early Look into the First 500 Myr of Galaxy Formation with JWST

We present an investigation into the first 500 Myr of galaxy evolution from the Cosmic Evolution Early Release Science (CEERS) survey. CEERS, one of 13 JWST ERS programs, targets galaxy formation from z~0.5 to z>10 using several imaging and spectroscopic modes. We make use of the first epoch of CEERS NIRCam imaging, spanning 35.5 sq. arcmin, to search for candidate galaxies at z>9. Following a detailed data reduction process implementing several custom steps to produce high-quality reduced images, we perform multi-band photometry across seven NIRCam broad and medium-band (and six Hubble broadband) filters focusing on robust colors and accurate total fluxes. We measure photometric redshifts and devise a robust set of selection criteria to identify a sample of 26 galaxy candidates at z~9-16. These objects are compact with a median half-light radius of ~0.5 kpc. We present an early estimate of the z~11 rest-frame ultraviolet (UV) luminosity function, finding that the number density of galaxies at M_UV ~ -20 appears to evolve very little from z~9 to z~11. We also find that the abundance (surface density [arcmin^-2]) of our candidates exceeds nearly all theoretical predictions. We explore potential implications, including that at z>10 star formation may be dominated by top-heavy initial mass functions, which would result in an increased ratio of UV light per unit halo mass, though a complete lack of dust attenuation and/or changing star-formation physics may also play a role. While spectroscopic confirmation of these sources is urgently required, our results suggest that the deeper views to come with JWST should yield prolific samples of ultra-high-redshift galaxies with which to further explore these conclusions.

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arXiv2023

Evolution of the Size-Mass Relation of Star-forming Galaxies Since $z=5.5$ Revealed by CEERS

We combine deep imaging data from the CEERS early release JWST survey and HST imaging from CANDELS to examine the size-mass relation of star-forming galaxies and the morphology-quenching relation at stellar masses $\textrm{M}_{\star} \geq 10^{9.5} \ \textrm{M}_{\odot}$ over the redshift range $0.5 < z < 5.5$. In this study with a sample of 2,450 galaxies, we separate star-forming and quiescent galaxies based on their star-formation activity and confirm that star-forming and quiescent galaxies have different morphologies out to $z=5.5$, extending the results of earlier studies out to higher redshifts. We find that star-forming and quiescent galaxies have typical Sérsic indices of $n\sim1.3$ and $n\sim4.3$, respectively. Focusing on star-forming galaxies, we find that the slope of the size-mass relation is nearly constant with redshift, as was found previously, but shows a modest increase at $z \sim 4.2$. The intercept in the size-mass relation declines out to $z=5.5$ at rates that are similar to what earlier studies found. The intrinsic scatter in the size-mass relation is relatively constant out to $z=5.5$.

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arXiv2023

JWST constraints on the UV luminosity density at cosmic dawn: implications for 21-cm cosmology

An unprecedented array of new observational capabilities are starting to yield key constraints on models of the epoch of first light in the Universe. In this Letter we discuss the implications of the UV radiation background at cosmic dawn inferred by recent JWST observations for radio experiments aimed at detecting the redshifted 21-cm hyperfine transition of diffuse neutral hydrogen. Under the basic assumption that the 21-cm signal is activated by the Ly$α$ photon field produced by metal-poor stellar systems, we show that a detection at the low frequencies of the EDGES and SARAS3 experiments may be expected from a simple extrapolation of the declining UV luminosity density inferred at $z\lesssim 14$ from JWST early galaxy data. Accounting for an early radiation excess above the CMB suggests a shallower or flat evolution to simultaneously reproduce low and high-$z$ current UV luminosity density constraints, which cannot be entirely ruled out, given the large uncertainties from cosmic variance and the faint-end slope of the galaxy luminosity function at cosmic dawn. Our findings raise the intriguing possibility that a high star formation efficiency at early times may trigger the onset of intense Ly$α$ emission at redshift $z\lesssim 20$ and produce a cosmic 21-cm absorption signal 200 Myr after the Big Bang.

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arXiv2023

Confirmation and refutation of very luminous galaxies in the early universe

During the first 500 million years of cosmic history, the first stars and galaxies formed, seeding the Universe with heavy elements and eventually reionizing the intergalactic medium. Observations with JWST have uncovered a surprisingly high abundance of candidates for early star-forming galaxies, with distances (redshifts, $z$), estimated from multi-band photometry, as large as $z\approx 16$, far beyond pre-JWST limits. While generally robust, such photometric redshifts can suffer from degeneracies and occasionally catastrophic errors. Spectroscopic measurement is required to validate these sources and to reliably quantify physical properties that can constrain galaxy formation models and cosmology. Here we present JWST spectroscopy that confirms redshifts for two very luminous galaxies with $z > 11$, but also demonstrates that another candidate with suggested $z\approx 16$ instead has $z = 4.9$, with an unusual combination of nebular line emission and dust reddening that mimics the colors expected for much more distant objects. These results reinforce evidence for the early, rapid formation of remarkably luminous galaxies, while also highlighting the necessity of spectroscopic verification. The large abundance of bright, early galaxies may indicate shortcomings in current galaxy formation models, or deviation from physical properties (such as the stellar initial mass function) that are generally believed to hold at later times.

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arXiv2023

Spectroscopic Confirmation of CEERS NIRCam-selected Galaxies at $\boldsymbol{z \simeq 8-10}$

We present JWST/NIRSpec prism spectroscopy of seven galaxies selected from the Cosmic Evolution Early Release Science Survey (CEERS) NIRCam imaging with photometric redshifts z_phot>8. We measure emission line redshifts of z=7.65 and 8.64 for two galaxies, and z=9.77(+0.37,-0.29) and 10.01(+0.14,-0.19) for two others via the detection of continuum breaks consistent with Lyman-alpha opacity from a mostly neutral intergalactic medium. The presence (absense) of strong breaks (strong emission lines) give high confidence that these two galaxies are at z>9.6, but the break-derived redshifts have large uncertainties given the low spectral resolution and relatively low signal-to-noise of the CEERS NIRSpec prism data. The two z~10 sources are relatively luminous (M_UV<-20), with blue continua (-2.3 8 candidates with CEERS NIRSpec spectroscopy do not have secure redshifts, but the absence of emission lines in their spectra is consistent with redshifts z>9.6. We find that z>8 photometric redshifts are generally in agreement (within uncertainties) with the spectroscopic values. However, the photometric redshifts tend to be slightly overestimated (average Delta(z)=0.50+/-0.12), suggesting that current templates do not fully describe the spectra of very high-z sources. Overall, our results solidifies photometric evidence for a high space density of bright galaxies at z>8 compared to theoretical model predictions, and further disfavors an accelerated decline in the integrated UV luminosity density at z>8.

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arXiv2023

A brief review of contrastive learning applied to astrophysics

Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multi-dimensional datasets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years. To do so, it maximizes the agreement between the information extracted from augmented versions of the same input data, making the final representation invariant to the applied transformations. Contrastive Learning is particularly useful in astronomy for removing known instrumental effects and for performing supervised classifications and regressions with a limited amount of available labels, showing a promising avenue towards \emph{Foundation Models}. This short review paper briefly summarizes the main concepts behind contrastive learning and reviews the first promising applications to astronomy. We include some practical recommendations on which applications are particularly attractive for contrastive learning.

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arXiv2023

ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a simulation-based inference of the ex-situ stellar mass fraction of nearby galaxies to different observables -- integrated and spatially-resolved -- and to different galaxy formation models -- IllustrisTNG and EAGLE -- with Machine Learning. We find that at fixed simulation, the fraction of accreted stars can be inferred with very high accuracy, with an error $\sim5$ per cent (10 per cent) from 2D integral-field spectroscopic maps (integrated quantities) throughout the considered stellar mass range. A bias (> 5 per cent) and an increase in scatter by a factor of 2 are introduced when testing with a different simulation, revealing a lack of generalization to distinct galaxy-formation models. Interestingly, upon using only stellar mass and kinematics maps in the central galactic regions for training, we find that this bias is removed and the ex-situ stellar mass fraction can be recovered in both simulations with < 15 per cent scatter, independently of the training set's origin. This opens up the door to a potential robust inference of the accretion histories of galaxies from existing Integral Field Unit surveys, such as MaNGA, covering a similar field of view (FOV) and containing spatially-resolved spectra for tens of thousands of nearby galaxies.

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