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Valentina La Torre

Publications and source records attributed to Valentina La Torre.

8 recordsLinked to original sources

Toward Unbreaking the Universe: MINERVA Measurements of Color Gradients in Massive Quiescent Galaxies Can Help Ease Too-Early Star Formation Tensions

The discovery of a population of massive, ancient quiescent galaxies within the first 2 Gyr of the Universe's history has led to significant tensions with models of galaxy formation. However, these analyses are often based on slit spectroscopy, which typically captures only the center-most region of these galaxies and, crucially, assumes these cores are representative of the entire galaxy. To illustrate the varying stellar populations present throughout these galaxies, we present an analysis of color gradients in four $z>3$, $\log(M_\star/M_\odot)>11$ quiescent galaxies which previous works have argued are in tension with models. Using medium-band photometry from MINERVA JWST observations, we measure resolved photometry in a series of elliptical annuli out to $0.7^{\prime\prime}$ ($\sim4~R_e$). We find negative color gradients in three galaxies, and for the most extreme color gradient ($Δ(U-V)/ΔR=-0.126\pm0.030~{\rm mag~kpc^{-1}}$), we find the stellar mass is 0.1 dex lower when compared to photometry measured within NIRSpec slits. In the limiting case where these color gradients are entirely driven by age, we find lessened tensions with extreme value statistics models out to $z\sim9.5$, though different stellar population modeling choices also contribute significantly. Ultimately, these findings highlight the need for integral field unit spectroscopy. Spatially-resolved spectra can provide the evidence needed to break the age--dust--metallicity degeneracy, and reliably separate the effects of the observed color gradients from the effects of different physical modeling assumptions on the formation histories of these galaxies.

astro-ph.GA↗

Feeding the Dead: Neutral Gas Inflow in a Long-Quenched Ancient Massive Galaxy at $z\sim 2.7$ Observed with JWST/NIRSpec

We report the spectroscopic detection of neutral gas inflow into a massive ($M_* \simeq 4\times 10^{10} M_\odot$) quiescent galaxy observed at $z_{\rm{spec}} = 2.6576$ with JWST. From the redshifted absorption of the NaI doublet at $λλ5890, 5896 $ Ang, we estimate an inflow velocity $v=278^{+79}_{-79}$ km s$^{-1}$ and a column density $\log(N_{NaI}/\rm{cm^2}) = 13.02^{+0.03}_{-0.03}$. We derive the inflowing mass of the gas $M_{in} = 1.6^{+0.1}_{-0.1} \times 10^8 M_\odot$ and rate $\dot{M}_{in} = 19^{+6}_{-7} \, M_\odot \, \rm{yr}^{-1}$. The presence of several surrounding galaxies suggests that the galaxy may be accreting gas from nearby companions. However, we cannot confirm it with current data and the intergalactic medium or cosmic filaments are also viable sources of the inflowing gas. Despite the ongoing inflow, the galaxy remains quiescent, with an upper limit to the star formation rate of $0.2 \, M_\odot \, \rm{yr}^{-1}$. Moreover, its star formation history suggests that the galaxy has remained quiescent during the past $\sim1$ Gyr, with half of its stars formed by redshift $z_{50}=11^{+18}_{-3}$. We discuss that the inflow is not massive, dense, or long-lived enough to ignite significant star formation, or it is fueling low-level AGN activity instead. This is direct evidence that quiescent galaxies can accrete cold gas after their quenching while keeping their star formation subdued. Follow-up observations with JWST and ALMA will be needed to constraint the nature of the inflowing gas.

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MINERVA: A NIRCam Medium Band and MIRI Imaging Survey to Unlock the Hidden Gems of the Distant Universe

We present an overview of the MINERVA survey, a 259.8 hour (prime) and 127 hour (parallel) Cycle 4 treasury program on the James Webb Space Telescope (JWST). MINERVA is obtaining 8 filter NIRCam medium band imaging (F140M, F162M, F182M, F210M, F250M, F300M, F360M, F460M) and 2 filter MIRI imaging (F1280W, F1500W) in four of the five CANDELS Extragalactic fields: UDS, COSMOS, AEGIS and GOODS-N. These fields were previously observed in Cycle 1 with 7 - 9 NIRCam filters by the PRIMER, CEERS and JADES programs. MINERVA reaches a 5$σ$ depth of 28.1 mag in F300M and covers $\sim$ 542 arcmin$^2$, increasing the area of existing JWST medium-band coverage in at least 8 bands by $\sim$ 7$\times$. The MIRI imaging reaches a 5$σ$ depth of 23.9 mag in F1280W and covers $\sim$ 275 arcmin$^2$ in at least 2 MIRI filters. When combined with existing imaging, these data will provide a photometric catalog with 20-26 JWST filters (depending on field) and 26-35 filters total, including HST. This paper presents a detailed breakdown of the filter coverage, exposure times, and field layout relative to previous observations, as well as an overview of the primary science goals of the project. These include uncovering the physics of enigmatic sources hiding in current broadband catalogs, improving systematics on stellar mass functions and number densities by factors of $\gtrsim$ 3, and resolved mapping of stellar mass and star formation at 1 $< z <$ 6. When complete, MINERVA will become an integral part of the treasury deep field imaging datasets, significantly improving population studies with well-understood completeness, robust photometric redshifts, stellar masses, and sizes, and facilitating spectroscopic follow up for decades to come.

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HOLESOM: Constraining the Properties of Slowly-Accreting Massive Black Holes with Self-Organizing Maps

Accreting massive black holes (MBHs, with M$_\bullet > 10^3$ M$_{\odot}$) are known for their panchromatic emission, spanning from radio to gamma rays. While MBHs accreting at significant fractions of their Eddington rate are readily detectable, those accreting at much lower rates in radiatively inefficient modes often go unnoticed, blending in with other astrophysical sources. This challenge is particularly relevant for gas-starved MBHs in external galaxies and those possibly wandering in the Milky Way. We present HOLESOM, a machine learning-powered tool based on the Self-Organizing Maps (SOMs) algorithm, specifically designed to identify slowly-accreting MBHs using sparse photometric data. Trained on a comprehensive set of $\sim$ 20, 000 spectral energy distributions (SEDs), HOLESOM can (i) determine if the photometry of a source is consistent with slowly-accreting MBHs and (ii) estimate its black hole mass and Eddington ratio, including uncertainties. We validate HOLESOM through extensive tests on synthetic data and real-world cases, including Sagittarius A* (Sgr A*), demonstrating its effectiveness in identifying slowly-accreting MBHs. Additionally, we derive analytical relations between radio and X-ray luminosities to further constrain physical parameters. The primary strength of HOLESOM lies in its ability to accurately identify MBH candidates, which can then be targeted for follow-up photometric and spectroscopic observations. Fast and scalable, HOLESOM offers a robust framework for automatically scanning large multi-wavelength datasets, making it a valuable tool for unveiling hidden MBH populations in the local Universe.

astro-ph.HE↗

Estimating Galaxy Parameters with Self-Organizing Maps and the Effect of Missing Data

The current and upcoming large data volume galaxy surveys require the use of machine learning techniques to maximize their scientific return. This study explores the use of Self-Organizing Maps (SOMs) to estimate galaxy parameters with a focus on handling cases of missing data and providing realistic probability distribution functions for the parameters. We train a SOM with a simulated mass-limited lightcone assuming a ugrizYJHKs+IRAC dataset, mimicking the Hyper Suprime-Cam (HSC) Deep joint dataset. For parameter estimation, we derive SOM likelihood surfaces considering photometric errors to derive total (statistical and systematic) uncertainties. We explore the effects of missing data including which bands are particular critical to the accuracy of the derived parameters. We demonstrate that the parameter recovery is significantly better when the missing bands are "filled-in" rather than if they are completely omitted. We propose a practical method for such recovery of missing data.

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Cosmology with one galaxy?

Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of $Ω_{\rm m}$, at fixed $Ω_{\rm b}$, with a $\sim10\%$ precision, while no constraint can be placed on $σ_8$. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, $z\leq3$, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of $Ω_{\rm m}$. We believe that our results can be explained taking into account that changes in the value of $Ω_{\rm m}$, or potentially $Ω_{\rm b}/Ω_{\rm m}$, affect the dark matter content of galaxies. That effect leaves a distinct signature in galaxy properties to the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.

astro-ph.CO↗

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

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The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations

We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{\rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Ω_m$, $σ_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{\rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.

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