arXiv subjects

Christian Kragh Jespersen

Publications and source records attributed to Christian Kragh Jespersen.

27 records · Page 2Linked to original sources

arXiv2024

How the Galaxy-Halo Connection Depends on Large-Scale Environment

We investigate the connection between galaxies, dark matter halos, and their large-scale environments at $z=0$ with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: Explainable Boosting Machines (EBMs) with simple galaxy environment features and $\mathbb{E}(3)$-invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on $3$ Mpc scales. Interpretations via SHapley Additive exPlanations (SHAP) also suggest that, in the context of the TNG300 galaxy-halo connection, simple spherical overdensity on $\sim 3$ Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, $L$, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe ($L=10$ Mpc). We conclude that $3$ Mpc distance scales are most critical for describing the TNG galaxy-halo connection using the spherical overdensity parameterization but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries.

astro-ph.GA↗

arXiv2024

Size - Stellar Mass Relation and Morphology of Quiescent Galaxies at $z\geq3$ in Public $JWST$ Fields

We present the results of a systematic study of the rest-frame optical morphology of quiescent galaxies at $z \geq 3$ using the Near-Infrared Camera (NIRCam) onboard $JWST$. Based on a sample selected by $UVJ$ color or $NUVUVJ$ color, we focus on 26 quiescent galaxies with $9.8<\log{(M_\star/M_\odot)}<11.4$ at $2.8 10.3$ implies that our size - stellar mass relations are below those at lower redshifts, with the amplitude of $\sim0.6\, {\rm kpc}$ at $M_\star = 5\times 10^{10}\, M_\odot$. This value agrees with the extrapolation from the size evolution of quiescent galaxies at $z<3$ in the literature, implying that the size of quiescent galaxies increases monotonically from $z\sim3-5$. Our sample is mainly composed of galaxies with bulge-like structures according to their median Sérsic index and axis ratio of $n\sim3-4$ and $q\sim0.6-0.8$, respectively. On the other hand, there is a trend of increasing fraction of galaxies with low Sérsic index, suggesting $3<z<5$ might be the epoch of onset of morphological transformation with a fraction of very notable disky quenched galaxies.

astro-ph.GA↗

arXiv2023

Leaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here we present a Graph Neural Network-based model that is able to accurately predict key properties of galaxies such as stellar mass, $g-r$ color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range ($z = 0-5$), for all galaxies with stellar masses more massive than $10^9\,M_\odot$ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model.

astro-ph.GA↗

arXiv2023

Efficient survey design for finding high-redshift galaxies with JWST

Several large JWST blank field observing programs have not yet discovered the first galaxies expected to form at $15 \leq z \leq 20$. This has motivated the search for more effective survey strategies that will be able to effectively probe this redshift range. Here, we explore the use of gravitationally lensed cluster fields, that have historically been the most effective discovery tool with HST. In this paper, we analyze the effectiveness of the most massive galaxy clusters that provide the highest median magnification factor within a single JWST NIRCam module in uncovering this population. The results of exploiting these lensing clusters to break the $z > 15$ barrier are compared against the results from large area, blank field surveys such as JADES and CEERS in order to determine the most effective survey strategy for JWST. We report that the fields containing massive foreground galaxy clusters specifically chosen to occupy the largest fraction of a single NIRCam module with high magnification factors in the source plane, whilst containing all multiple images in the image plane within a single module provide the highest probability of both probing the $15 \leq z \leq 20$ regime, as well as discovering the highest redshift galaxy possible with JWST. We also find that using multiple massive clusters in exchange for shallower survey depths is a more time efficient method of probing the $z > 15$ regime.

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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.

astro-ph.CO↗

arXiv2023

Learning the galaxy-environment connection with graph neural networks

Galaxies co-evolve with their host dark matter halos. Models of the galaxy-halo connection, calibrated using cosmological hydrodynamic simulations, can be used to populate dark matter halo catalogs with galaxies. We present a new method for inferring baryonic properties from dark matter subhalo properties using message-passing graph neural networks (GNNs). After training on subhalo catalog data from the Illustris TNG300-1 hydrodynamic simulation, our GNN can infer stellar mass from the host and neighboring subhalo positions, kinematics, masses, and maximum circular velocities. We find that GNNs can also robustly estimate stellar mass from subhalo properties in 2d projection. While other methods typically model the galaxy-halo connection in isolation, our GNN incorporates information from galaxy environments, leading to more accurate stellar mass inference.

astro-ph.IM↗

arXiv2023

An Atlas of Color-selected Quiescent Galaxies at $z>3$ in Public $JWST$ Fields

We present the results of a systematic search for candidate quiescent galaxies in the distant Universe in eleven $JWST$ fields with publicly available observations collected during the first three months of operations and covering an effective sky area of $\sim145$ arcmin$^2$. We homogeneously reduce the new $JWST$ data and combine them with existing observations from the $Hubble\,Space\,Telescope$. We select a robust sample of $\sim80$ candidate quiescent and quenching galaxies at $3 < z < 5$ using two methods: (1) based on their rest-frame $UVJ$ colors, and (2) a novel quantitative approach based on Gaussian Mixture Modeling of the $NUV-U$, $U-V$, and $V-J$ rest-frame color space, which is more sensitive to recently quenched objects. We measure comoving number densities of massive ($M_\star\geq 10^{10.6} M_\odot$) quiescent galaxies consistent with previous estimates relying on ground-based observations, after homogenizing the results in the literature with our mass and redshift intervals. However, we find significant field-to-field variations of the number densities up to a factor of $2-3$, highlighting the effect of cosmic variance and suggesting the presence of overdensities of red quiescent galaxies at $z>3$, as it could be expected for highly clustered massive systems. Importantly, $JWST$ enables the robust identification of quenching/quiescent galaxy candidates at lower masses and higher redshifts than before, challenging standard formation scenarios. All data products, including the literature compilation, are made publicly available.

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arXiv2022

$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees

Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across cosmologically significant volumes, these methods still require significant computation times, representing a barrier to many applications. Graph Neural Networks (GNNs) have recently proven to be the natural choice for learning physical relations. Among the most inherently graph-like structures found in astrophysics are the dark matter merger trees that encode the evolution of dark matter halos. In this paper we introduce a new, graph-based emulator framework, $\texttt{Mangrove}$, and show that it emulates the galactic stellar mass, cold gas mass and metallicity, instantaneous and time-averaged star formation rate, and black hole mass -- as predicted by a SAM -- with root mean squared error up to two times lower than other methods across a $(75 Mpc/h)^3$ simulation box in 40 seconds, 4 orders of magnitude faster than the SAM. We show that $\texttt{Mangrove}$ allows for quantification of the dependence of galaxy properties on merger history. We compare our results to the current state of the art in the field and show significant improvements for all target properties. $\texttt{Mangrove}$ is publicly available.

astro-ph.GA↗

arXiv2021

Finding High-Redshift Galaxies with JWST

One of the primary goals for the upcoming James Webb Space Telescope (JWST) is to observe the first galaxies. Predictions for planned and proposed surveys have typically focused on average galaxy counts, assuming a random distribution of galaxies across the observed field. The first and most massive galaxies, however, are expected to be tightly clustered, an effect known as cosmic variance. We show that cosmic variance is likely to be the dominant contribution to uncertainty for high-redshift mass and luminosity functions, and that median high-redshift and high-mass galaxy counts for planned observations lie significantly below average counts. Several different strategies are considered for improving our understanding of the first galaxies, including adding depth, area, and independent pointings. Adding independent pointings is shown to be the most efficient both for discovering the single highest-redshift galaxy and also for constraining mass and luminosity functions.

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