Search for: All records

Abstract The observed prevalence of galaxies exhibiting bursty star formation histories (SFHs) atz≳ 6 has created new challenges and opportunities for understanding their formation pathways. The degenerate effects of the efficiency and burstiness of star formation on the observed UV luminosity function are separable by galaxy clustering. However, quantifying the timescales of burstiness requires more than just the continuum UV measurements. Here we develop a flexible semi-analytic framework for modeling both the amplitude of star formation rate (SFR) variations and their temporal correlation, from which the luminosity function and clustering can be derived for SFR indicators tracing different characteristic timescales (e.g., UV continuum and Hα luminosities). Based on this framework, we study the prospect of using galaxy summary statistics to distinguish models where SFR fluctuations are prescribed by different power spectral density (PSD) forms. Using the Fisher matrix approach, we forecast the constraints on parameters in our PSD-based model that can be extracted from mock JWST observations of the UV and Hαluminosity functions and clustering bias factors atz∼ 6. If potential confusion due to e.g., dust attenuation and stellar population effects can be properly quantified, these results imply the possibility of probing the burstiness of high-zgalaxies with one-point and two-point statistics and highlight the benefits of combining long-term and short-term SFR tracers. Our flexible framework can be readily extended to characterize the SFH of high-redshift galaxies with a wider range of observational diagnostics. 

Abstract Supernovae (SNe) may be the dominant channel by which dust grains accumulate in galaxies during the first Gyr of cosmic time as formation channels important for lower-redshift galaxies, e.g., asymptotic giant branch stars and grain growth, may not have had sufficient time to take over. SNe produce fewer small grains, leading to a flatter attenuation law. In this work, we fit observations of 138 spectroscopically confirmedz > 6 galaxies adopting standard spectral energy distribution (SED) modeling assumptions and compare standard attenuation law prescriptions to a flat attenuation law. Compared to SMC dust, flat attenuation close to what may be expected from dust produced in SNe yields up to 0.5 mag higherA_Vand 0.4 dex larger stellar masses. It also finds better fits to the rest-frame UV photometry with lower ${\chi }_{\mathrm{UV}}^2$, allowing the observed UV luminosities taken from the models to be fainter by 0.2 dex on average. The systematically fainter observed UV luminosities for fixed observed photometry could help resolve current tension between the ionizing photon production implied by JWST observations and the redshift evolution of the neutral hydrogen fraction. Given these systematic effects and the physical constraint of cosmic time itself, fairly flat attenuation laws that could represent the properties of dust grains produced by SNe should be a standard consideration in fitting to the SEDs ofz > 6 galaxies. 

Abstract We study the cosmological impact of warm, dark-sector relic particles produced as Hawking radiation in a primordial-black-hole-dominated universe before big bang nucleosynthesis. If these dark-sector particles are stable, they would survive to the present day asHawking relicsand modify the growth of cosmological structure. We show that such relics are produced with much larger momenta, but in smaller quantities than the familiar thermal relics considered in standard cosmology. Consequently, Hawking relics with keV–MeV masses affect the growth of large-scale structure in a similar way to eV-scale thermal relics like massive neutrinos. We model their production and evolution, and show that their momentum distributions are broader than comparable relics with thermal distributions. Warm Hawking relics affect the growth of cosmological perturbations and we constrain their abundance to be less than 2% of the dark matter over a broad range of their viable parameter space. Finally, we examine how future measurements of the matter power spectrum can distinguish Hawking relics from thermal particles. 

The 21-cm signal provides a novel avenue to measure the thermal state of the Universe during cosmic dawn and reionization (redshifts $z\sim 5–30$), and thus to probe energy injection from decaying or annihilating dark matter (DM). These DM processes are inherently inhomogeneous: both decay and annihilation are density-dependent, and furthermore, the fraction of injected energy that is deposited at each point depends on the gas ionization and density, leading to further anisotropies in absorption and propagation. In this work, we develop a new framework for modeling the impact of spatially inhomogeneous energy injection and deposition during cosmic dawn, accounting for ionization and baryon density dependence, as well as the attenuation of propagating photons. We showcase how this first completely inhomogeneous treatment affects the predicted 21-cm power spectrum in the presence of exotic sources of energy injection, and forecast the constraints that upcoming HERA measurements of the 21-cm power spectrum will set on DM decays to photons and to electron/positron pairs. These projected constraints considerably surpass those derived from CMB and Lyman- $\alpha$measurements, and for decays to electron/positron pairs they exceed all existing constraints in the sub-GeV mass range, reaching lifetimes of $\sim {10}^{28}\mathrm{s}$. Our analysis demonstrates the unprecedented sensitivity of 21-cm cosmology to exotic sources of energy injection during the cosmic dark ages. Our code, 21cm, includes all these effects and is publicly available in an accompanying release. Published by the American Physical Society2025 

Abstract Recent JWST observations atz > 6 may imply galactic ionizing photon production above prior expectations. Under observationally motivated assumptions about escape fractions, these suggest az ~ 8–9 end to reionization, in tension with thez < 6 end required by the Lyαforest. In this work, we use radiative transfer simulations to understand what different observations tell us about when reionization ended and when it started. We consider a model that ends too early (z_end ≈ 8) alongside two more realistic scenarios withz_end ≈ 5: one starting late (z ~ 9) and another early (z ~ 13). We find that the latter requires up to an order-of-magnitude evolution in galaxy ionizing properties at 6 < z < 12, perhaps in tension with measurements ofξ_ionby JWST, which indicate little evolution. We study how these models compare to recent measurements of the Lyαforest opacity, mean free path, intergalactic medium thermal history, visibility ofz > 8 Lyαemitters, and the patchy kSZ signal from the cosmic microwave background (CMB). We find that neither of the late-ending scenarios is strongly disfavored by any single data set. However, a majority of observables, spanning several distinct types of observations, prefer a late start. Not all probes agree with this conclusion, hinting at a possible lack of concordance arising from deficiencies in observations and/or theoretical modeling. Observations by multiple experiments (including JWST, Roman, and CMB-S4) in the coming years will establish a concordance picture of reionization's beginning or uncover such deficiencies. 

Abstract Detecting the first generation of stars, Population III (Pop III), has been a long-standing goal in astrophysics, yet they remain elusive even in the JWST era. Here we present a novel NIRCam-based selection method for Pop III galaxies, and carefully validate it through completeness and contamination simulations. We systematically search ≃ 500 arcmin²across JWST legacy fields for Pop III candidates, including GLIMPSE, which, assisted by gravitational lensing, has produced JWST’s deepest NIRCam imaging thus far. We discover one promising Pop III galaxy candidate (GLIMPSE-16043) at $z=6.50_{-0.24}^{+0.03}$, a moderately lensed galaxy ( $\mu =2.9_{-0.2}^{+0.1}$) with an intrinsic UV magnitude of $M_{\mathrm{UV}}=-15.89_{-0.14}^{+0.12}$. It exhibits key Pop III features: strong Hαemission (rest-frame EW 2810 ± 550 Å); a Balmer jump; no dust (UV slopeβ = −2.34 ± 0.36); and undetectable metal lines (e.g., [Oiii]; [Oiii]/Hβ < 0.44), implying a gas-phase metallicity ofZ_gas/Z_⊙ < 0.5%. These properties indicate the presence of a nascent, metal-deficient young stellar population (<5 Myr) with a stellar mass of ≃10⁵M_⊙. Intriguingly, this source deviates significantly from the extrapolated UV–metallicity relation derived from recent JWST observations atz= 4–10, consistent with UV enhancement by a top-heavy Pop III initial mass function or the presence of an extremely metal-poor active galactic nucleus. We also derive the first observational constraints on the Pop III UV luminosity function atz ≃ 6–7. The volume density of GLIMPSE-16043 (≈10⁻⁴cMpc⁻³) is in excellent agreement with theoretical predictions, independently reinforcing its plausibility. This study demonstrates the power of our novel NIRCam method to finally reveal distant galaxies even more pristine than the Milky Way’s most metal-poor satellites, thereby promising to bring us closer to the first generation of stars than we have ever been before. 

Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using thearepocode. One suite consists of uniform-box simulations covering a ${\left(25h^{-1}\mathrm{Mpc}\right)}^3$volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales. 

Abstract We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field.By fitting the mass-weighted halo field in theAbacusSummitsimulations atz= 0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2–5% underestimation of the large-scale power and can cause the neural net to overfit.We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space.Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales.Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.