Search

JWST has revealed diverse new populations of high-redshift ($z\sim4-11$) AGN and extreme star-forming galaxies that challenge current models. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. From a parent sample of 205 $\mathrm{z_{spec}}>3$ UNCOVER galaxies with NIRSpec/PRISM follow-up, we identify 12 C IV, He II, and C III] emitters. Leveraging the combined rest-optical and UV coverage of PRISM, we limit the emission-line model space using the sample's [O III]/H$\beta$ distribution, significantly decreasing the overlap between AGN and star-formation models in the UV diagnostics. We then find that the five He II emitters are the strongest AGN candidates, with further support from two [Ne V] detections and one X-ray detection from Chandra. We cannot robustly quantify the AGN fraction in this sample, but we note that close to 20% of $\mathrm{M_{*}>2\times10^{9}\,M_{\odot}}$ parent sample galaxies are AGN candidates. The lower-mass line emitters, which are consistent with both AGN and star-forming photoionization models, have more compact sizes and higher specific star formation rates than the parent sample. Higher-resolution and deeper data on these UV line emitters should provide much stronger constraints on the obscured AGN fraction at $z > 3$., Comment: Submitted to ApJ

We explore the physical properties of five massive quiescent galaxies at $z\sim2.5$, revealing the presence of non-negligible dust reservoirs. JWST NIRSpec observations were obtained for each target, finding no significant line emission; multiple star formation tracers independently place upper limits between $0.1-10~M_\odot / \mathrm{yr}$. Spectral energy distribution modeling with Prospector infers stellar masses between $\log_{10}[M / M_\odot] \sim 10-11$ and stellar mass-weighted ages between $1-2$ Gyr. The inferred mass-weighted effective radii ($r_{eff}\sim 0.4-1.4$ kpc) and inner $1$ kpc stellar surface densities ($\log_{10}[\Sigma / M_\odot \mathrm{kpc}^2 ]\gtrsim 9$) are typical of quiescent galaxies at $z \gtrsim 2$. The galaxies display negative color gradients (redder core and bluer outskirts); for one galaxy, this effect results from a dusty core, while for the others it may be evidence of an "inside-out" growth process. Unlike local quiescent galaxies, we identify significant reddening in these typical cosmic noon passive galaxies; all but one require $A_V \gtrsim 0.4$. This finding is in qualitative agreement with previous studies but our deep 20-band NIRCam imaging is able to significantly suppress the dust-age degeneracy and confidently determine that these galaxies are reddened. We speculate about the physical effects that may drive the decline in dust content in quiescent galaxies over cosmic time., Comment: 18 pages, 8 figures, submitted to ApJ

In this paper, we introduce a novel star partitioning problem for simple connected graphs $G=(V,E)$. The goal is to find a partition of the edges into stars that minimizes the maximum number of stars a node is contained in while simultaneously satisfying node-specific capacities. We design and analyze an efficient polynomial time algorithm with a runtime of $\mathcal{O}(|E|^2)$ that determines an optimal partition. Moreover, we explicitly provide a closed form of an optimal value for some graph classes. We generalize our algorithm to find even an optimal star partition of linear hypergraphs, multigraphs, and graphs with self-loop. We use flow techniques to design an algorithm for the star partitioning problem with an improved runtime of $\mathcal{O}(\log(\Delta) \cdot |E| \cdot \min\{|V|^{\frac{2}{3}},|E|^{\frac{1}{2}}\})$, where $\Delta$ is maximum node degree in $G$. In contrast to the unweighted setting, we show that a node-weighted decision variant of this problem is \texttt{strongly NP-complete} even without capacity constraints. Furthermore, we provide an extensive comparison to the problem of minimizing the minimum indegree satisfying node capacity constraints., Comment: 19 pages, 7 figures, ALGOWIN 2024

We present the design and observations of low resolution JWST/NIRSpec PRISM spectroscopy from the Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization (UNCOVER) Cycle 1 JWST Treasury program. Targets are selected using JWST/NIRCam photometry from UNCOVER and other programs, and cover a wide range of categories and redshifts to ensure the legacy value of the survey. These categories include the first galaxies at $z\gtrsim10$, faint galaxies during the Epoch of Reionization ($z\sim6-8$), high redshift AGN ($z\gtrsim6$), Population III star candidates, distant quiescent and dusty galaxies ($1\lesssim z \lesssim 6$), and filler galaxies sampling redshift--color--magnitude space from $z\sim 0.1-13$. Seven NIRSpec MSA masks across the extended Abell 2744 cluster were observed, along with NIRCam parallel imaging in 8 filters (F090W, F115W, F150W, F200W, F277W, F356W, F410M, F444W, F480M) over a total area of ~26 arcmin$^2$, overlapping existing HST coverage from programs including the Hubble Frontier Fields and BUFFALO. We successfully observed 553 objects down to $m_{\mathrm{F444W}}\sim30\mathrm{AB}$, and by leveraging mask overlaps, we reach total on-target exposure times ranging from 2.4-16.7h. We demonstrate the success rate and distribution of confirmed redshifts, and also highlight the rich information revealed by these ultradeep spectra for a subset of our targets. An updated lens model of Abell 2744 is also presented, including 14 additional spectroscopic redshifts and finding a total cluster mass of $M_{\mathrm{SL}}=(2.1\pm0.3)\times10^{15}\,\mathrm{M}_{\odot}$. We publicly release reduced 1D and 2D spectra for all objects observed in Summer 2023 along with a spectroscopic redshift catalog and the updated lens model of the cluster (https://jwst-uncover.github.io/DR4.html)., Comment: 19 pages, 9 figures, 4 tables, submitted to ApJ, comments welcome! Data available at: https://jwst-uncover.github.io/DR4.html (v2: figure format correction)

This paper provides a comprehensive overview of the uD3TN project's development, detailing its transformation into a flexible and modular software implementation of the Delay-/Disruption-Tolerant Networking (DTN) Bundle Protocol. Originating from uPCN, designed for microcontrollers, uD3TN has undergone significant architectural refinement to increase flexibility, compatibility, and performance across various DTN applications. Key developments include achieving platform independence, supporting multiple Bundle Protocol versions concurrently, introducing abstract Convergence Layer Adapter (CLA) interfaces, and developing the so called Application Agent Protocol (AAP) for interaction with the application layer. Additional enhancements, informed by field tests, include Bundle-in-Bundle Encapsulation and exploring a port to the Rust programming language, indicating the project's ongoing adaptation to practical needs. The paper also introduces the Generic Bundle Forwarding Interface and AAPv2, showcasing the latest innovations in the project. Moreover, it provides a comparison of uD3TN's architecture with the Interplanetary Overlay Network (ION) protocol stack, highlighting some general architectural principles at the foundation of DTN protocol implementations.

The mass-metallicity relation (MZR) provides crucial insights into the baryon cycle in galaxies and provides strong constraints on galaxy formation models. We use JWST NIRSpec observations from the UNCOVER program to measure the gas-phase metallicity in a sample of eight galaxies during the epoch of reionization at $z=6-8$. Thanks to strong lensing of the galaxy cluster Abell 2744, we are able to probe extremely low stellar masses between $10^{6}$ and $10^{8} M_{\odot}$. Using strong lines diagnostics and the most recent JWST calibrations, we derive extremely-low oxygen abundances ranging from 12+log(O/H)=6.7 to 7.8. By combining this sample with more massive galaxies at similar redshifts, we derive a best-fit relation of 12+{\rm log(O/H)}=$0.39_{-0.02}^{+0.02} \times$ log(\mstar) $+ 4.52_{-0.17}^{+0.17}$, which is steeper than determinations at $z \sim 3$. Our results show a clear redshift evolution in the overall normalization of the relation, galaxies at higher redshift having significantly lower metallicities at a given mass. A comparison with theoretical models provides important constraints on which physical processes, such as metal mixing, star formation or feedback recipes, are important in reproducing the observations. Additionally, these galaxies exhibit star formation rates that are higher by a factor of a few to tens compared to extrapolated relations at similar redshifts or theoretical predictions of main-sequence galaxies, pointing to a recent burst of star formation. All these observations are indicative of highly stochastic star formation and ISM enrichment, expected in these low-mass systems, suggesting that feedback mechanisms in high-$z$ dwarf galaxies might be different from those in place at higher masses., Comment: Submitted to ApJL

Parametric converters are parametric amplifiers that mix two spatially separate nondegenerate modes and are commonly used for amplifying and squeezing microwave signals in quantum computing and sensing. In Josephson parametric converters, the strong localized nonlinearity of the Josephson Junction limits the amplification and squeezing, as well as the dynamic range, in current devices. In contrast, a weak distributed nonlinearity can provide higher gain and dynamic range, when implemented as a kinetic inductance (KI) nanowire of a dirty superconductor, and has additional benefits such as resilience to magnetic field, higher-temperature operation, and simplified fabrication. Here, we propose, demonstrate, and analyze the performance of a KI parametric converter that relies on the weak distributed nonlinearity of a KI nanowire. The device utilizes three-wave mixing induced by a DC current bias. We demonstrate its operation as a nondegenerate parametric amplifier with high phase-sensitive gain, reaching two-mode amplification and deamplification of $\sim$30 dB for two resonances separated by 0.8 GHz, in excellent agreement with our theory of the device. We observe a dynamic range of -108~dBm at 30 dB gain. Our device can significantly broaden applications of quantum-limited signal processing devices including phase-preserving amplification and two-mode squeezing., Comment: 6 pages, 4 figures. Accepted for publication in Physical Review Applied

Recent observations with the James Webb Space Telescope (JWST) have uncovered unexpectedly high cosmic star formation activity in the early Universe, mere hundreds of millions of years after the Big Bang. These observations are often understood to reflect an evolutionary shift in star formation efficiency (SFE) caused by changing galactic conditions during these early epochs. We present FIREbox-HR, a high-resolution, cosmological hydrodynamical simulation from the Feedback in Realistic Environments project, which offers insights into the SFE of galaxies during the first billion years of cosmic time. FIREbox-HR re-simulates the cosmic volume (L = 22.1 cMpc) of the original FIREbox run with eight times higher mass resolution (m_b ~ 7800 M_sun), but with identical physics, down to z ~ 6. FIREbox-HR predicts ultraviolet (UV) luminosity functions in good agreement with available observational data. The simulation also successfully reproduces the observed cosmic UV luminosity density at z ~ 6 - 14, demonstrating that relatively high star formation activity in the early Universe is a natural outcome of the baryonic processes encoded in the FIRE-2 model. According to FIREbox-HR, the SFE - halo mass relation for intermediate mass halos (M_halo ~ 10^9 - 10^11 M_sun) does not significantly evolve with redshift and is only weakly mass-dependent. These properties of the SFE - halo mass relation lead to a larger contribution from lower mass halos at higher z, driving the gradual evolution of the observed cosmic UV luminosity density. A theoretical model based on the SFE - halo mass relation inferred from FIREbox-HR allows us to explore implications for galaxy evolution. Future observations of UV faint galaxies at z > 12 will provide an opportunity to further test these predictions and deepen our understanding of star formation during Cosmic Dawn., Comment: 26 pages, 14 figures, 5 tables, submitted to MNRAS, comments welcome

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments., Comment: Main body: 17 pages, 4 Figures. SI: 11 pages, 3 Figures. Under review at Geophysical Research Letters

In this work, we combine the many-body formulation of the internally contracted multireference coupled cluster (ic-MRCC) method with Evangelista's multireference formulation of the driven similarity renormalization group (DSRG). The DSRG method can be viewed as a unitary multireference coupled cluster theory, which renormalizes the amplitudes based on a flow equation approach to eliminate numerical instabilities. We extend this approach by demonstrating that the unitary flow equation approach can be adapted for nonunitary transformations, rationalizing the renormalization of ic-MRCC amplitudes. We denote the new approach, the renormalized ic-MRCC (ric-MRCC) method. To achieve high accuracy with a reasonable computational cost, we introduce a new approximation to the Baker-Campbell-Hausdorff expansion. We fully consider the linear commutator while approximating the quadratic commutator, for which we neglect specific contractions involving amplitudes with active indices. Moreover, we introduce approximate perturbative triples to obtain the ric-MRCCSD[T] method. We demonstrate the accuracy of our approaches in comparison to advanced multireference methods for the potential energy curves of H8, F2, H2O, N2, and Cr2. Additionally, we show that ric-MRCCSD and ric-MRCSSD[T] match the accuracy of CCSD(T) for evaluating spectroscopic constants and of full configuration interaction energies for a set of small molecules., Comment: 59 pages, 4 figures, 6 tables

A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data. We can create a QNN by parametrizing a quantum process and then using it to model unknown relations between quantum states. In this paper, we explore how to use measurement-based quantum computation for quantum machine learning problems and propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA). Using the proposed QNN, we solve several tasks, including learning a universal set of gates, optimizing measurement with post-processing, learning a quantum instrument, and the classification of classical data. Finally, we discuss how to train an ansatz under the hardware constraints imposed by photonic Gottesman-Kitaev-Preskill qubits. Our work demonstrates the feasibility of using measurement-based quantum computation as a framework for quantum machine learning algorithms., Comment: 10 pages, 11 figures

In this paper, we describe the "Medium Bands, Mega Science" JWST Cycle 2 survey (JWST-GO-4111) and demonstrate the power of these data to reveal both the spatially-integrated and spatially-resolved properties of galaxies from the local universe to the era of cosmic dawn. Executed in November 2023, MegaScience obtained ~30 arcmin^2 of deep multiband NIRCam imaging centered on the z~0.3 Abell 2744 cluster, including eleven medium-band filters and the two shortest-wavelength broad-band filters, F070W and F090W. Together, MegaScience and the UNCOVER Cycle 1 treasury program provide a complete set of deep (~28-30 mag) images in all NIRCam medium- and broad-band filters. This unique dataset allows us to precisely constrain photometric redshifts, map stellar populations and dust attenuation for large samples of distant galaxies, and examine the connection between galaxy structures and formation histories. MegaScience also includes ~17 arcmin^2 of NIRISS parallel imaging in two broad-band and four medium-band filters from 0.9-4.8um, expanding the footprint where robust spectral energy distribution (SED) fitting is possible. We provide example SEDs and multi-band cutouts at a variety of redshifts, and use a catalog of JWST spectroscopic redshifts to show that MegaScience improves both the scatter and catastrophic outlier rate of photometric redshifts by factors of 2-3. Additionally, we demonstrate the spatially-resolved science enabled by MegaScience by presenting maps of the [OIII] line emission and continuum emission in three spectroscopically-confirmed z>6 galaxies. We show that line emission in reionization-era galaxies can be clumpy, extended, and spatially offset from continuum emission, implying that galaxy assembly histories are complex even at these early epochs. We publicly release fully reduced mosaics and photometric catalogs for both the NIRCam primary and NIRISS parallel fields., Comment: 21 pages, 10 figures. Fully reduced imaging, photometric catalogs, and photometric redshift fits publicly available at https://jwst-uncover.github.io/megascience/

In this work, we investigate the possibility of improving multireference-driven coupled cluster (CC) approaches with an algorithm that iteratively combines complete active space (CAS) calculations with tailored CC and externally corrected CC. This is accomplished by establishing a feedback loop between the CC and CAS parts of a calculation through similarity transformation of the Hamiltonian with those CC amplitudes that are not encompassed by the active space. We denote this approach the complete active space iterative coupled cluster (CASiCC) ansatz. We investigate its efficiency and accuracy in the singles and doubles approximation by studying the prototypical molecules H4, H8, H2O, and N2. Our results demonstrate that CASiCC systematically improves on the single-reference CCSD and the ecCCSD methods across entire potential energy curves, while retaining modest computational costs. However, the tailored coupled cluster method shows superior performance in the strong correlation regime suggesting that its accuracy is based on error compensation. We find that the iterative version of externally corrected and tailored coupled cluster methods converge to the same results., Comment: 38 pages, 7 figures, 1 table

Our ability to trace the star-forming molecular gas is important to our understanding of the Universe. We can trace this gas using CO emission, converting the observed CO intensity into the H$_2$ gas mass of the region using the CO-to-H$_2$ conversion factor (Xco). In this paper, we use simulations to study the conversion factor and the molecular gas within galaxies. We analysed a suite of simulations of isolated disc galaxies, ranging from dwarfs to Milky Way-mass galaxies, that were run using the FIRE-2 subgrid models coupled to the CHIMES non-equilibrium chemistry solver. We use the non-equilibrium abundances from the simulations, and we also compare to results using abundances assuming equilibrium, which we calculate from the simulation in post-processing. Our non-equilibrium simulations are able to reproduce the relation between CO and H$_2$ column densities, and the relation between Xco and metallicity, seen within observations of the Milky Way. We also compare to the xCOLD GASS survey, and find agreement with their data to our predicted CO luminosities at fixed star formation rate. We also find the multivariate function used by xCOLD GASS overpredicts the H$_2$ mass for our simulations, motivating us to suggest an alternative multivariate function of our fitting, though we caution that this fitting is uncertain due to the limited range of galaxy conditions covered by our simulations. We also find that the non-equilibrium chemistry has little effect on the conversion factor (<5\%) for our high-mass galaxies, though still affects the H$_2$ mass and Lco by $\approx$25\%., Comment: 19 pages, 16 figures (including appendices). Accepted for publication in MNRAS

The unprecedented infrared spectroscopic capabilities of JWST have provided high-quality interstellar medium (ISM) metallicity measurements and enabled characterization of the gas-phase mass-metallicity relation (MZR) for galaxies at $z \gtrsim 5$ for the first time. We analyze the gas-phase MZR and its evolution in a high-redshift suite of FIRE-2 cosmological zoom-in simulations at $z=5-12$ and for stellar masses $M_* \sim 10^6-10^{10} \rm{M}_\odot$. These simulations implement a multi-channel stellar feedback model and produce broadly realistic galaxy properties, including when evolved to $z=0$. The simulations predict very weak redshift evolution of the MZR over the redshift range studied, with the normalization of the MZR increasing by less than $0.01$ dex as redshift decreases from $z = 12$ to $z=5$. The median MZR in the simulations is well-approximated as a constant power-law relation across this redshift range given by $\log(Z/Z_\odot) = 0.37\log(M_*/\rm{M}_\odot) - 4.3$. We find good agreement between our best-fit model and recent observations made by JWST at high redshift. The weak evolution of the MZR at $z > 5$ contrasts with the evolution at $z \lesssim 3$, where increasing normalization of the MZR with decreasing redshift is observed and predicted by most models. The FIRE-2 simulations predict increasing scatter in the gas-phase MZR with decreasing stellar mass, in qualitative agreement with some observations., Comment: 13 pages, 6 figures

In high-density crowds, local motion can propagate, amplify, and lead to macroscopic phenomena, including 'density waves'. These density waves only occur when individuals interact, and impulses are transferred to neighbours. How this impulse is passed on by the human body and which effects this has on individuals is still not fully understood. To further investigate this, experiments focusing on the propagation of a push were conducted. In the experiments the crowd is greatly simplified by five people lining up in a row. The rearmost person in the row was pushed forward in a controlled manner with a punching bag. The intensity of the push, the initial distance between participants and the initial arm posture were varied. Collected data included side view and top view video recordings, head trajectories, 3D motion using motion capturing (MoCap) suits as well as pressure measured at the punching bag. With a hybrid tracking algorithm, the MoCap data are combined with the head trajectories to allow an analysis of the motion of each limb in relation to other persons. The observed motion of the body in response to the push can be divided into three phases. These are (i) receiving an impulse, (ii) receiving and passing on an impulse, and (iii) passing on an impulse. Using the 3D MoCap data, we can identify the start and end times of each phase. To determine when a push is passed on, the forward motion of the person in front has to be considered. The projection of the center of mass relative to the initial position of the feet is a measure of the extent to which a person is displaced from the rest position. Specifying the timing of these phases is particularly important to distinguish between different types of physical interactions. Our results contribute to the development and validation of a pedestrian model for identifying risks due to motion propagation in dense crowds., Comment: Submitted to Safety Science

In the Tricolored Euclidean Traveling Salesperson problem, we are given~$k=3$ sets of points in the plane and are looking for disjoint tours, each covering one of the sets. Arora (1998) famously gave a PTAS based on ``patching'' for the case $k=1$ and, recently, Dross et al.~(2023) generalized this result to~$k=2$. Our contribution is a $(5/3+\epsilon)$-approximation algorithm for~$k=3$ that further generalizes Arora's approach. It is believed that patching is generally no longer possible for more than two tours. We circumvent this issue by either applying a conditional patching scheme for three tours or using an alternative approach based on a weighted solution for $k=2$.

In this paper we reassess the parameterized complexity and approximability of the well-studied Steiner Forest problem in several graph classes of bounded width. The problem takes an edge-weighted graph and pairs of vertices as input, and the aim is to find a minimum cost subgraph in which each given vertex pair lies in the same connected component. It is known that this problem is APX-hard in general, and NP-hard on graphs of treewidth 3, treedepth 4, and feedback vertex set size 2. However, Bateni, Hajiaghayi and Marx [JACM, 2011] gave an approximation scheme with a runtime of $n^{O(\frac{k^2}{\varepsilon})}$ on graphs of treewidth $k$. Our main result is a much faster efficient parameterized approximation scheme (EPAS) with a runtime of $2^{O(\frac{k^2}{\varepsilon} \log \frac{k^2}{\varepsilon})} \cdot n^{O(1)}$. If $k$ instead is the vertex cover number of the input graph, we show how to compute the optimum solution in $2^{O(k \log k)} \cdot n^{O(1)}$ time, and we also prove that this runtime dependence on $k$ is asymptotically best possible, under ETH. Furthermore, if $k$ is the size of a feedback edge set, then we obtain a faster $2^{O(k)} \cdot n^{O(1)}$ time algorithm, which again cannot be improved under ETH.

We report the spectroscopic confirmation of a massive ($\log(M_\star/M_\odot)=10.34 \pm_{0.07}^{0.06}$), HST-dark ($m_\mathrm{F150W} - m_\mathrm{F444W} = 3.6$) quiescent galaxy at $z_{spec}=3.97$ in the UNCOVER survey. NIRSpec/PRISM spectroscopy and a non-detection in deep ALMA imaging surprisingly reveals that the galaxy is consistent with a low ($<$10 $M_\odot \ \mathrm{yr^{-1}}$) star formation rate despite evidence for moderate dust attenuation. The F444W image is well modeled with a two component \sersic fit that favors a compact, $r_e\sim200$ pc, $n\sim2.9$ component and a more extended, $r_e\sim1.6$ kpc, $n\sim1.7$ component. The galaxy exhibits strong color gradients: the inner regions are significantly redder than the outskirts. Spectral energy distribution models that reproduce both the red colors and low star formation rate in the center of UNCOVER 18407 require both significant ($A_v\sim1.4$ mag) dust attenuation and a stellar mass-weighted age of 900 Myr, implying 50\% of the stars in the core already formed by $z=7.5$. Using spatially resolved annular mass-to-light measurements enabled by the galaxy's moderate magnification ($\mu=2.12\pm_{0.01}^{0.05}$) to reconstruct a radial mass profile from the best-fitting two-component \sersic model, we infer a total mass-weighted $r_\mathrm{eff} = 0.72 \pm_{0.11}^{0.15}$ kpc and log$(\Sigma_\mathrm{1 kpc} \ [\mathrm{M_\odot/kpc^2}]) = 9.61 \pm_{0.10}^{0.08}$. The early formation of a dense, low star formation rate, and dusty core embedded in a less attenuated stellar envelope suggests an evolutionary link between the earliest-forming massive galaxies and their elliptical descendants. Furthermore, the disparity between the global, integrated dust properties and the spatially resolved gradients highlights the importance of accounting for radially varying stellar populations when characterizing the early growth of galaxy structure., Comment: 17 pages, 9 figures, 2 tables. Resubmitted to ApJ after response to referee and update to include new medium band imaging from the JWST MEGASCIENCE program. Comments welcome!

Observations show a tight correlation between the stellar mass of galaxies and their gas-phase metallicity (MZR). This relation evolves with redshift, with higher-redshift galaxies being characterized by lower metallicities. Understanding the physical origin of the slope and redshift evolution of the MZR may provide important insight into the physical processes underpinning it: star formation, feedback, and cosmological inflows. While theoretical models ascribe the shape of the MZR to the lower efficiency of galactic outflows in more massive galaxies, what drives its evolution remains an open question. In this letter, we analyze how the MZR evolves over $z=0-3$, combining results from the FIREbox cosmological volume simulation with analytical models. Contrary to a frequent assertion in the literature, we find that the evolution of the gas fraction does not contribute significantly to the redshift evolution of the MZR. Instead, we show that the latter is driven by the redshift-dependence of the inflow metallicity, outflow metallicity, and mass loading factor, whose relative importance depends on stellar mass. These findings also suggest that the evolution of the MZR is not explained by galaxies moving along a fixed surface in the space spanned by stellar mass, gas phase metallicity, and star formation rate., Comment: 6 pages, 4 figures. Submitted for publication on MNRAS

When selecting data for training large-scale models, standard practice is to filter for examples that match human notions of data quality. Such filtering yields qualitatively clean datapoints that intuitively should improve model behavior. However, in practice the opposite can often happen: we find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. To develop better methods for selecting data, we start by framing dataset selection as an optimization problem that we can directly solve for: given target tasks, a learning algorithm, and candidate data, select the subset that maximizes model performance. This framework thus avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks. Our resulting method greatly improves language model (LM) performance on both pre-specified tasks and previously unseen tasks. Specifically, choosing target tasks representative of standard LM problems and evaluating on diverse held-out benchmarks, our selected datasets provide a 2x compute multiplier over baseline methods.

With its sensitivity in the rest-frame optical, the James Webb Space Telescope (JWST) has uncovered active galactic nuclei (AGN), comprising both intrinsically faint and heavily reddened sources, well into the first billion years of the Universe, at $z \sim 4-11$. In this work, we revisit the AGN contribution to reionization given the high number densities associated with these objects. We use the DELPHI semi-analytic model, base-lined against the latest high-redshift datasets from the JWST and the Atacama Large millimetre Array (ALMA) to model early star forming galaxies and AGN. We calculate the escape fractions of ionizing radiation from both star formation and AGN and include the impact of reionization feeback in suppressing the baryonic content of low-mass galaxies in ionized regions. This model is validated against the key observables for star forming galaxy, AGN and reionization. In our {\it fiducial} model, reionization reaches its mid-point at $z \sim 6.9$ and ends by $z \sim 5.9$. Low stellar mass ($M_*\leq 10^9M_\odot$) star forming galaxies are found to be the key drivers of the reionization process, providing about $77\%$ of the total photon budget. Despite their high numbers, high accretion rates and higher escape fractions compared to star forming galaxies at $z \sim 5$, AGN only provide about $23\%$ of the total reionization budget which is dominated by black holes in high stellar mass systems (with $M_* \geq 10^9M_\odot$). This is because AGN number densities become relevant only at $z \leq 7$ - as a result, AGN contribute as much as galaxies as late as $z \sim 6.2$, when reionization is already in its end stages. Finally, we find that even contrasting models of the AGN ionizing photon escape fraction (increasing or decreasing with stellar mass) do not qualitatively change our results., Comment: Submitted to A&A; comments welcome

The capabilities of monocular depth estimation (MDE) models are limited by the availability of sufficient and diverse datasets. In the case of MDE models for autonomous driving, this issue is exacerbated by the linearity of the captured data trajectories. We propose a NeRF-based data augmentation pipeline to introduce synthetic data with more diverse viewing directions into training datasets and demonstrate the benefits of our approach to model performance and robustness. Our data augmentation pipeline, which we call \textit{NeRFmentation}, trains NeRFs on each scene in a dataset, filters out subpar NeRFs based on relevant metrics, and uses them to generate synthetic RGB-D images captured from new viewing directions. In this work, we apply our technique in conjunction with three state-of-the-art MDE architectures on the popular autonomous driving dataset, KITTI, augmenting its training set of the Eigen split. We evaluate the resulting performance gain on the original test set, a separate popular driving dataset, and our own synthetic test set.

We present a measurement of the low-mass quiescent size-mass relation at Cosmic Noon (1

Over the last decade, hidden Markov models (HMMs) have become increasingly popular in statistical ecology, where they constitute natural tools for studying animal behavior based on complex sensor data. Corresponding analyses sometimes explicitly focus on - and in any case need to take into account - periodic variation, for example by quantifying the activity distribution over the daily cycle or seasonal variation such as migratory behavior. For HMMs including periodic components, we establish important mathematical properties that allow for comprehensive statistical inference related to periodic variation, thereby also providing guidance for model building and model checking. Specifically, we derive the periodically varying unconditional state distribution as well as the time-varying and overall state dwell-time distributions - all of which are of key interest when the inferential focus lies on the dynamics of the state process. We use the associated novel inference and model-checking tools to investigate changes in the diel activity patterns of fruit flies in response to changing light conditions., Comment: 33 pages, 11 Figures

We investigate for the first time a certain class of non-factorisable contributions of the four-quark operators ${\cal O}_{3-6}$ in the weak effective Hamiltonian to the $\Lambda_b \to \Lambda \ell^+\ell^-$ decay amplitude. We focus on the case where a virtual photon is radiated from one of the light constituents of the $\Lambda_b$ baryon, in the kinematic situation of large hadronic recoil with an energetic $\Lambda$ baryon in the final state. The effect on the suitably defined ``non-local form factors'' is calculated using the light-cone sum rule approach for a correlator with an interpolating current for the light $\Lambda$ baryon. We find that this approach requires the introduction of new soft functions that generalise the standard light-cone distribution amplitudes (LCDAs) for the heavy $\Lambda_b$ baryon. We give a heuristic discussion of their properties and a model that relates them to the standard LCDAs. Within this framework, we provide numerical results for the size of the non-local form factors considered., Comment: 28 pages, 2 figures, references added

We review recent progress in the computation of structure-dependent QED corrections to exclusive $B$ decays in the factorization approach., Comment: Invited review for EPJST "$b$-quark physics as a precision laboratory: status and future prospects'', 31 pages, 3 figures

A kinematic misalignment of the stellar and gas components is a phenomenon observed in a significant fraction of galaxies. However, the underlying physical mechanisms are not well understood. A commonly proposed scenario for the formation of a misaligned component requires any pre-existing gas disc to be removed, via fly-bys or ejective feedback from an active galactic nucleus. In this Letter, we study the evolution of a Milky Way mass galaxy in the FIREbox cosmological volume that displays a thin, counter-rotating gas disc with respect to its stellar component at low redshift. In contrast to scenarios involving gas ejection, we find that pre-existing gas is mainly removed via the conversion into stars in a central starburst, triggered by a merging satellite galaxy. The newly-accreted, counter-rotating gas eventually settles into a kinematically misaligned disc. About 4.4 (8 out of 182) of FIREbox galaxies with stellar masses larger than 5e9 Msun at z=0 exhibit gas-star kinematic misalignment. In all cases, we identify central starburst-driven depletion as the main reason for the removal of the pre-existing co-rotating gas component, with no need for feedback from, e.g., a central active black hole. However, during the starburst, the gas is funneled towards the central regions, likely enhancing black hole activity. By comparing the fraction of misaligned discs between FIREbox and other simulations and observations, we conclude that this channel might have a non-negligible role in inducing kinematic misalignment in galaxies., Comment: 9 pages; 3 figures; submitted to ApJ Letters

Computer-aided molecular design (CAMD) studies quantitative structure-property relationships and discovers desired molecules using optimization algorithms. With the emergence of machine learning models, CAMD score functions may be replaced by various surrogates to automatically learn the structure-property relationships. Due to their outstanding performance on graph domains, graph neural networks (GNNs) have recently appeared frequently in CAMD. But using GNNs introduces new optimization challenges. This paper formulates GNNs using mixed-integer programming and then integrates this GNN formulation into the optimization and machine learning toolkit OMLT. To characterize and formulate molecules, we inherit the well-established mixed-integer optimization formulation for CAMD and propose symmetry-breaking constraints to remove symmetric solutions caused by graph isomorphism. In two case studies, we investigate fragment-based odorant molecular design with more practical requirements to test the compatibility and performance of our approaches., Comment: 35 pages, 8 figures, 7 tables

Atomic hydrogen (HI) serves a crucial role in connecting galactic-scale properties such as star formation with the large-scale structure of the Universe. While recent numerical simulations have successfully matched the observed covering fraction of HI near Lyman Break Galaxies (LBGs) and in the foreground of luminous quasars at redshifts $z \lesssim 3$, the low-mass end remains as-of-yet unexplored in observational and computational surveys. We employ a cosmological, hydrodynamical simulation (FIREbox) supplemented with zoom-in simulations (MassiveFIRE) from the Feedback In Realistic Environments (FIRE) project to investigate the HI covering fraction of Lyman Limit Systems ($N_{\mathrm{HI}} \gtrsim 10^{17.2}$ cm$^{-2}$) across a wide range of redshifts ($z=0-6$) and halo masses ($10^8-10^{13} M_{\odot}$ at $z=0$, $10^8-10^{11} M_{\odot}$ at $z=6$) in the absence of feedback from active galactic nuclei. We find that the covering fraction inside haloes exhibits a strong increase with redshift, with only a weak dependence on halo mass for higher-mass haloes. For massive haloes ($M_{\mathrm{vir}} \sim 10^{11}-10^{12} M_{\odot}$), the radial profiles showcase scale-invariance and remain independent of mass. The radial dependence is well-captured by a fitting function. The covering fractions in our simulations are in good agreement with measurements of the covering fraction in LBGs. Our comprehensive analysis unveils a complex dependence with redshift and halo mass for haloes with $M_{\mathrm{vir}} \lesssim 10^{10} M_{\odot}$ that future observations aim to constrain, providing key insights into the physics of structure formation and gas assembly.

After the nearly hundred gravitational-wave detections reported by the LIGO-Virgo-KAGRA Collaboration, the question of the cosmological origin of merging binary black holes (BBHs) remains open. The two main formation channels generally considered are from isolated field binaries or via dynamical assembly in dense star clusters. Here, we focus on understanding the dynamical formation of merging BBHs within massive clusters in galaxies of different masses. To this end, we apply a new framework to consistently model the formation and evolution of massive star clusters in zoom-in cosmological simulations of galaxies. Each simulation, taken from the FIRE project, provides a realistic star formation environment with a unique star formation history and hosts realistic giant molecular clouds that constitute the birthplace of star clusters. Combined with the code for star cluster evolution CMC, we are able to produce populations of dynamically formed merging BBHs across cosmic time in different environments. As the most massive star clusters preferentially form in dense massive clouds of gas, we find that, despite their low metallicities favourable to the creation of black holes, low-mass galaxies contain few massive clusters and therefore have a limited contribution to the global production of dynamically formed merging BBHs. Furthermore, we find that massive clusters can host hierarchical BBH mergers with clear identifiable physical properties. Looking at the evolution of the BBH merger rate in different galaxies, we find strong correlations between BBH mergers and the most extreme episodes of star formation. Finally, we discuss the implications for future LIGO-Virgo-KAGRA gravitational wave observations., Comment: 14 pages, 9 figures, 3 tables

We show that squeezing is a crucial resource for interferometers based on the spatial separation of ultra-cold interacting matter. Atomic interactions lead to a general limitation for the precision of these atom interferometers, which can neither be surpassed by larger atom numbers nor by conventional phase or number squeezing. However, tailored squeezed states allow to overcome this sensitivity bound by anticipating the major detrimental effect that arises from the interactions. We envisage applications in future high-precision differential matter-wave interferometers, in particular gradiometers, e.g., for gravitational-wave detection., Comment: 10 pages, 4 figures