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The Hubbard model provides a test bed to investigate the complex behaviour arising from electron-electron interaction in strongly-correlated systems and naturally emerges as the foundation model for lattice density functional theory (DFT). Similarly to conventional DFT, lattice DFT computes the ground-state energy of a given Hubbard model, by minimising a universal energy functional of the on-site occupations. Here we use machine learning to construct a class of scalable `semi-local' exchange-correlation functionals with an arbitrary degree of non-locality for the one-dimensional spinfull Hubbard model. Then, by functional derivative we construct an associated Kohn-Sham potential, that is used to solve the associated Kohn-Sham equations. After having investigated how the accuracy of the semi-local approximation depends on the degree of non-locality, we use our Kohn-Sham scheme to compute the polarizability of linear chains, either homogeneous or disordered, approaching the thermodynamic limit. approaching the thermodynamic limit., Comment: 15 pages, 14 figures

We present a study of new 7.7-11.3 $\mu$m data obtained with the James Webb Space Telescope Mid-InfraRed Instrument in the starburst galaxy M 82. In particular, we focus on the dependency of the integrated CO(1-0) line intensity on the MIRI-F770W and MIRI-F1130W filter intensities to investigate the correlation between CO content and the 7.7 and 11.3 $\mu$m features from polycyclic aromatic hydrocarbons (PAH) in M 82's outflows. To perform our analysis, we identify CO clouds using archival $^{12}$CO($J$=1-0) NOEMA moment 0 map within 2 kpc from the center of M 82, with sizes ranging between $\sim$21 and 270 pc; then, we compute the CO-to-PAH relations for the 306 validated CO clouds. On average, the power-law slopes for the two relations in M 82 are lower than what is seen in local main-sequence spirals. In addition, there is a moderate correlation between $I_{\rm CO(1-0)}$-$I_{\rm 7.7\mu m} /I_{\rm 11.3\mu m}$ for most of the CO cloud groups analyzed in this work. Our results suggest that the extreme conditions in M 82 translate into CO not tracing the full budget of molecular gas in smaller clouds, perhaps as a consequence of photoionization and/or emission suppression of CO molecules due to hard radiation fields from the central starburst.

While national biobanks are essential for advancing medical research, their non-probability sampling designs limit their representativeness of the target population. This paper proposes a method that leverages high-quality national surveys to create synthetic sampling weights for non-probabilistic cohort studies, aiming to improve representativeness. Specifically, we focus on deriving more accurate base weights, which enhance calibration by meeting population constraints, and on automating data-supported selection of cross-tabulations for calibration. This approach combines a pseudo-design-based model with a novel Last-In-First-Out criterion, enhancing the accuracy and stability of the estimates. Extensive simulations demonstrate that our method, named RAILS, reduces bias, improves efficiency, and strengthens inference compared to existing approaches. We apply the proposed method to the All of Us Research Program, using data from the National Health Interview Survey 2020 and the American Community Survey 2022 and comparing prevalence estimates for common phenotypes against national benchmarks. The results underscore our method's ability to effectively reduce selection bias in non-probability samples, offering a valuable tool for enhancing biobank representativeness. Using the developed sampling weights for the All of Us Research Program, we can estimate the prevalence of the United States population for phenotypes and genotypes not captured by national probability studies., Comment: 36 pages, 2 figure2, 4 tables

The $\sim$5 Myr PDS 70 is the only known system with protoplanets residing in the cavity of the circumstellar disk from which they formed, ideal for studying exoplanet formation and evolution within its natal environment. Here we report the first spin constraint and C/O measurement of PDS 70b from Keck/KPIC high-resolution spectroscopy. We detected CO (3.8 $\sigma$) and H$_2$O (3.5 $\sigma$) molecules in the PDS 70b atmosphere via cross-correlation, with a combined CO and H$_2$O template detection significance of 4.2 $\sigma$. Our forward model fits, using BT-Settl model grids, provide an upper limit for the spin-rate of PDS 70b ($<$29 km s$^{-1}$). The atmospheric retrievals constrain the PDS 70b C/O ratio to ${0.28}^{+0.20}_{-0.12}$ ($<$0.63 under 95$\%$ confidence level) and a metallicity [C/H] of ${-0.2}^{+0.8}_{-0.5}$ dex, consistent with that of its host star. The following scenarios can explain our measured C/O of PDS 70b in contrast with that of the gas-rich outer disk (for which C/O $\gtrsim$ 1). First, the bulk composition of PDS 70b might be dominated by dust+ice aggregates rather than disk gas. Another possible explanation is that the disk became carbon-enriched $\textit{after}$ PDS 70b was formed, as predicted in models of disk chemical evolution and as observed in both very low mass star and older disk systems with $\textit{JWST}$/MIRI. Because PDS 70b continues to accrete and its chemical evolution is not yet complete, more sophisticated modeling of the planet and the disk, and higher quality observations of PDS 70b (and possibly PDS 70c), are necessary to validate these scenarios., Comment: Accepted to ApJ Letters; 13 pages, 3 figures

The term `Digital Divide' emerged in the mid-1990s, highlighting the gap between those with access to emerging information technologies and those without. This gap persists for older adults even in the 21st century. To address this, our study focused on how older adults in Ireland can feel safer online. We conducted a two-phase study. In Phase I, 58 participants used Dot Voting to identify top cyber-security priorities, including password management, privacy, and avoiding scams. This informed Phase II, where we held focus groups with 31 participants from rural and urban communities in Ireland. Researchers provided tailored advice through presentations and leaflets, followed by open discussions. Our findings show that, despite being highly aware of cyber-scams, older adults remain very concerned about them. Participants expressed hesitation about using online password managers and two-factor authentication but valued advice on privacy and tools that can help them feel more in control online.

Deep learning technologies, particularly deep neural networks (DNNs), have demonstrated significant success across many domains. This success has been accompanied by substantial advancements and innovations in the algorithms behind the operations required by DNNs. These enhanced algorithms hold the potential to greatly increase the performance of DNNs. However, discovering the best performing algorithm for a DNN and altering the DNN to use such algorithm is a difficult and time consuming task. To address this, we introduce an open source framework which provides easy to use fine grain algorithmic control for DNNs, enabling algorithmic exploration and selection. Along with built-in high performance implementations of common deep learning operations, the framework enables users to implement and select their own algorithms to be utilized by the DNN. The framework's built-in accelerated implementations are shown to yield outputs equivalent to and exhibit similar performance as implementations in PyTorch, a popular DNN framework. Moreover, the framework incurs no additional performance overhead, meaning that performance depends solely on the algorithms chosen by the user., Comment: IEEE HPEC 2024

Determining the assembly index of a molecule, which aims to find the least number of steps required to make its molecular graph by recursively using previously made structures, is a novel problem seeking to quantify the minimum number of constraints required to build a given molecular graph which has wide applications from biosignature detection to cheminformatics including drug discovery. In this article, we consider this problem from an algorithmic perspective and propose an exact algorithm to efficiently find assembly indexes of large molecules including some natural products. To achieve this, we start by identifying the largest possible duplicate sub-graphs during the sub-graph enumeration process and subsequently implement a dynamic programming strategy with a branch and bound heuristic to exploit already used duplicates and reject impossible states in the enumeration. To do so efficiently, we introduce the assembly state data-structure as an array of edge-lists that keeps track of the graph fragmentation, by keeping the last fragmented sub-graph as its first element. By a precise manipulation of this data-structure we can efficiently perform each fragmentation step and reconstruct an exact minimal pathway construction for the molecular graph. These techniques are shown to compute assembly indices of many large molecules with speed and memory efficiency. Finally, we demonstrate the strength of our approach with different benchmarks, including calculating assembly indices of hundreds of thousands molecules from the COCONUT natural product database., Comment: 30 pages, 7 figures, 33 references

Chemputation is the process of programming chemical robots to do experiments using a universal symbolic language, but the literature can be error prone and hard to read due to ambiguities. Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including natural language processing, robotic control, and more recently, chemistry. Despite significant advancements in standardizing the reporting and collection of synthetic chemistry data, the automatic reproduction of reported syntheses remains a labour-intensive task. In this work, we introduce an LLM-based chemical research agent workflow designed for the automatic validation of synthetic literature procedures. Our workflow can autonomously extract synthetic procedures and analytical data from extensive documents, translate these procedures into universal XDL code, simulate the execution of the procedure in a hardware-specific setup, and ultimately execute the procedure on an XDL-controlled robotic system for synthetic chemistry. This demonstrates the potential of LLM-based workflows for autonomous chemical synthesis with Chemputers. Due to the abstraction of XDL this approach is safe, secure, and scalable since hallucinations will not be chemputable and the XDL can be both verified and encrypted. Unlike previous efforts, which either addressed only a limited portion of the workflow, relied on inflexible hard-coded rules, or lacked validation in physical systems, our approach provides four realistic examples of syntheses directly executed from synthetic literature. We anticipate that our workflow will significantly enhance automation in robotically driven synthetic chemistry research, streamline data extraction, improve the reproducibility, scalability, and safety of synthetic and experimental chemistry., Comment: 22 pages, 7 figures, 34 references

Evolution is often understood through genetic mutations driving changes in an organism's fitness, but there is potential to extend this understanding beyond the genetic code. We propose that natural products - complex molecules central to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond genes. By applying Assembly Theory (AT), which views selection as a process not limited to biological systems, we can map and measure evolutionary forces in these molecules. AT enables the exploration of the assembly space of natural products, demonstrating how the principles of the selfish gene apply to these complex chemical structures, selecting vastly improbable and complex molecules from a vast space of possibilities. By comparing natural products with a broader molecular database, we can assess the degree of evolutionary contingency, providing insight into how molecular novelty emerges and persists. This approach not only quantifies evolutionary selection at the molecular level but also offers a new avenue for drug discovery by exploring the molecular assembly spaces of natural products. Our method provides a fresh perspective on measuring the evolutionary processes both, shaping and being read out, by the molecular imprint of selection., Comment: 25 pages, 7 figures, 30 references

Crystallisation is an important phenomenon which facilitates the purification as well as structural and bulk phase material characterisation using crystallographic methods. However, different conditions can lead to a vast set of different crystal structure polymorphs and these often exhibit different physical properties, allowing materials to be tailored to specific purposes. This means the high dimensionality that can result from variations in the conditions which affect crystallisation, and the interaction between them, means that exhaustive exploration is difficult, time-consuming, and costly to explore. Herein we present a robotic crystal search engine for the automated and efficient high-throughput approach to the exploration of crystallisation conditions. The system comprises a closed-loop computer crystal-vision system that uses machine learning to both identify crystals and classify their identity in a multiplexed robotic platform. By exploring the formation of a well-known polymorph, we were able to show how a robotic system could be used to efficiently search experimental space as a function of relative polymorph amount and efficiently create a high dimensionality phase diagram with minimal experimental budget and without expensive analytical techniques such as crystallography. In this way, we identify the set of polymorphs possible within a set of experimental conditions, as well as the optimal values of these conditions to grow each polymorph., Comment: 18 pages, 6 figures, 20 references

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

The 2023-2024 AASA Superintendent Salary & Benefits Study marks the twelfth consecutive edition of this report. The 2023-2024 version employed a slightly revised and updated survey instrument. The survey tracked the demographics, salary, benefits, and other elements of the employment agreements of school superintendents throughout the country. This year's study results are based on 2,706 complete responses -- the highest response rate since inception of the study in 1999. The survey was distributed online and relied on superintendents responding to 66 items with the understanding that the report of findings would contain no personally identifiable information. Therefore, readers must consider the data descriptive and not necessarily representative of all superintendents. The study is intended to provide superintendents with actionable information needed to negotiate and manage their compensation and benefits.

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecular identities. AT allows us to estimate the complexity of molecules by deducing the amount of shared information stored within them when . By examining 74 samples from a diverse range of biotic and abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique) and 59518 molecular fragments (6755 unique). Using this MS dataset, together with AT, we were able to infer the joint assembly spaces (JAS) of samples from molecular analytes. We show how JAS allows agnostic annotation of samples without fingerprinting exact analyte identities, facilitating accurate determination of their biogenicity and taxonomical grouping. Furthermore, we developed an AT-based framework to construct a biochemistry-agnostic phylogenetic tree which is consistent with genome-based models and outperforms other similarity-based algorithms. Finally, we were able to use AT to track colony lineages of a single bacterial species based on phenotypic variation in their molecular composition with high accuracy, which would be challenging to track with genomic data. Our results demonstrate how AT can expand causal molecular inference to non-sequence information without requiring exact molecular identities, thereby opening the possibility to study previously inaccessible biological domains., Comment: 26 pages, 8 figures, 45 references

This work establishes a rigorous proof for the universality of the chemputer as a chemical synthesis machine, capable of constructing any stable and isolable molecule through a finite, expressible process. This process is governed by three key parameters: reagents, process conditions, and catalysts. Additionally, the study introduces dynamic error correction mechanisms integrated into each step of the synthesis pathway, ensuring real-time accuracy and reliability. The role of universally configurable hardware is also highlighted, with the introduction of a 'chempiling' function that translates synthesis pathways into executable hardware configurations. These advancements collectively demonstrate the chemputer's capability to perform any feasible chemical synthesis, thereby establishing it as a universal tool in chemical manufacturing and synthesis. I show that every finitely realizable chemical synthesis process that can exist within the bounds of physical laws can be perfectly instantiated and executed by a universal chemputer, provided that the process can be completed within the finite number of reagent input vessels, reaction vessels, and product output vessels available, and that the error correction mechanisms are sufficiently robust to maintain the accuracy of the synthesis within these constraints. Finally, I show that chemical reactions are not implicit functions, but are an emergent property coming from the combination of the reagents, process conditions, and catalysts., Comment: 11 pages, 1 figure, 12 references

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

Assembly theory (AT) quantifies selection using the assembly equation and identifies complex objects that occur in abundance based on two measurements, assembly index and copy number, where the assembly index is the minimum number of joining operations necessary to construct an object from basic parts, and the copy number is how many instances of the given object(s) are observed. Together these define a quantity, called Assembly, which captures the amount of causation required to produce objects in abundance in an observed sample. This contrasts with the random generation of objects. Herein we describe how AT's focus on selection as the mechanism for generating complexity offers a distinct approach, and answers different questions, than computational complexity theory with its focus on minimum descriptions via compressibility. To explore formal differences between the two approaches, we show several simple and explicit mathematical examples demonstrating that the assembly index, itself only one piece of the theoretical framework of AT, is formally not equivalent to other commonly used complexity measures from computer science and information theory including Shannon entropy, Huffman encoding, and Lempel-Ziv-Welch compression. We also include proofs that assembly index is not in the same computational complexity class as these compression algorithms and discuss fundamental differences in the ontological basis of AT, and assembly index as a physical observable, which distinguish it from theoretical approaches to formalizing life that are unmoored from measurement., Comment: 38 pages, 4 figures, 1 table, and 91 references plus supplement with proof of assembly index computational class

Assembly Theory (AT) was developed to help distinguish living from non-living systems. The theory is simple as it posits that the amount of selection or Assembly is a function of the number of complex objects where their complexity can be objectively determined using assembly indices. The assembly index of a given object relates to the number of recursive joining operations required to build that object and can be not only rigorously defined mathematically but can be experimentally measured. In pervious work we outlined the theoretical basis, but also extensive experimental measurements that demonstrated the predictive power of AT. These measurements showed that is a threshold in assembly indices for organic molecules whereby abiotic chemical systems could not randomly produce molecules with an assembly index greater or equal than 15. In a recent paper by Hazen et al [1] the authors not only confused the concept of AT with the algorithms used to calculate assembly indices, but also attempted to falsify AT by calculating theoretical assembly indices for objects made from inorganic building blocks. A fundamental misunderstanding made by the authors is that the threshold is a requirement of the theory, rather than experimental observation. This means that exploration of inorganic assembly indices similarly requires an experimental observation, correlated with the theoretical calculations. Then and only then can the exploration of complex inorganic molecules be done using AT and the threshold for living systems, as expressed with such building blocks, be determined. Since Hazen et al.[1] present no experimental measurements of assembly theory, their analysis is not falsifiable., Comment: 6 pages, 11 references

In this paper we study the filamentary substructure of 3.3 $\mu$m PAH emission from JWST/NIRCam observations in the base of the M82 star-burst driven wind. We identify plume-like substructure within the PAH emission with widths of $\sim$50 pc. Several of the plumes extend to the edge of the field-of-view, and thus are at least 200-300 pc in length. In this region of the outflow, the vast majority ($\sim$70\%) of PAH emission is associated with the plumes. We show that those structures contain smaller scale "clouds" with widths that are $\sim$5-15 pc, and they are morphologically similar to the results of "cloud-crushing" simulations. We estimate the cloud-crushing time-scales of $\sim$0.5-3 Myr, depending on assumptions. We show this time scale is consistent with a picture in which these observed PAH clouds survived break-out from the disk rather than being destroyed by the hot wind. The PAH emission in both the midplane and the outflow is shown to tightly correlate with that of Pa$\alpha$ emission (from HST/NICMOS data), at the scale of both plumes and clouds, though the ratio of PAH-to-Pa$\alpha$ increases at further distances from the midplane. Finally, we show that the outflow PAH emission is suppressed in regions of the M82 wind that are bright in X-ray emission. Overall, our results are broadly consistent with a picture in which cold gas in galactic outflows is launched via hierarchically structured plumes, and those small scale clouds are more likely to survive the wind environment when collected into the larger plume structure., Comment: Submitted to MNRAS

Polarimetric infrared detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy was discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A1+xTiS3, specifically BaTiS3[1,2] and Sr9/8TiS3[3,4]. In these materials, the critical role of atomic-scale structure modulations[4,5] in the unconventional electrical[5,6], optical[7,8], and thermal[7,9] properties raises the broader question of other materials that belong to this family. To address this issue, for the first time, we synthesized high-quality single crystals of a largely unexplored member of the A1+xTiX3 (X = S, Se) family, BaTiSe3. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported[10] P63/mmc structure. The crystal structure of BaTiSe3 features antiparallel c-axis displacements similar to BaTiS3,[2] but is of lower symmetry. Polarization-resolved Raman and Fourier transform infrared (FTIR) spectroscopy were used to characterize the optical anisotropy of BaTiSe3, whose refractive index along the ordinary (perpendicular to c) and extraordinary (parallel to c) optical axes was quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence {\Delta}n~0.9, BaTiSe3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging.

We present new observations of the central 1 kpc of the M 82 starburst obtained with the James Webb Space Telescope (JWST) near-infrared camera (NIRCam) instrument at a resolution ~0.05"-0.1" (~1-2 pc). The data comprises images in three mostly continuum filters (F140M, F250M, and F360M), and filters that contain [FeII] (F164N), H2 v=1-0 (F212N), and the 3.3 um PAH feature (F335M). We find prominent plumes of PAH emission extending outward from the central starburst region, together with a network of complex filamentary substructure and edge-brightened bubble-like features. The structure of the PAH emission closely resembles that of the ionized gas, as revealed in Paschen alpha and free-free radio emission. We discuss the origin of the structure, and suggest the PAHs are embedded in a combination of neutral, molecular, and photoionized gas., Comment: Submitted to The Astrophysical Journal

The PHENIX experiment has performed a systematic study of identified charged-hadron ($\pi^\pm$, $K^\pm$, $p$, $\bar{p}$) production at midrapidity in $p$$+$Al, $^3$He$+$Au, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV. Identified charged-hadron invariant transverse-momentum ($p_T$) and transverse-mass ($m_T$) spectra are presented and interpreted in terms of radially expanding thermalized systems. The particle ratios of $K/\pi$ and $p/\pi$ have been measured in different centrality ranges of large (Cu$+$Au, U$+$U) and small ($p$$+$Al, $^3$He$+$Au) collision systems. The values of $K/\pi$ ratios measured in all considered collision systems were found to be consistent with those measured in $p$$+$$p$ collisions. However the values of $p/\pi$ ratios measured in large collision systems reach the values of $\approx0.6$, which is $\approx2$ times larger than in $p$$+$$p$ collisions. These results can be qualitatively understood in terms of the baryon enhancement expected from hadronization by recombination. Identified charged-hadron nuclear-modification factors ($R_{AB}$) are also presented. Enhancement of proton $R_{AB}$ values over meson $R_{AB}$ values was observed in central $^3$He$+$Au, Cu$+$Au, and U$+$U collisions. The proton $R_{AB}$ values measured in $p$$+$Al collision system were found to be consistent with $R_{AB}$ values of $\phi$, $\pi^\pm$, $K^\pm$, and $\pi^0$ mesons, which may indicate that the size of the system produced in $p$$+$Al collisions is too small for recombination to cause a noticeable increase in proton production., Comment: 480 authors from 78 institutions, 18 pages, 6 tables, 16 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present a $^{12}$CO($J$=2-1) survey of 60 local galaxies using data from the Atacama Large Millimeter/submillimeter Compact Array as part of the Extragalactic Database for Galaxy Evolution: the ACA EDGE survey. These galaxies all have integral field spectroscopy from the CALIFA survey. Compared to other local galaxy surveys, ACA EDGE is designed to mitigate selection effects based on CO brightness and morphological type. Of the 60 galaxies in ACA EDGE, 36 are on the star-formation main sequence, 13 are on the red sequence, and 11 lie in the ``green valley" transition between these sequences. We test how star formation quenching processes affect the star formation rate (SFR) per unit molecular gas mass, SFE$_{\rm mol}=$SFR/$M_{\rm mol}$, and related quantities in galaxies with stellar masses $10\leq$log[$M_\star/$M$_\odot$]$\leq11.5$ covering the full range of morphological types. We observe a systematic decrease of the molecular-to-stellar mass fraction ($R^{\rm mol}_{\star}$) with decreasing level of star formation activity, with green valley galaxies having also lower SFE$_{\rm mol}$ than galaxies on the main sequence. On average, we find that the spatially resolved SFE$_{\rm mol}$ within the bulge region of green valley galaxies is lower than in the bulges of main sequence galaxies if we adopt a constant CO-to-H$_2$ conversion factor, $\alpha_{\rm CO}$. While efficiencies in main sequence galaxies remain almost constant with galactocentric radius, in green valley galaxies we note a systematic increase of SFE$_{\rm mol}$, $R^{\rm mol}_{\star}$, and specific star formation rate, sSFR, with increasing radius. Our results suggest that although gas depletion (or removal) seems to be the most important driver of the star-formation quenching in galaxies transiting through the green valley, a reduction in star formation efficiency is also required during this stage.