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The COVID-19 pandemic has become the leading societal concern. The pandemic has shown that the public health concern is not only a medical problem, but also affects society as a whole; so, it has also become the leading scientific concern. We discuss in this treatise the importance of bringing the world’s scientists together to find effective solutions for controlling the pandemic. By applying novel research frameworks, interdisciplinary collaboration promises to manage the pandemic’s consequences and prevent recurrences of similar pandemics.

An early look at a galactic cluster A 'protocluster' of massive galaxies at a redshift z = 5.3, dating to only a billion years after the Big Bang, has been discovered in data from the Cosmological Evolution Survey (COSMOS), a project combining the power of the Hubble Space Telescope and ground-based telescopes on 2 square degrees of sky in the constellation Sextans. The protocluster occupies an overdense region more than 13 megaparsecs (40 million light years) across, rich in molecular gas and young stars. Its properties match the predictions of galaxy formation simulations, suggesting that the protocluster will evolve into a massive galaxy cluster typical of those seen at lower redshifts. Massive clusters of galaxies have been found as early as 3.9 billion years after the Big Bang. Cosmological simulations predict that these systems should descend from 'protoclusters' — early overdensities of massive galaxies that merge hierarchically to form a cluster. Observational evidence for this picture, however, is sparse because high-redshift protoclusters are rare and difficult to observe. Here, a protocluster region 1 billion years ( z = 5.3) after the Big Bang is reported. This cluster extends over >13 megaparsecs, contains a luminous quasar as well as a system rich in molecular gas. A lower limit of >4 × 10 11 solar masses of dark and luminous matter in this region is placed, consistent with that expected from cosmological simulations. Massive clusters of galaxies have been found that date from as early as 3.9 billion years 1 (3.9 Gyr; z = 1.62) after the Big Bang, containing stars that formed at even earlier epochs 2 , 3 . Cosmological simulations using the current cold dark matter model predict that these systems should descend from ‘protoclusters’—early overdensities of massive galaxies that merge hierarchically to form a cluster 4 , 5 . These protocluster regions themselves are built up hierarchically and so are expected to contain extremely massive galaxies that can be observed as luminous quasars and starbursts 4 , 5 , 6 . Observational evidence for this picture, however, is sparse because high-redshift protoclusters are rare and difficult to observe 6 , 7 . Here we report a protocluster region that dates from 1 Gyr ( z = 5.3) after the Big Bang. This cluster of massive galaxies extends over more than 13 megaparsecs and contains a luminous quasar as well as a system rich in molecular gas 8 . These massive galaxies place a lower limit of more than 4 × 10 11 solar masses of dark and luminous matter in this region, consistent with that expected from cosmological simulations for the earliest galaxy clusters 4 , 5 , 7 .

Dating the ages and weighting the stellar populations in galaxies are essential steps when studying galaxy formation through cosmic times. Evolutionary population synthesis models with different input physics are used for this purpose. Moreover, the contribution from the thermally pulsing asymptotic giant branch (TP-AGB) stellar phase, which peaks for intermediate-age 0.6–2 Gyr systems, has been debated for decades. Here we report the detection of strong cool-star signatures in the rest-frame near-infrared spectra of three young (~1 Gyr), massive (~10 10   M ⊙ ) quiescent galaxies at large look-back time, z  = 1–2, using JWST/NIRSpec. The coexistence of oxygen- and carbon-type absorption features, spectral edges and features from rare species, such as vanadium and possibly zirconium, reveal a strong contribution from TP-AGB stars. Population synthesis models with a significant TP-AGB contribution reproduce the observations better than those with a weak TP-AGB, which are commonly used. These findings call for revisions of published stellar population fitting results, as they point to populations with lower masses and younger ages and have further implications for cosmic dust production and chemical enrichment. New generations of improved models are needed, informed by these and future observations. Strong near-infrared spectral features from asymptotic giant branch stars are detected by JWST in the integrated light from distant quiescent galaxies, clarifying their contribution to galaxy spectra and population synthesis models.

The ratio of magnesium to iron abundance is measured for a massive quiescent galaxy at a redshift of 2.1, corresponding to when the Universe was three billion years old. A star-forming galaxy in quiescence This paper presents the first chemical abundance measurement of a galaxy beyond a redshift of z = 2. It is at z = 2.1, when the Universe was 3 billion years old, and the analysis shows it to be the most magnesium-enhanced massive galaxy found so far, with twice the enhancement found in similar-mass galaxies today. The abundance pattern of the galaxy is consistent with enrichment exclusively by core-collapse supernovae, and a star-formation timescale of 0.1 to 0.5 billion years, making it one of the most vigorous star-forming galaxies in the Universe. Unlike spiral galaxies such as the Milky Way, the majority of the stars in massive elliptical galaxies were formed in a short period early in the history of the Universe. The duration of this formation period can be measured using the ratio of magnesium to iron abundance ([Mg/Fe]) in spectra 1 , 2 , 3 , 4 , which reflects the relative enrichment by core-collapse and type Ia supernovae. For local galaxies, [Mg/Fe] probes the combined formation history of all stars currently in the galaxy, including younger and metal-poor stars that were added during late-time mergers 5 . Therefore, to directly constrain the initial star-formation period, we must study galaxies at earlier epochs. The most distant galaxy for which [Mg/Fe] had previously been measured 6 is at a redshift of z  ≈ 1.4, with [Mg/Fe] =  . A slightly earlier epoch ( z  ≈ 1.6) was probed by combining the spectra of 24 massive quiescent galaxies, yielding an average [Mg/Fe] = 0.31 ± 0.12 (ref. 7 ). However, the relatively low signal-to-noise ratio of the data and the use of index analysis techniques for both of these studies resulted in measurement errors that are too large to allow us to form strong conclusions. Deeper spectra at even earlier epochs in combination with analysis techniques based on full spectral fitting are required to precisely measure the abundance pattern shortly after the major star-forming phase ( z  > 2). Here we report a measurement of [Mg/Fe] for a massive quiescent galaxy at a redshift of z  = 2.1, when the Universe was three billion years old. With [Mg/Fe] = 0.59 ± 0.11, this galaxy is the most Mg-enhanced massive galaxy found so far, having twice the Mg enhancement of similar-mass galaxies today. The abundance pattern of the galaxy is consistent with enrichment exclusively by core-collapse supernovae and with a star-formation timescale of 0.1 to 0.5 billion years—characteristics that are similar to population II stars in the Milky Way. With an average past star-formation rate of 600 to 3,000 solar masses per year, this galaxy was among the most vigorous star-forming galaxies in the Universe.

The Spitzer Survey of Stellar Structure in Galaxies ([inline image]) is an Exploration Science Legacy Program approved for the Spitzer post-cryogenic mission. It is a volume-, magnitude-, and size-limited (d < 40 Mpc,

The unseen universe The cover shows part of the first map of the large-scale distribution of 'dark matter' in the Universe, constructed using images obtained in the largest ever survey with the Hubble Space Telescope. Dark matter is a mysterious substance that dominates the mass of the Universe, but neither emits nor reflects light, so is consequently invisible. It can be detected indirectly via gravitational lensing, the deflection of light from distant galaxies by any foreground concentrations of mass. The new map depicts a network of dark matter filaments that have grown over time and are separated by huge voids. Ordinary 'baryonic' particles (which account for only a sixth of the total mass in the Universe) subsequently build all stars, galaxies and planets inside this underlying scaffold of dark matter, during a process of gravitationally induced structure formation. (Cover image: NASA/ESA/R. Massey.) Ordinary baryonic particles account for only one-sixth of the total matter in the Universe, the rest being the mysterious 'dark matter'. This paper presents high-fidelity maps of the large-scale distribution of dark matter, resolved in both angle and depth. The results are consistent with predictions of gravitationally induced structure formation. Ordinary baryonic particles (such as protons and neutrons) account for only one-sixth of the total matter in the Universe 1 , 2 , 3 . The remainder is a mysterious ‘dark matter’ component, which does not interact via electromagnetism and thus neither emits nor reflects light. As dark matter cannot be seen directly using traditional observations, very little is currently known about its properties. It does interact via gravity, and is most effectively probed through gravitational lensing: the deflection of light from distant galaxies by the gravitational attraction of foreground mass concentrations 4 , 5 . This is a purely geometrical effect that is free of astrophysical assumptions and sensitive to all matter—whether baryonic or dark 6 , 7 . Here we show high-fidelity maps of the large-scale distribution of dark matter, resolved in both angle and depth. We find a loose network of filaments, growing over time, which intersect in massive structures at the locations of clusters of galaxies. Our results are consistent with predictions of gravitationally induced structure formation 8 , 9 , in which the initial, smooth distribution of dark matter collapses into filaments then into clusters, forming a gravitational scaffold into which gas can accumulate, and stars can be built 10 .

TheSpitzerSurvey of Stellar Structure in Galaxies ( S 4 G ) is an Exploration Science Legacy Program approved for theSpitzerpost–cryogenic mission. It is a volume-, magnitude-, and size-limited ( d < 40 Mpc d < 40     Mpc ,|b| > 30° | b | > 30 ° , m Bcorr < 15.5 m B corr < 15.5 , and D 25 > 1′ D 25 > 1 ′ ) survey of 2331 galaxies using the Infrared Array Camera (IRAC) at 3.6 and 4.5 μm. Each galaxy is observed for 240 s and mapped to≥1.5 × D 25 ≥ 1.5 × D 25 . The final mosaicked images have a typical 1σ rms noise level of 0.0072 and0.0093 MJy sr-1 0.0093     MJy   sr - 1 at 3.6 and 4.5 μm, respectively. Our azimuthally averaged surface brightness profile typically traces isophotes atμ3.6μm(AB)(1σ) ∼ 27 mag arcsec-2 μ 3.6 μ m ( AB ) ( 1 σ ) ∼ 27     mag   arcsec - 2 , equivalent to a stellar mass surface density of∼1 M ⊙pc-2 ∼ 1     M ⊙ pc - 2 . S 4 G thus provides an unprecedented data set for the study of the distribution of mass and stellar structures in the local universe. This large, unbiased, and extremely deep sample of all Hubble types from dwarfs to spirals to ellipticals will allow for detailed structural studies, not only as a function of stellar mass, but also as a function of the local environment. The data from this survey will serve as a vital testbed for cosmological simulations predicting the stellar mass properties of present-day galaxies. This article introduces the survey and describes the sample selection, the significance of the 3.6 and 4.5 μm bands for this study, and the data collection and survey strategies. We describe the S 4 G data analysis pipeline and present measurements for a first set of galaxies, observed in both the cryogenic and warm mission phases ofSpitzer. For every galaxy we tabulate the galaxy diameter, position angle, axial ratio, inclination atμ3.6μm(AB) = 25.5 μ 3.6 μ m ( AB ) = 25.5 , and26.5 mag arcsec-2 26.5     mag   arcsec - 2 (equivalent to≈μB(AB) = 27.2 ≈ μ B ( AB ) = 27.2 and28.2 mag arcsec-2 28.2     mag   arcsec - 2 , respectively). These measurements will form the initial S 4 G catalog of galaxy properties. We also measure the total magnitude and the azimuthally averaged radial profiles of ellipticity, position angle, surface brightness, and color. Finally, using the galaxy-fitting code GALFIT, we deconstruct each galaxy into its main constituent stellar components: the bulge/spheroid, disk, bar, and nuclear point source, where necessary. Together, these data products will provide a comprehensive and definitive catalog of stellar structures, mass, and properties of galaxies in the nearby universe and will enable a variety of scientific investigations, some of which are highlighted in this introductory S 4 G survey paper.

Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may struggle to capture complex galaxy properties. This study aims to develop a robust and efficient machine learning (ML) classification method to identify quiescent and star-forming galaxies within the Farmer COSMOS2020 catalog. We utilized JWST wide-field light cones from the Santa Cruz semi-analytical modeling framework to train a supervised ML model, the CatBoostClassifier, using 28 color features derived from 8 mutual photometric bands within the COSMOS catalog. The model was validated against a testing set and compared to the SED-fitting method in terms of precision, recall, F1-score, and execution time. Preprocessing steps included addressing missing data, injecting observational noise, and applying a magnitude cut (ch1 < 26 AB) along with a redshift range of 0.2 < z < 3.5 to align the simulated and observational datasets. The ML method achieved an F1-score of 89\\% for quiescent galaxies, significantly outperforming the SED-fitting method, which achieved 54%. The ML model demonstrated superior recall (88% vs. 38%) while maintaining comparable precision. When applied to the COSMOS2020 catalog, the ML model predicted a systematically higher fraction of quiescent galaxies across all redshift bins within 0.2 < z < 3.5 compared to traditional methods like NUVrJ and SED-fitting. This study shows that ML, combined with multi-wavelength data, can effectively identify quiescent and star-forming galaxies, providing valuable insights into galaxy evolution. The trained classifier and full classification catalog are publicly available.

Photometric properties (effective surface brightness, effective radius, radial profile index, axis ratio, color, color gradient) of 328 galaxies in the Coma cluster fainter than $R=15$ mag are examined as a function of the distance from the cluster center. No significant gradient is found for the effective surface brightness, effective radius and radial profile index. The distribution of axis ratios shows a concentration of round galaxies at the cluster center in the magnitude range $16.5 \\lt R \\lt 18$; most of these are found to be old and to have intermediate metal abundance, suggesting that they are nucleated dwarf ellipticals. On the other hand, we find a significant gradient in color, in the sense that galaxy colours become bluer with increasing distance from the cluster center. We conclude that this color gradient represents a metallicity gradient.To search for other articles by the author(s) go to: http://adsabs.harvard.edu/abstract_service.html

With current and upcoming experiments such as WFIRST, Euclid and LSST, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data set behaves like a high-dimensional nonlinear surface, an excellent target for machine learning. In this work, we use a lightcone of semianalytic galaxies tuned to match CANDELS observations from Lu et al. (2014) to train a set of neural networks on a set of galaxy physical properties. We add realistic photometric noise and use trained neural networks to predict stellar masses and average star formation rates on real CANDELS galaxies, comparing our predictions to SED fitting results. On semianalytic galaxies, we are nearly competitive with template-fitting methods, with biases of \\(0.01\\) dex for stellar mass, \\(0.09\\) dex for star formation rate, and \\(0.04\\) dex for metallicity. For the observed CANDELS data, our results are consistent with template fits on the same data at \\(0.15\\) dex bias in \\(M_{\\rm star}\\) and \\(0.61\\) dex bias in star formation rate. Some of the bias is driven by SED-fitting limitations, rather than limitations on the training set, and some is intrinsic to the neural network method. Further errors are likely caused by differences in noise properties between the semianalytic catalogs and data. Our results show that galaxy physical properties can in principle be measured with neural networks at a competitive degree of accuracy and precision to template-fitting methods.