JWST reveals a possible z∼11z \sim 11 galaxy merger in triply-lensed MACS0647−-JD

MACS0647$-$JD is a triply-lensed $z\sim11$ galaxy originally discovered with the Hubble Space Telescope. Here we report new JWST imaging, which clearly resolves MACS0647$-$JD as having two components that are either merging galaxies or stellar complexes within a single galaxy. Both are very small, with stellar masses $\sim10^8\,M_\odot$ and radii $r<100\,\rm pc$. The brighter larger component "A" is intrinsically very blue ($\beta\sim-2.6$), likely due to very recent star formation and no dust, and is spatially extended with an effective radius $\sim70\,\rm pc$. The smaller component "B" appears redder ($\beta\sim-2$), likely because it is older ($100-200\,\rm Myr$) with mild dust extinction ($A_V\sim0.1\,\rm mag$), and a smaller radius $\sim20\,\rm pc$. We identify galaxies with similar colors in a high-redshift simulation, finding their star formation histories to be out of phase. With an estimated stellar mass ratio of roughly 2:1 and physical projected separation $\sim400\,\rm pc$, we may be witnessing a galaxy merger 400 million years after the Big Bang. We also identify a candidate companion galaxy C $\sim3\,{\rm kpc}$ away, likely destined to merge with galaxies A and B. The combined light from galaxies A+B is magnified by factors of $\sim$8, 5, and 2 in three lensed images JD1, 2, and 3 with F356W fluxes $\sim322$, $203$, $86\,\rm nJy$ (AB mag 25.1, 25.6, 26.6). MACS0647$-$JD is significantly brighter than other galaxies recently discovered at similar redshifts with JWST. Without magnification, it would have AB mag 27.3 ($M_{UV}=-20.4$). With a high confidence level, we obtain a photometric redshift of $z=10.6\pm0.3$ based on photometry measured in 6 NIRCam filters spanning $1-5\rm\mu m$, out to $4300\,\r{A}$ rest-frame. JWST NIRSpec observations planned for January 2023 will deliver a spectroscopic redshift and a more detailed study of the physical properties of MACS0647$-$JD.Comment: 27 pages, 14 figures, submitted to Natur

CooccurrenceAffinity: An R package for computing a novel metric of affinity in co-occurrence data that corrects for pervasive errors in traditional indices.

1. Analysis of co-occurrence data with traditional indices has led to many problems such as sensitivity of the indices to prevalence and the same value representing either a strong positive or strong negative association across different datasets. In our recent study (Mainali et al 2022), we revealed the source of the problems that make the traditional indices fundamentally flawed and unreliable-namely that the indices in common use have no target of estimation quantifying degree of association in the non-null case-and we further developed a novel parameter of association, alpha, with complete formulation of the null distribution for estimating the mechanism of affinity. We also developed the maximum likelihood estimate (MLE) of alpha in our previous study. 2. Here, we introduce the CooccurrenceAffinity R package that computes the MLE for alpha. We provide functions to perform the analysis based on a 2×2 contingency table of occurrence/co-occurrence counts as well as a m×n presence-absence matrix (e.g., species by site matrix). The flexibility of the function allows a user to compute the alpha MLE for entity pairs on matrix columns based on presence-absence states recorded in the matrix rows, or for entity pairs on matrix rows based on presence-absence recorded in columns. We also provide functions for plotting the computed indices. 3. As novel components of this software paper not reported in the original study, we present theoretical discussion of a median interval and of four types of confidence intervals. We further develop functions (a) to compute those intervals, (b) to evaluate their true coverage probability of enclosing the population parameter, and (c) to generate figures. 4. CooccurrenceAffinity is a practical and efficient R package with user-friendly functions for end-to-end analysis and plotting of co-occurrence data in various formats, making it possible to compute the recently developed metric of alpha MLE as well as its median and confidence intervals introduced in this paper. The package supplements its main output of the novel metric of association with the three most common traditional indices of association in co-occurrence data: Jaccard, Sørensen-Dice, and Simpson

Null model analyses are not adequate to summarize strong associations: Rebuttal to Ulrich et al. (2022)

We recently developed a novel metric of association in pairwise co-occurrence data (Mainali et al., 2022) to address fundamental flaws in traditional indices, as elaborately discussed and conclusively shown in our published paper. Our new metric, the maximum likelihood estimator (MLE) alpha-hat of a statistical parameter alpha, quantifies the degree of association between species occupancy at ecological sites, and it is insensitive to the species prevalences and number of sites. In contrast, we showed that classic indices of co-occurrence (Jaccard, Simpson, Sørensen–Dice) can be highly sensitive to fixed margins of contingency tables, estimating wildly variable degrees of association and even reversing the direction of association for tables with different margins but the same degree-of-association alpha.https://doi.org/10.1111/jbi.1475

Analyzing the dynamics of urbanization in Delhi National Capital Region in India using satellite image time-series analysis

Understanding urban land-use changes and accurately quantifying urban land transitions is essential to global land-change research. The present study aimed to capture non-linear land transitions within urban areas using an automated change detection technique in a satellite image time series. Traditional land-use and cover maps used to map and monitor urban areas assume land change is a linear process and that urbanization is the last stage of land transition. In reality, however, most land transitions are non-linear. The present study focused on Delhi National Capital Territory, in India, and its adjacent major cities. A popular time-series analysis method was applied on MODIS NDVI time-series (2000–2017) data to detect change within the impervious surface area of the region. Overall validation and analysis of the results showed that the method was able to capture the direction and timing of the changes very well within all levels of urban density (except very high-density areas with more than 98% built-up density). The majority of urban areas in the region experienced interrupted, abrupt, and gradual greening. The results show different examples of non-linear land transitions detected from satellite images. Until recently, these land transitions could only be observed via long-term field surveys and/or local knowledge. The results reveal that the land-change trajectories can be different based on the level of built-up density, size of the urban area, physical proximity, and accessibility to relatively bigger urban areas. Knowledge gained from this study can be useful in better understanding the micro-climatic patterns and environmental quality within a city. </jats:p