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The UltraViolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) survey is a Hubble Space Telescope (HST) Cycle-26 Treasury Program, allocated in total 164 orbits of primary Wide-Field Camera 3 Ultraviolet and VISible light F275W imaging with coordinated parallel Advanced Camera for Surveys F435W imaging, on four of the five premier extragalactic survey fields: GOODS-N, GOODS-S, EGS, and COSMOS. We introduce this survey by presenting a comprehensive analysis of the absolute escape fraction ( ${f}_{{\rm{esc}}}^{{\rm{abs}}}$ ) of Lyman continuum radiation through stacking the UV images of a population of star-forming galaxies with secure redshifts at 2.4 ≤ z ≤ 3.0. Our stacking benefits from the catalogs of high-quality spectroscopic redshifts compiled from archival ground-based data and HST slitless spectroscopy, carefully vetted by dedicated visual inspection efforts. We develop a robust stacking method to apply to 10 samples of in total 56 galaxies, and perform detailed Monte Carlo simulations of the intergalactic medium (IGM) attenuation, to take into account the sample variance of the mean IGM transmission when measuring ${f}_{{\rm{esc}}}^{{\rm{abs}}}$ . The full stack at z ≈ 2.44 from 28 galaxies places a stringent 1 σ upper limit of ${f}_{{\rm{esc}}}^{{\rm{abs}}}\lesssim 5 \% $ , whereas the full stack at z ≈ 2.72 of an equal number of galaxies gives an upper limit of ${f}_{{\rm{esc}}}^{{\rm{abs}}}\lesssim 26 \% $ at 1 σ confidence level. These new F275W and F435W imaging mosaics from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.

In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data.

Endometriosis is a chronic inflammatory gynecologic condition among women of reproductive age causing a plethora of symptoms that significantly affect their quality of life and mental health. Recent literature reports mounting evidence for several inflammation-mediated pathways for endometriosis pathogenesis, where elevated levels of proinflammatory factors, such as intercellular adhesion molecule 1, tumor necrosis factor-alpha, and nuclear factor kappa B have been established. Simultaneously, the prevalent clinical use of H1-antihistamines for other pathologies along with active research into its action pathways has led to our current understanding that H1-antihistamines counteract several inflammation mediators, including intercellular adhesion molecule 1, tumor necrosis factor-alpha, and nuclear factor kappa B. Although a wide range of existing drug-based endometriosis managements act via the estrogen-dependent pathways, along with some newer ones attempting to use antagonists of targeted inflammation-modulating factors, investigations into the direct use of H1-antihistamines in the context of endometriosis are currently lacking. In this brief perspective opinion essay, correlative evidence has been placed forward that points toward a potential avenue of H1-antihistamines for endometriosis management, and some ideas have been highlighted for future research considerations.

UVCANDELS is a Hubble Space Telescope Cycle-26 Treasury Program awarded 164 orbits of primary ultraviolet (UV) F275W imaging and coordinated parallel optical F435W imaging in four CANDELS fields—GOODS-N, GOODS-S, EGS, and COSMOS—covering a total area of ∼426 arcmin ^2 . This is ∼2.7 times larger than the area covered by previous deep-field space UV data combined, reaching a depth of about 27 and 28 ABmag (5 σ in 0.”2 apertures) for F275W and F435W, respectively. Along with new photometric catalogs, we present an analysis of the rest-frame UV luminosity function (LF), relying on our UV-optimized aperture photometry method, yielding a factor of 1.5 increase over H-isophot aperture photometry in the signal-to-noise ratios of galaxies in our F275W imaging. Using well-tested photometric redshift measurements, we identify 5810 galaxies at redshifts 0.6 < z < 1, down to an absolute magnitude of M _UV = −14.2. In order to minimize the effect of uncertainties in estimating the completeness function, especially at the faint end, we restrict our analysis to sources above 30% completeness, which provides a final sample of 4726 galaxies at −21.5 < M _UV < −15.5. We performed a maximum likelihood estimate to derive the best-fit parameters of the UV LF. We report a best-fit faint-end slope of $\alpha =-{1.359}_{-0.041}^{+0.041}$ at z ∼ 0.8. Creating subsamples at z ∼ 0.7 and z ∼ 0.9, we observe a possible evolution of α with redshift. The unobscured UV luminosity density at M _UV < −10 is derived as ${\rho }_{{\rm{U}}{\rm{V}}}={1.339}_{-0.030}^{+0.027}\,(\times {10}^{26}\,{\rm{e}}{\rm{r}}{\rm{g}}\,{{\rm{s}}}^{-1}\,{{\rm{H}}{\rm{z}}}^{-1}\,{{\rm{M}}{\rm{p}}{\rm{c}}}^{-3})$ using our best-fit LF parameters. The new F275W and F435 photometric catalogs from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep Hubble Space Telescope (HST) F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and Extended Groth Strip). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the ∼430 arcmin ^2 UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift-fitting tools. Furthermore, using the full UV-to-IR photometric data set, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed form) of the star formation histories (SFHs). Compared to the flexible SFH parameterization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low-mass (≲10 ^9 M _⊙ ) and high-mass (≳10 ^10 M _⊙ ) end, by as much as ∼0.5 dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star formation in these galaxies.

Lyman-break galaxy (LBG) candidates at z ≳ 10 are rapidly being identified in James Webb Space Telescope (JWST)/NIRCam observations. Due to the (redshifted) break produced by neutral hydrogen absorption of rest-frame UV photons, these sources are expected to drop out in the bluer filters while being well detected in redder filters. However, here we show that dust-enshrouded star-forming galaxies at lower redshifts ( z ≲ 7) may also mimic the near-infrared (near-IR) colors of z > 10 LBGs, representing potential contaminants in LBG candidate samples. First, we analyze CEERS-DSFG-1, a NIRCam dropout undetected in the F115W and F150W filters but detected at longer wavelengths. Combining the JWST data with (sub)millimeter constraints, including deep NOEMA interferometric observations, we show that this source is a dusty star-forming galaxy (DSFG) at z ≈ 5.1. We also present a tentative 2.6 σ SCUBA-2 detection at 850 μ m around a recently identified z ≈ 16 LBG candidate in the same field and show that, if the emission is real and associated with this candidate, the available photometry is consistent with a z ∼ 5 dusty galaxy with strong nebular emission lines despite its blue near-IR colors. Further observations on this candidate are imperative to mitigate the low confidence of this tentative submillimeter emission and its positional uncertainty. Our analysis shows that robust (sub)millimeter detections of NIRCam dropout galaxies likely imply z ∼ 4–6 redshift solutions, where the observed near-IR break would be the result of a strong rest-frame optical Balmer break combined with high dust attenuation and strong nebular line emission, rather than the rest-frame UV Lyman break. This provides evidence that DSFGs may contaminate searches for ultra-high redshift LBG candidates from JWST observations.

We report the discovery of a candidate galaxy with a photo- z of z ∼ 12 in the first epoch of the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science Survey. Following conservative selection criteria, we identify a source with a robust z _phot = ${11.8}_{-0.2}^{+0.3}$ (1 σ uncertainty) with m _F200W = 27.3 and ≳7 σ detections in five filters. The source is not detected at λ < 1.4 μ m in deep imaging from both Hubble Space Telescope (HST) and JWST and has faint ∼3 σ detections in JWST F150W and HST F160W, which signal a Ly α break near the red edge of both filters, implying z ∼ 12. This object (Maisie’s Galaxy) exhibits F115W − F200W > 1.9 mag (2 σ lower limit) with a blue continuum slope, resulting in 99.6% of the photo- z probability distribution function favoring z > 11. All data-quality images show no artifacts at the candidate’s position, and independent analyses consistently find a strong preference for z > 11. Its colors are inconsistent with Galactic stars, and it is resolved ( r _h = 340 ± 14 pc). Maisie’s Galaxy has log M _* / M _⊙ ∼ 8.5 and is highly star-forming (log sSFR ∼ −8.2 yr ^−1 ), with a blue rest-UV color ( β ∼ −2.5) indicating little dust, though not extremely low metallicity. While the presence of this source is in tension with most predictions, it agrees with empirical extrapolations assuming UV luminosity functions that smoothly decline with increasing redshift. Should follow-up spectroscopy validate this redshift, our universe was already aglow with galaxies less than 400 Myr after the Big Bang.

The study of galaxy evolution hinges on our ability to interpret multiwavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models, which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to wide and deep multiwave band galaxy surveys, the volume of high-quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, including better modeling prescriptions, newer templates, and more extensive sampling in wavelength space. We present a comprehensive analysis of different SED-fitting codes including their methods and output with the aim of measuring the uncertainties caused by the modeling assumptions. We apply 14 of the most commonly used SED-fitting codes on samples from the CANDELS photometric catalogs at z ∼ 1 and z ∼ 3. We find agreement on the stellar mass, while we observe some discrepancies in the star formation rate (SFR) and dust-attenuation results. To explore the differences and biases among the codes, we explore the impact of the various modeling assumptions as they are set in the codes (e.g., star formation histories, nebular, dust and active galactic nucleus models) on the derived stellar masses, SFRs, and A _V values. We then assess the difference among the codes on the SFR–stellar mass relation and we measure the contribution to the uncertainties by the modeling choices (i.e., the modeling uncertainties) in stellar mass (∼0.1 dex), SFR (∼0.3 dex), and dust attenuation (∼0.3 mag). Finally, we present some resources summarizing best practices in SED fitting.

We present the first comprehensive release of photometric redshifts (photo - z 's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile–Quantile ( Q – Q ) plot to identify biases and signatures of underestimated or overestimated errors in photo - z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group’s PDF, we explore three methods of combining the different groups’ PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f -divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo - z PDFs and point estimates are achieved with the minimum f -divergence using the best four PDFs for each object (mFDa4) and the hierarchical Bayesian (HB4) methods, respectively. The HB4 photo - z point estimates produced σ _NMAD = 0.0227/0.0189 and ∣Δ z /(1 + z )∣ > 0.15 outlier fraction = 0.067/0.019 for spectroscopic and 3D Hubble Space Telescope redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo - z catalogs, which are available at https://archive.stsci.edu/prepds/candels/ .

Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.

Identifying merging galaxies is an important—but difficult—step in galaxy evolution studies. We present random forest (RF) classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM and modifying them to mimic future CEERS observations and nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the RFs using the merger history information for the simulated galaxies available from IllustrisTNG. The RFs correctly classify ∼60% of non-merging and merging galaxies across 0.5 < z < 4.0. Rest-frame asymmetry parameters appear more important for lower-redshift merger classifications, while rest-frame bulge and clump parameters appear more important for higher-redshift classifications. Adjusting the classification probability threshold does not improve the performance of the forests. Finally, the shape and slope of the resulting merger fraction and merger rate derived from the RF classifications match with theoretical Illustris predictions but are underestimated by a factor of ∼0.5.

The majority of massive star-forming galaxies at z ∼ 2 have velocity gradients suggestive of rotation, in addition to large amounts of disordered motions. In this paper, we demonstrate that it is challenging to distinguish the regular rotation of a disk galaxy from the orbital motions of merging galaxies with seeing-limited data. However, the merger fractions at z ∼ 2 are likely too low for this to have a large effect on measurements of disk fractions. To determine how often mergers pass for disks, we look to galaxy formation simulations. We analyze ∼24,000 synthetic images and kinematic maps of 31 high-resolution simulations of isolated galaxies and mergers at z ∼ 2. We determine if the synthetic observations pass the criteria commonly used to identify disk galaxies and whether the results are consistent with their intrinsic dynamical states. Galaxies that are intrinsically mergers pass the disk criteria for anywhere from 0% to 100% of sightlines. The exact percentage depends strongly on the specific disk criteria adopted and weakly on the separation of the merging galaxies. Therefore, one cannot tell with certainty whether observations of an individual galaxy indicate a merger or a disk. To estimate the fraction of mergers passing as disks in current kinematics samples, we combine the probability that a merger will pass as a disk with theoretical merger fractions from a cosmological simulation. Taking the latter at face value, the observed disk fractions are overestimated by small amounts: at most by 5% at high stellar mass (1010–11M⊙) and 15% at low stellar mass (109–10M⊙). [ABSTRACT FROM AUTHOR]

The bulk of the stellar growth over cosmic time is dominated by IR-luminous galaxies at cosmic noon (), many of which harbor a hidden active galactic nucleus (AGN). We use state-of-the-art infrared color diagnostics, combining Spitzer and Herschel observations, to separate dust-obscured AGNs from dusty star-forming galaxies (SFGs) in the CANDELS and COSMOS surveys. We calculate 24 μm counts of SFGs, AGN/star-forming “Composites,” and AGNs. AGNs and Composites dominate the counts above 0.8 mJy at 24 μm, and Composites form at least 25% of an IR sample even to faint detection limits. We develop methods to use the Mid-Infrared Instrument (MIRI) on JWST to identify dust-obscured AGNs and Composite galaxies from . With the sensitivity and spacing of MIRI filters, we will detect >4 times as many AGN hosts as with Spitzer/IRAC criteria. Any star formation rates based on the 7.7 μm PAH feature (likely to be applied to MIRI photometry) must be corrected for the contribution of the AGN, or the star formation rate will be overestimated by ∼35% for cases where the AGN provides half the IR luminosity and ∼50% when the AGN accounts for 90% of the luminosity. Finally, we demonstrate that our MIRI color technique can select AGNs with an Eddington ratio of and will identify AGN hosts with a higher specific star formation rate than X-ray techniques alone. JWST/MIRI will enable critical steps forward in identifying and understanding dust-obscured AGNs and the link to their host galaxies. [ABSTRACT FROM AUTHOR]