Multiband Imaging Research Articles

Multiband Imaging of the HD 36546 Debris Disk: A Refined View from SCExAO/CHARIS*

Abstract We present the first multiwavelength (near-infrared; 1.1–2.4 μm) imaging of HD 36546's debris disk, using the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system coupled with the Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS). As a 3–10 Myr old star, HD 36546 presents a rare opportunity to study a debris disk at very early stages. SCExAO/CHARIS imagery resolves the disk over angular separations of ρ ∼ 0.″25–1.″0 (projected separations of rproj ∼ 25–101 au) and enables the first spectrophotometric analysis of the disk. The disk’s brightness appears symmetric between its eastern and western extents, and it exhibits slightly blue near-infrared colors on average (e.g., J−K = −0.4 ± 0.1)—suggesting copious submicron-sized or highly porous grains. Through detailed modeling adopting a Hong scattering phase function (SPF), instead of the more common Henyey–Greenstein function, and using the differential evolution optimization algorithm, we provide an updated schematic of HD 36546's disk. The disk has a shallow radial dust density profile (α in ≈ 1.0 and α out ≈ −1.5), a fiducial radius of r 0 ≈ 82.7 au, an inclination of i ≈ 79.°1, and a position angle of PA ≈ 80.°1. Through spine tracing, we find a spine that is consistent with our modeling, but also with a “swept-back wing” geometry. Finally, we provide constraints on companions, including limiting a companion responsible for a marginal Hipparcos–Gaia acceleration to a projected separation of ≲0.″2 and to a minimum mass of ≲11 M Jup.

LIALFP: Multi-band images synchronous fusion model based on latent information association and local feature preserving

The fusion of multi-band images, i.e., far-infrared image (FIRI), near-infrared image (NIRI) and visible image (VISI), is mainly confronted with three-sided challenges: One is whether image fusion process could be performed synchronously. Most of existing algorithms are aimed at two fusion targets, which makes them have to adopt sequential way to merge multiple images. Unfortunately, this may make their results vulnerable to ambiguity or artifact. The second is the ground truth of fused results could not be obtained at all in some image fusion fields (e.g., multi-band images). That leads immediately to the failure of supervised methods to give full play to their advantages. Third, the latent projection between the result and source images is often not directly considered. Notably, the relation involves not only the fusion result with all inputs, but also with each original. In order to solve aforementioned problems, this paper establishes a unsupervised representation learning model for synchronous multi-band images fusion. First, significant pixel fusion features are extracted to ensure that the primary information can be integrated. Secondly, the potential relationship between the result and the whole originals is assumed to be the linear mapping, reducing the unpredictability of these fusion results. In addition, these transformation matrices have been given the function of feature selection, which could choose discriminant features and project them into the fusion space. Then, the locally significant features of each source are captured by designed graph Laplacian matrix. Finally, experiments show the rationality and superiority of our algorithm through comparison with a variety of recent advanced algorithms from subjective judgment and objective indicators.

The Geological History of the Chang’e-5 Sample Return Region

Chang’e-5 (CE-5), China’s first sample-return mission, has successfully landed in Oceanus Procellarum near Mons Rümker. It is important to have a detailed study of the geological evolution of the CE-5 sample return region. This work aims to study the geological background, topography, geomorphology, major chemical composition, mineralogy, and chronology of the landing site region. First, we used the map of topography obtained by the Kaguya TC merged Digital Terrain Model (DTM) to analyze the topographic characteristics. Then, we used the Kaguya Multiband Imager (MI) reflectance data to derive FeO and TiO2 abundance and the hyperspectral data of the Moon Mineralogy Mapper (M3) onboard the Chandrayaan-1 spacecraft to study the mineralogy of the landing site region. Later, we defined and dated the geological units of the landing area using the crater size–frequency distribution (CSFD) method. Finally, we conducted a detailed analysis of the volcanism and tectonism that occurred in the CE-5 landing area. The study region has experienced multi-stage magmatic activities (~3.36 Ga to ~1.22 Ga) and formed multiple mare units with different chemical and mineral compositions. The relationship between the wrinkle ridges cut by small impact craters suggests that the U7/Em5 has experienced Copernican aged tectonism recently ~320 Ma. The U7/Em5 unit where the Chang’e-5 sample return mission landed is dominantly composed of mature pyroxene and the basalts are mainly high-iron and mid-titanium basalts. Additionally, the analysis of pure basalt in the U7/Em5 suggests that the samples returned by the CE-5 mission may contain the ejecta and ray materials of young craters, including sharp B, Harding, Copernicus, and Aristarchus.

Discovery of 178 Giant Radio Galaxies in 1059 deg2 of the Rapid ASKAP Continuum Survey at 888 MHz

We report the results of a visual inspection of images of the Rapid ASKAP Continuum Survey (RACS) in search of extended radio galaxies (ERG) that reach or exceed linear sizes on the order of one Megaparsec. We searched a contiguous area of 1059 deg2 from RAJ = 20h20m to 06h20m, and −50∘<DecJ<−40∘, which is covered by deep multi-band optical images of the Dark Energy Survey (DES) and in which previously only three ERGs larger than 1 Mpc had been reported. For over 1800 radio galaxy candidates inspected, our search in optical and infrared images resulted in hosts for 1440 ERG, for which spectroscopic and photometric redshifts from various references were used to convert their largest angular size (LAS) to projected linear size (LLS). This resulted in 178 newly discovered giant radio sources (GRS) with LLS >1 Mpc, of which 18 exceed 2 Mpc and the largest one is 3.4 Mpc. Their redshifts range from 0.02 to ∼2.0, but only 10 of the 178 new GRS have spectroscopic redshifts. For the 146 host galaxies, the median r-band magnitude and redshift are 20.9 and 0.64, while for the 32 quasars or candidates these are 19.7 and 0.75. Merging the six most recent large compilations of GRS results in 458 GRS larger than 1 Mpc, so we were able to increase this number by ∼39% to 636.

Machine learning for inversing FeO and TiO2 content on the Moon: Method and comparison

The FeO and TiO2 contents are critical for distinguishing petrological properties of the Moon and for studying the distribution of the lunar maria and its multi-period volcanic activity. Traditional methods used the ratio between spectral reflectances to estimate FeO and TiO2 contents, which are empirical models. The development of machine learning algorithms offered new ideas for solving inversion problems, and these algorithms can automatically mine the data for potential correlations wherever possible. In this work, by using the Kaguya Multiband Imager data, we construct an optimized spectral inversion model using the Convolutional Neural Network (CNN) algorithm to produce a map of the FeO and TiO2 content on the lunar surface. The CNN models were compared with the traditional linear model and the Random Forest (RF) model. The results were indicated that the CNN models had higher accuracy and the CNN model eliminated the shortcoming of the RF model that the inversion results were limited by the training data, and certainly optimizes the impact of data striping. The CNN models can better describe the nonlinear relationship between spectral reflectance and oxide content. This also provides the basis for the inversion of the other oxides (e.g., MgO, Al2O3, CaO and SiO2). These new maps from the CNN model provide reference information for further studies of the geological evolution of the Moon.

Concurrent TMS/fMRI with high sensitivity and accelerated image acquisition

Abstract Functional magnetic resonance imaging (fMRI) has become the prime technique for non-invasive brain mapping as it yields high-resolution activation maps throughout the brain. Combined with TMS and applied simultaneously, concurrent TMS/fMRI offers the unique opportunity for directly assessing TMS-caused brain activity. Such, it is possible to study TMS effects across varying stimulation amplitude, protocol and targets. As changes in the BOLD fMRI signal are typically in range of only a few percent, high sensitivity acquisition is a prerequisite for successful TMS/fMRI. Standard concurrent TMS/fMRI setups are based on the use of single-channel, large-diameter birdcage coils that enable positioning of the TMS coil within the birdcage coil. In addition to their intrinsically low sensitivity, birdcage coils cannot be used with any of the various accelerated image acquisition methods used in MRI today, including parallel imaging (SENSE, GRAPPA) and multiband imaging (MB; simultaneous multi-slice, SMS). A different setup, based on thin, 7-channel receive arrays was recently proposed to overcome the shortcomings typical to birdcage coil setups [1,2]. Here we quantitatively assess the sensitivity of this new setup across a range of acquisition protocols. We also measure the effects of combining multiple receive-arrays for extended coverage. In particular, we show for the first time that it is possible to use three sets of 7-channel receive-arrays simultaneously to obtain unprecedentedly high sensitivity across the brain.

Lunar surface chemistry observed by the KAGUYA multiband imager

The abundance and distribution of oxides and Mg# (Mg/(Mg ​+ ​Fe) in mole percent) values are important for revealing the petrologic characteristics and evolutionary history of the Moon. In this work, new lunar surface chemistry maps, including Al2O3, CaO, FeO, MgO, TiO2, and Mg# maps, are generated from KAGUYA multiband imager (MI) spectra. These new maps are produced by the machine learning algorithm of the particle swarm optimization-support vector machine (PSO-SVM), and are characterized by relatively high accuracies and a high spatial resolution of 59 ​m/pixel, which are free to some degree from the topographic shadow effect. Moreover, Mg# values across the Moon exhibit an obvious chemical trichotomy. According to this Mg# feature, a new geological partition is suggested, and the lunar surface is divided into three geological units: the PM, SPAM, and FH units. The PM unit features low Mg# values, with an average of 0.55. The PM unit consists of Oceanus Procellarum, Grimaldi Crater, Tsiolkovskiy Crater, and some maria on the nearside and farside (e.g., the Frigoris, Imbrium, Tranquillitatis, Smythii, Crisium, Marginis, Orientale, and Moscoviense Maria). The FH unit possesses the highest Mg# values across the Moon, with an average of 0.70. The FH unit is composed of the Feldspathic Highlands Terrane (FHT), except for some maria (e.g., Orientale, Moscoviense, Australe, Marginis, and Smythii) and some craters (e.g., Tsiolkovskiy and Schickard). The SPAM unit is typical of medium Mg# values with an average of 0.65, and is constituted by the South Pole-Aitken Terrane (SPAT), Mare Australe, and some craters (e.g., the Schickard, Phocylides, Nasmyth, Schiller, and Inghirami Craters). The significant difference in Mg# values among the three geological units highlights the asymmetric magmatic evolution, crustal growth, and mare volcanism in the three units.

Real-time discovery of AT2020xnd: a fast, luminous ultraviolet transient with minimal radioactive ejecta

ABSTRACT The many unusual properties of the enigmatic AT2018cow suggested that at least some subset of the empirical class of fast blue optical transients (FBOTs) represents a genuinely new astrophysical phenomenon. Unfortunately, the intrinsic rarity and fleeting nature of these events have made it difficult to identify additional examples early enough to acquire the observations necessary to constrain theoretical models. We present here the Zwicky Transient Facility discovery of AT2020xnd (ZTF20acigmel, the ‘Camel’) at z = 0.243, the first unambiguous AT2018cow analogue to be found and confirmed in real time. AT2018cow and AT2020xnd share all key observational properties: a fast optical rise, sustained high photospheric temperature, absence of a second peak attributable to ejection of a radioactively heated stellar envelope, extremely luminous radio, millimetre, and X-ray emission, and a dwarf-galaxy host. This supports the argument that AT2018cow-like events represent a distinct phenomenon from slower-evolving radio-quiet supernovae, likely requiring a different progenitor or a different central engine. The sample properties of the four known members of this class to date disfavour tidal disruption models but are consistent with the alternative model of an accretion powered jet following the direct collapse of a massive star to a black hole. Contextual filtering of alert streams combined with rapid photometric verification using multiband imaging provides an efficient way to identify future members of this class, even at high redshift.

Identification of artisanal mining sites in the Amazon Rainforest using Geographic Object-Based Image Analysis (GEOBIA) and Data Mining techniques

Pará is a Brazilian state leader in deforestation and deserves special attention due to the intense artisanal mining activity that has caused severe environmental damage to the Amazon Rainforest. Remote sensing is an important tool for identifying areas degraded by mining activities. However, the large territorial extension of the Amazon Rainforest and the equally large corresponding database make the mapping by photointerpretation a costly and slow process. This study attempts to overcome this obstacle by employing the Geographic Object-Based Image Analysis (GEOBIA) approach together with Data Mining techniques in the automatic identification of areas degraded by artisanal mining in the Crepori National Forest (CNF). A NDVI image and a multiband image derived from Sentinel-2 data were segmented and the former proved to be more appropriate to the development of this research. The use of the Correlation-based Feature Selection (CFS) algorithm in attribute selection led to a 55% database dimensionality reduction. Additionally, the results obtained in the decision tree construction by the J48 algorithm showed that the spectral attributes were the most relevant in the classification of artisanal mining areas, especially the attributes related to the near infrared (NIR) band. The attributes of textural and spatial origin also contributed to the model, whereas the contextual attribute was not relevant to our classification problem. The results from classification demonstrated that the ‘Vegetation’ class is the largest in the Crepori National Forest, representing 99.50% of the total area, followed by ‘Areas Degraded by Artisanal Mining’ and ‘Other Anthropized Areas’, representing 0.17% of the total area, and, lastly, the ‘Hydrography’ class totaling 0.16%. Total anthropization in the CNF decreased between 2014 and 2017, from 2,955 ha to 2,506 ha. It is worth noting that, when compared with the Brazilian Forest Service's (Serviço Florestal Brasileiro) data, our results reveal that more than 50% (679.46 ha) of artisanal mining areas mapped in 2017 were installed after 2014, majorly in the CNF southern region. The performance of our classification model is good, reaching a global accuracy of 88.18% and a Kappa coefficient of 0.84. In class-by-class indexes, the method presented a minimum precision of 0.79 and a minimum recall of 0.75, both referring to the ‘Other Anthropized Areas’ class.

Widely distributed exogenic materials of varying compositions and morphologies on asteroid (101955) Bennu

ABSTRACTUsing the multiband imager MapCam on board the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) spacecraft, we identified 77 instances of proposed exogenic materials distributed globally on the surface of the B-type asteroid (101955) Bennu. We identified materials as exogenic on the basis of an absorption near 1 $\mu$m that is indicative of anhydrous silicates. The exogenic materials are spatially resolved by the telescopic camera PolyCam. All such materials are brighter than their surroundings, and they are expressed in a variety of morphologies: homogeneous, breccia-like, inclusion-like, and others. Inclusion-like features are the most common. Visible spectrophotometry was obtained for 46 of the 77 locations from MapCam images. Principal component analysis indicates at least two trends: (i) mixing of Bennu's average spectrum with a strong 1-$\mu$m band absorption, possibly from pyroxene-rich material, and (ii) mixing with a weak 1-$\mu$m band absorption. The end member with a strong 1-$\mu$m feature is consistent with Howardite-Eucrite-Diogenite (HED) meteorites, whereas the one showing a weak 1-$\mu$m feature may be consistent with HEDs, ordinary chondrites, or carbonaceous chondrites. The variation in the few available near-infrared reflectance spectra strongly suggests varying compositions among the exogenic materials. Thus, Bennu might record the remnants of multiple impacts with different compositions to its parent body, which could have happened in the very early history of the Solar system. Moreover, at least one of the exogenic objects is compositionally different from the exogenic materials found on the similar asteroid (162173) Ryugu, and they suggest different impact tracks.

Near-Earth space balloon-based multi-band airglow imager optic design.

As a common tracer in the atmosphere, airglow can be used as an important means to study the interaction between the lower atmosphere, near space, and ionosphere. In the near-Earth space, the high-altitude balloon can realize long time flight, which makes the airglow detection realize both high range resolution and time resolution. In this paper, a balloon-based multi-band airglow imager is designed, which can observe OI (557.7 nm), Na (589.3 nm), OI (630.0 nm), and OH (720-910 nm) with annular field of view (30° inner ring and 80° outer ring), and its resolution is 500 m at 250 km. The multi-band airglow imager designed in this paper is equipped in the payload cabin and raised to above 30 km for flat flying for more than 6 h. The experimental results show that the imager worked normally, and airglow images were photographed and stored; the optical system can stand the harsh environment in the near space. The multi-band airglow imager designed in this paper will take part in other near-space exploration tasks in the future and obtain corresponding results.

Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity

Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.

Investigation of the source region of the lunar-meteorite group with the remote sensing datasets: Implication for the origin of mare volcanism in Mare Imbrium

Lunar meteorites provide information about areas of the Moon not sampled by the Apollo and Luna missions, so they can be used as more-representative geochemical datasets for evaluating global lunar evolution. However, the launching craters remain unknown for many lunar meteorites. This paper, therefore, aims to identify the launching sites of lunar meteorites with remote-sensing datasets. This work focuses on one lunar-meteorite group, the Northwest Africa 773 (NWA 773) clan, which has the very-low-Ti basaltic composition and one of the youngest crystallization ages (~3 Ga) among the lunar basaltic meteorites. We investigated the source of the NWA 773 clan by comparing their geochemical compositions and geochronological information with the following remote-sensing datasets: 1) the Lunar Prospector gamma-ray elemental maps (FeO, TiO2, Th) and the SELENE gamma-ray elemental maps (CaO), 2) the surface ages determined from imaging data by using the crater-counting method, and 3) high-spatial-resolution maps of the FeO and TiO2 and spectral-ratio images made from the SELENE multi-band imager data. From these comparisons, the NWA 773 clan is most likely to have originated from the north part of Mare Imbrium inside the Procellarum KREEP Terrane (PKT). Furthermore, the eruption age of surface basalt surrounding the Le Verrier D crater in the north part of Mare Imbrium between 3.0 and 3.3 Ga, and the formation of the Le Verrier D crater, are consistent with the duration of igneous activity and timing of meteoroid impact of the NWA 773 clan. There is a fresh crater with bright ejecta on the edge of the Le Verrier D crater, and it is possibly the young launch crater of this meteorite group.

HOLISMOKES

We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens simulations, deep neural network classification, and visual inspection, is aimed at efficiently selecting systems with wide image separations (Einstein radiiθE ∼ 1.0–3.0″), intermediate redshift lenses (z ∼ 0.4–0.7), and bright arcs for galaxy evolution and cosmology. We classifiedgriimages of all 62.5 million galaxies in HSC Wide withi-band Kron radius ≥0.8″ to avoid strict preselections and to prepare for the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin Observatory. We obtained 206 newly-discovered candidates classified as definite or probable lenses with either spatially-resolved multiple images or extended, distorted arcs. In addition, we found 88 high-quality candidates that were assigned lower confidence in previous HSC searches, and we recovered 173 known systems in the literature. These results demonstrate that, aided by limited human input, deep learning pipelines with false positive rates as low as ≃0.01% can be very powerful tools for identifying the rare strong lenses from large catalogs, and can also largely extend the samples found by traditional algorithms. We provide a ranked list of candidates for future spectroscopic confirmation.

基于高动态范围成像的温室番茄植株图像色彩矫正方法

In this paper, aiming at the need of stable access to visual information of intelligent management of greenhouse tomatoes, the color correction method of tomato plant image based on high dynamic range imaging technology was studied, in order to overcome the objective limitation of complex natural lighting conditions on the stable color presentation of working objects. In view of the color distortion caused by the temporal and spatial fluctuation of illumination in greenhouse and sudden change of radiation intensity in complex background, a calibration method of camera radiation response model based on multi-exposure intensity images is proposed. The fusion effect of multi band image is evaluated by field test. The results show that after multi band image fusion processing, the brightness difference between the recognized target and other near color background is significantly enhanced, and the brightness fluctuation of the background is suppressed. The color correction method was verified by field experiments, and the gray information, discrete degree and clarity of tomato plant images in different scenes and periods were improved.

An ALMA view of 11 dusty star-forming galaxies at the peak of cosmic star formation history

ABSTRACT We present the ALMA view of 11 main-sequence dusty star-forming galaxies (DSFGs) (sub-)millimetre selected in the Great Observatories Origins Survey South (GOODS-S) field and spectroscopically confirmed to be at the peak of cosmic star formation history (z ∼ 2). Our study combines the analysis of galaxy spectral energy distribution with ALMA continuum and CO spectral emission by using ALMA Science Archive products at the highest spatial resolution currently available for our sample (Δθ ≲ 1 arcsec). We include galaxy multiband images and photometry (in the optical, radio, and X-rays) to investigate the interlink between dusty, gaseous, and stellar components and the eventual presence of AGN. We use multiband sizes and morphologies to gain an insight on the processes that lead galaxy evolution, e.g. gas condensation, star formation, AGN feedback. The 11 DSFGs are very compact in the (sub-)millimetre (median rALMA = 1.15 kpc), while the optical emission extends to larger radii (median rH/rALMA = 2.05). CO lines reveal the presence of a rotating disc of molecular gas, but we cannot exclude the presence of interactions and/or molecular outflows. Images at higher (spectral and spatial) resolution are needed to disentangle from the possible scenarios. Most of the galaxies are caught in the compaction phase, when gas cools and falls into galaxy centre, fuelling the dusty burst of star formation and the growing nucleus. We expect these DSFGs to be the high-z star-forming counterparts of massive quiescent galaxies. Some features of CO emission in three galaxies are suggestive of forthcoming/ongoing AGN feedback, which is thought to trigger the morphological transition from star-forming discs to early-type galaxies.

Enriching the knowledge of Ostia Antica painted fragments: a multi-methodological approach

This paper presents the study of selected painted fragments from different contexts of Ostia Antica city, dating between 2nd century BCE and the end of the 1st century CE. The aim is to identify the raw materials used and to understand the execution techniques through a non-invasive protocol including techniques based either on multiband imaging (Visible-VIS, Ultraviolet induced Luminescence - UVL and Visible Induced Luminescence - VIL) and single spot analyses (Fiber Optic Reflectance Spectroscopy- FORS and portable X-Ray Fluorescence spectrometry - XRF). The most representative and interesting fragments were sampled for further studies with laboratory techniques such as optical microscopy (OM) and electron microscopy (SEM), Fourier Transform Infrared and micro-Raman Spectroscopies (FT-IR and μRaman).The extensive use of non-invasive techniques, even working on fragments, is proved to be the most robust and effective approach enabling the analysis of a high number of areas, dramatically increasing the statistical meaning of the collected data. The elaboration of such a huge number of data allows highlighting differences and similarities, thus achieving a more realistic overview of the materials composition and addressing the sampling to the more significant and complex areas.

Chemical Characterization of Pope Pius VII Ancient Ecclesiastical Vestment by a Multi-Analytical Approach

This paper presents a multi-analytical investigation performed for the study of the ecclesiastical vestment, with insignia, of Pope Pius VII, painted from the end of the 18th up to the beginning of the 19th century, made of five clothing elements: chasuble, stole, maniple, chalice veil and purse. The aim of this research was to assess the conservation state of the silk and painted backgrounds; to define the manufacturing technique of the work; to localize, if present, the underdrawing and any retouching; to identify the pigments and, where possible, the binders used. A diagnostic protocol was developed based on preliminary investigations through multiband imaging techniques known as MBI (visible, ultraviolet-induced visible luminescence (UVL), near-infrared reflected (NIR) and infrared reflected false color (IRRFC) photography). The images acquired with MBI techniques ensured a more specific choice of spots to be analyzed directly in situ by non-invasive techniques. In particular, portable digital optical microscopy and X-ray fluorescence (XRF) were performed. Two fragments detached from the chasuble were also analyzed by microFT-IR, microRaman, scanning electron microscopy (SEM-EDS) and gas chromatography/mass spectrometry (GC-MS). Application of the multi-analytical protocol enabled the materials used to be characterized and helped to define the peculiar execution technique used. The presence of an underdrawing made with a carbon pencil was highlighted by MBI. Red lakes, iron-based pigments and copper-based pigments have been identified on the painting palette, applied with arabic gum as a binder.

Catalog-free modeling of galaxy types in deep images

Context.Current models of galaxy evolution are constrained by the analysis of catalogs containing the flux and size of galaxies extracted from multiband deep fields. However, these catalogs contain inevitable observational and extraction-related biases that can be highly correlated. In practice, taking all of these effects simultaneously into account is difficult, and therefore the derived models are inevitably biased as well.Aims.To address this issue, we use robust likelihood-free methods to infer luminosity function parameters, which is made possible by the massive compression of multiband images using artificial neural networks. This technique makes the use of catalogs unnecessary when observed and simulated multiband deep fields are compared and model parameters are constrained. Because of the efficient data compression, the method is not affected by the required binning of the observables inherent to the use of catalogs.Methods.A forward-modeling approach generates galaxies of multiple types depending on luminosity function parameters rendered on photometric multiband deep fields that include instrumental and observational characteristics. The simulated and the observed images present the same selection effects and can therefore be properly compared. We trained a fully convolutional neural network to extract the most model-parameter-sensitive summary statistics out of these realistic simulations, shrinking the dimensionality of the summary space to the number of parameters in the model. Finally, using the trained network to compress both observed and simulated deep fields, the model parameter values were constrained through population Monte Carlo likelihood-free inference.Results.Using synthetic photometric multiband deep fields similar to previously reported CFHTLS and WIRDS D1/D2 deep fields and massively compressing them through the convolutional neural network, we demonstrate the robustness, accuracy, and consistency of this new catalog-free inference method. We are able to constrain the parameters of luminosity functions of different types of galaxies, and our results are fully compatible with the classic catalog-extraction approaches.

New Constraints on the Lunar Optical Space Weathering Rate

AbstractSpace weathering processes form submicroscopic metallic iron particles that are optically active, darkening and reddening the lunar surface over time. The optical effects of these particles depend on their size; nanophase iron darkens and reddens, while microphase iron darkens without reddening. Using available Kaguya Multiband Imager parameter maps believed to estimate submicroscopic iron abundance, we investigate trends that may be associated with abundance of nanophase and microphase iron near dated lunar craters. We observe that nanophase iron is strongly correlated with crater age, while microphase iron exhibits a weaker correlation. We present models for the highlands nanophase and microphase iron accumulation rates from 100 ka to 1 Ga. Our observations suggest that highlands nanophase iron abundance is a direct result of space weathering exposure, while highlands microphase iron abundance is likely influenced by lunar source materials or stochastic impact‐delivery mechanisms.