Photometric Model Research Articles

Star Photometry for DECam Legacy Survey and Sloan Digital Sky Survey Images Based on Convolutional Neural Networks

Flux is one of the most fundamental parameters in astrophysics, and aperture photometry and point-spread function (PSF) photometry are commonly used methods to obtain the flux. With the continuous development of astronomical equipment that has generated massive data, researchers have to find more effective methods to obtain stellar fluxes. However, current photometric software such as SExtractor are very sensitive to the configuration parameters and are difficult to configure. We propose a new photometric model based on deep learning called sf-convolutional neural network (CNN) to extract aperture fluxes and PSF fluxes. For the simulated data including 5727 stars, the experimental results show that sf-CNN can predict fluxes better than SExtractor. The mean absolute error (MAE) values of sf-CNN and SExtractor for predicting PSF fluxes are 0.0034 and 0.0134, respectively. On the 6293 mixed stars in DECam Legacy Survey Data Release (DR) 9, the MAE values of the predicted PSF fluxes are 0.0075 and 0.0177, respectively. The PSF accuracy of the sf-CNN model is significantly higher than that of SExtractor. Additionally, the MAE values of the predicted aperture fluxes on 6215 mixed stars and 1341 blends of stars in Sloan Digital Sky Survey DR 12 illustrate that the accuracy of sf-CNN is still the highest. Meanwhile, the results indicate that sf-CNN outperforms VGG16 and ResNet50. Furthermore, sf-CNN is 100–200 times faster than Photutils on RTX 3070 GPU and 20–40 times faster than Photutils on I7 12700 CPU. sf-CNN can calculate fluxes efficiently and accurately only by setting a few parameters and may thus become a fundamental tool for the era of big data in astronomy.

Antioxidant and antidiabetic effects of aqueous extract of <i>Senna alata</i> on alloxan-induced diabetic rats

This study aimed to evaluate the antioxidant effect and the state of the liver following injury by alloxan monohydrate and treatment with an aqueous extract of Senna alata (SA) leaf. A total of 24 male Albino Wistar rats assigned into 4 groups (A-D) of 6 rats per group were used in this study. Forty-eight hours following administration of alloxan monohydrate and confirmation of diabetes mellitus in groups B-D rats, group C were treated with SA while group D rats were treated with glibenclamide. Rats in groups A and B received distilled water. The treatments were through oral administration, once daily for 21 consecutive days. On day 21 post-treatment, serum samples were collected for in vivo antioxidants and liver enzyme assays. The liver tissues were also collected for macroscopic examination and histomorphology. The in vitro antioxidant activity of SA was also determined using DPPH photometric model. The SA-treated rats recorded significantly reduced activities of ALT, ALP and total bilirubin values when compared to rats in the diabetic untreated group. There was also a significant increase in the activities of catalase, superoxide dismutase and glutathione in SA-treated rats compared to those of the untreated diabetic group. The study also recorded 67.33% in vitro antioxidant activity of SA at 400 μg/ml. Liver photomicrographs of rats treated with the extract were comparable to those of the normal control while diabetic untreated rats showed congestion of the central vein, degeneration and necrosis of hepatocytes. SA has both in vitro and in vivo antioxidant activity and protected the liver against damage by alloxan monohydrate.

Beating Stellar Systematic Error Floors Using Transit-based Densities

It has long been understood that the light curve of a transiting planet constrains the density of its host star. That fact is routinely used to improve measurements of the stellar surface gravity and has been argued to be an independent check on the stellar mass. Here we show how the stellar density can also dramatically improve the precision of the radius and effective temperature of the star. This additional constraint is especially significant when we properly account for the 4.2% radius and 2.0% temperature systematic errors inherited from photometric zero-points, model atmospheres, interferometric calibration, and extinction. In the typical case, we can constrain stellar radii to 3% and temperatures to 1.75% with our evolutionary-model-based technique. In the best real-world cases, we can infer radii to 1.6% and temperatures to 1.1%—well below the systematic measurement floors—which can improve the precision in the planetary parameters by a factor of two. We explain in detail the mechanism that makes it possible and show a demonstration of the technique for a near-ideal system, WASP-4. We also show that both the statistical and systematic uncertainties in the parallax from Gaia DR3 are often a significant component of the uncertainty in L * and must be treated carefully. Taking advantage of our technique requires simultaneous models of the stellar evolution, bolometric flux (e.g., a stellar spectral energy distribution), and the planetary transit, while accounting for the systematic errors in each, as is done in EXOFASTv2.

Ascertaining the Ideality of Photometric Stereo Datasets under Unknown Lighting

The standard photometric stereo model makes several assumptions that are rarely verified in experimental datasets. In particular, the observed object should behave as a Lambertian reflector, and the light sources should be positioned at an infinite distance from it, along a known direction. Even when Lambert’s law is approximately fulfilled, an accurate assessment of the relative position between the light source and the target is often unavailable in real situations. The Hayakawa procedure is a computational method for estimating such information directly from data images. It occasionally breaks down when some of the available images excessively deviate from ideality. This is generally due to observing a non-Lambertian surface, or illuminating it from a close distance, or both. Indeed, in narrow shooting scenarios, typical, e.g., of archaeological excavation sites, it is impossible to position a flashlight at a sufficient distance from the observed surface. It is then necessary to understand if a given dataset is reliable and which images should be selected to better reconstruct the target. In this paper, we propose some algorithms to perform this task and explore their effectiveness.

2021 occultations and transits of Linus orbiting (22) Kalliope

Aims.The satellite Linus orbiting the main-belt asteroid (22) Kalliope exhibited mutual occultation and transit events in late 2021. A photometric campaign was organised and observations were undertaken by the TRAPPIST-South, SPECULOOS-Artemis, OWL-Net, and BOAO telescopes, with the goal to further constrain dynamical and photometric models of this sizeable asteroid-satellite system.Methods.Our dynamical model is sufficiently complex, featuring multipoles (up to the order ofℓ= 2), internal tides, and external tides. The model was constrained by astrometry (spanning 2001–2021), occultations, adaptive-optics imaging, and calibrated photometry, as well as relative photometry. Our photometric model was substantially improved. A new precise (<0.1 mmag) light curve algorithm was implemented, based on polygon intersections, which are computed exactly by including partial eclipses and partial visibility of polygons. Moreover, we implemented a ‘cliptracing’ algorithm, again based on polygon intersections, in which partial contributions to individual pixels are computed exactly. Both synthetic light curves and synthetic images then become very smooth.Results.Based on our combined solution, we confirmed the size of Linus, namely, (28 ± 1)km. However, this solution exhibits some tension among the light curves and the PISCO speckle-interferometry dataset, acquired simultaneously with the 2021 events. This indicates that improvements of the shape are still possible. In most solutions, Linus is darker than Kalliope, with the single-scattering albedosAw= 0.40 vs. 0.44. This is confirmed on deconvolved images. A detailed revision of astrometric data has allowed us to revise also theJ2≡ −C20value of Kalliope. Most importantly, a homogeneous body is excluded. For a differentiated body, two solutions exist: low-oblateness (C20≃ −0.12), with a spherical iron core, and, alternatively, high-oblateness (C20≃ −0.22) with an elongated iron core. These values correspond, respectively, to the low- and high-energy collisions we studied via SPH simulations in our previous work.

Photometric and Monte-Carlo modeling unified approach for the calculation of spatially-resolved correction coefficients linking simulated and experimental diffuse reflectance spectra.

The estimation of skin optical properties by means of inverse problem solving from spatially resolved diffuse reflectance (SR-DR) spectra is one way to exploit the acquired clinical signals. This method requires the comparison between the experimental spectra collected with a medical device, and spectra generated by the photons transport numerical simulations. This comparison is usually limited to spectral shape due to the absence of intensity standardization of the experimental DR spectra. This study proposes to theoretically (using photometric calculation) and experimentally (from experimental spectra acquired on optical phantom) establish a corrective factor to obtain common intensity unit for experimental and simulated signals.

MOBSTER – VII. Using light curves to infer magnetic and rotational properties of stars with centrifugal magnetospheres

ABSTRACT Early-type B stars with strong magnetic fields and rapid rotation form centrifugal magnetospheres (CMs), as the relatively weak stellar wind becomes magnetically confined and centrifugally supported above the Kepler co-rotation radius. CM plasma is concentrated at and above the Kepler co-rotation radius at the intersection between the rotation and magnetic field axis. Stellar rotation can cause these clouds of material to intersect the viewer’s line of sight, leading to photometric eclipses. However, for stars with strong ($\sim 10\, {\rm kG}$) magnetic fields and rapid rotation, CMs can become optically thick enough for emission to occur via electron scattering. Using high-precision space photometry from a sample of stars with strong H α emission, we apply simulated light curves from the rigidly rotating magnetosphere model to directly infer magnetic and rotational properties of these stars. By comparing the values inferred from photometric modelling to those independently determined by spectropolarimetry, we find that magnetic obliquity angle β, viewer inclination i, and critical rotation fraction W can be approximately recovered for three of the four stars studied here. However, there are large discrepancies between the optical depth at the Kepler radius τK expected from magnetometry, and the values required to match the observations. We show that τK of order unity is needed to reasonably match the light-curve morphology of our sample stars.

KiDS-1000: Cross-correlation with Planck cosmic microwave background lensing and intrinsic alignment removal with self-calibration

Context. Galaxy shear and cosmic microwave background (CMB) lensing convergence cross-correlations contain additional information on cosmology with respect to auto-correlations. While remaining immune to certain systemic effects, these cross-correlations are nonetheless affected by the galaxy’s intrinsic alignments (IA). These effects may, in fact, be responsible for the reported low lensing amplitude of the galaxy shear × CMB convergence cross-correlations, compared to the standard PlanckΛCDM (cosmological constant and cold dark matter) cosmology predictions. Aims. In this work, we investigate how IA affects the Kilo-Degree Survey (KiDS) galaxy lensing shear and Planck CMB lensing convergence cross-correlation and we compare it to previous treatments, both with and without IA taken into consideration. Methods. We compared the marginalization over IA parameters and the IA self-calibration (SC) method (with additional observables defined only from the source galaxies) to demonstrate that SC can efficiently break the degeneracy between the CMB lensing amplitude, Alens, and the IA amplitude, AIA. We further investigated how different systematics affect the resulting AIA and Alens and we validated our results with the MICE2 simulation. Results. We find that by including the SC method to constrain IA, the information loss due to the degeneracy between CMB lensing and IA is strongly reduced. The best-fit values are Alens = 0.84−0.22+0.22 and AIA = 0.60−1.03+1.03, while different angular scale cuts can affect Alens by ∼10%. We show that an appropriate treatment of the boost factor, cosmic magnification, and photometric redshift modeling is important for obtaining the correct IA and cosmological results.

MASR-PSN: a low-resolution photometric stereo images-relevant deep learning model for high-resolution surface normal reconstruction

MASR-PSN: a low-resolution photometric stereo images-relevant deep learning model for high-resolution surface normal reconstruction

The multichord stellar occultation by the centaur Bienor on January 11, 2019

Within our program of physical characterization of trans-Neptunian objects and centaurs, we predicted a stellar occultation by the centaur (54598) Bienor to occur on January 11, 2019, with good observability potential. We obtained high accuracy astrometric data to refine the prediction, resulting in a shadow path favorable for the Iberian Peninsula. This encouraged us to carry out an occultation observation campaign that resulted in five positive detections from four observing sites. This is the fourth centaur for which a multichord (more than two chords) stellar occultation has been observed so far, the other three being (2060) Chiron, (10199) Chariklo, and (95626) 2002 GZ32. From the analysis of the occultation chords, combined with the rotational light curve obtained shortly after the occultation, we determined that Bienor has an area-equivalent diameter of 150 ± 20 km. This diameter is ~30 km smaller than the one obtained from thermal measurements. The position angle of the short axis of the best fitting ellipse obtained through the analysis of the stellar occultation does not match that of the spin axis derived from long-term photometric models. We also detected a strong irregularity in one of the minima of the rotational light curve that is present no matter the aspect angle at which the observations were done. We present different scenarios to reconcile the results from the different techniques. We did not detect secondary drops related to potential rings or satellites. Nonetheless, similar rings in size to that of Chariklo's cannot be discarded due to low data accuracy.

PhotoRedshift-MML: A multimodal machine learning method for estimating photometric redshifts of quasars

ABSTRACT We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415 930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |Δz| = |(zphot − zspec)/(1 + zspec)| to evaluate the redshift prediction and demonstrated a $4.04{{\ \rm per\ cent}}$ increase in accuracy. With the help of the generated spectral features, the proportion of data with |Δz| < 0.1 can reach $84.45{{\ \rm per\ cent}}$ of the total test samples, whereas it reaches $80.41{{\ \rm per\ cent}}$ for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |Δz| is shown to decrease from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction.

Photometric-Stereo-Based Defect Detection System for Metal Parts.

Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts' geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.

CLusters in the UV as EngineS (CLUES). I. Survey Presentation and FUV Spectral Analysis of the Stellar Light

The CLusters in the Uv as EngineS (CLUES) survey is a Cosmic Origins Spectrograph (COS) campaign aimed at acquiring the 1130–1770 Å rest-frame spectroscopy of very young (<20 Myr) and massive (>104 M ⊙) star clusters in galaxies that are part of the Hubble treasury program Legacy ExtraGalactic UV Survey. In this first paper of a series, we describe the CLUES sample consisting of 20 young star clusters and report their physical properties as derived by both multiwavelength photometry and far-UV (FUV) spectroscopy with Hubble Space Telescope. Thanks to the synergy of the two different data sets, we build a coherent picture of the diverse stellar populations found in each region (with sizes of 40–160 pc). We associate the FUV-brightest stellar population to the central targeted star cluster and the other modeled population to the diffuse stars that are included in the COS aperture. We observe better agreement between photometric and spectroscopic ages for star clusters younger than 5 Myr. For clusters older than 5 Myr, photometry and spectroscopy measurements deviate, with the latter producing older ages, due to the degeneracy of photometric models. FUV spectroscopy enables us to better constrain the stellar metallicities, a parameter that optical colors are insensitive to. Finally, the derived E(B − V) are quite similar, with a tendency for FUV spectroscopy to favor solutions with higher extinctions. The recovered masses are in agreement within a factor of 2 for all of the clusters.

A possible nearby microlensing stellar remnant hiding in Gaia DR3 astrometry

Massive galactic lenses with large Einstein Radii should cause a measurable astrometric microlensing effect, that is, a light centroid shift due to the motion of the two images. Such a shift in the position of a background star due to microlensing was not included in the Gaia astrometric model, and therefore significant deviation should cause Gaia’s astrometric parameters to be determined incorrectly. Here we study one of the photometric microlensing events reported in the Gaia Data Release 3, GaiaDR3-ULENS-001, for which a poor goodness of Gaia fit and erroneous parallax could indicate the presence of an astrometric microlensing signal. Based on the photometric microlensing model, we simulated Gaia astrometric time series with the astrometric microlensing effect added. We find that including microlensing with an angular Einstein radius of θE = 2.60−0.24+0.21 mas (2.47−0.24+0.28 mas) assuming a positive (negative) impact parameter, u0, reproduces the astrometric quantities reported by Gaia well. We estimate the mass of the lens to be 1.00−0.18+0.23 M⊙ (0.70−0.13+0.17 M⊙) and its distance 0.90−0.11+0.14 kpc (0.69−0.09+0.13 kpc), proposing the lens could be a nearby isolated white dwarf.

Assessing the spectrophotometric properties of Mercury's hollows through multiangular MESSENGER/MDIS observations

The origin and formation mechanism of Mercury's hollows, which are bright, often haloed, small, shallow, irregular, rimless and flat floored depressions, represent one of the major open science questions regarding the Hermean surface and the processes shaping it morphology. In this work, we perform a photometric modelling of multiangular and multiband images of Tyagaraja and Canova craters' hollows to investigate the physical properties of their reflecting material. Thanks to such observations, we demonstrate that we can derive a better topographic correction when compared to the one obtained from the global photometric models of Mercury. Indeed, our parameters, which result from the inversion of the Hapke and Kaasalainen-Shkuratov models, can be useful for both future spectrophotometric analyses of Mercury and laboratory experiments aiming to identify hollows analogue materials. The analysis of our estimated model parameters imply that the Tyagaraja and Canova hollow walls are more backscattering and smoother than the crater floors, in agreement with independent phase ratio analyses. Our results suggest that the hollow forming material is made of roundish particles or particles with a high density of scattering centers, such as holes, vesicles or fractures, consistent with the release of volatiles as part of the hollows' formation mechanism.

The PHEMU21 catalogue and astrometric results of the observations of the mutual occultations and eclipses of the Galilean satellites of Jupiter made in 2021

ABSTRACT 2021 was the year of Jupiter’s equinox, that is the Sun and the Earth passed through the equatorial plane of the planet and therefore the orbital planes of its main satellites. This occurrence made it possible to observe mutual occultations and eclipses between the satellites. Our former experience shows that observations of such events provide accurate astrometric data that can be used to obtain new information on the dynamics of the Galilean satellites. The observations are a series of photometric measurements of a satellite which are carried out through the organization of a world wide campaign of observations thus maximizing the number and the quality of the data obtained. This work focuses on processing the photometric observations of the mutual occultations and eclipses of the Galilean satellites of Jupiter made during the international campaign in 2021. The final goal is to derive new accurate astrometric data. We used an accurate photometric model of mutual events in conjunction with the accuracy of observation. We obtained and processed the 84 light curves obtained during the campaign. As compared with the current best ephemerides, the rms of ‘O–C’ residuals are equal to 49 and 48 mas in right ascension and declination, respectively.

Albedo variegation on Comet 67P/Churyumov–Gerasimenko

ABSTRACT We here study the level of albedo variegation on the nucleus of Comet 67P/Churyumov–Gerasimenko. This is done by fitting the parameters of a standard photometric phase function model to disc–average radiance factor data in images acquired by the Rosetta/OSIRIS Narrow Angle Camera in the orange filter. Local discrepancies between the observed radiance factor and the disc–average solution are interpreted as a proxy $\mathcal {W}$ of the local single–scattering albedo. We find a wide range $0.02 \lesssim \mathcal {W}\lesssim 0.09$ around an average of $\mathcal {W}=0.055$. The observed albedo variegation is strongly correlated with nucleus morphology – smooth terrain is brighter, and consolidated terrain is darker, than average. Furthermore, we find that smooth terrain darken prior to morphological changes, and that stratigraphically low terrain (with respect to the centre of each nucleus lobe) is brighter than stratigraphically high terrain. We propose that the observed albedo variegation is due to differences in porosity and the coherent effect: compaction causes small brighter particles to act collectively as larger optically effective particles that are darker. Accordingly, we consider the dark consolidated terrain materials more compacted than smooth terrain materials, and darkening of the latter is due to subsidence.

Spectrophotometry of the lunar regolith using the Chang’E-3 Panoramic Camera (PCAM)

Aims. We present a multiband photometric study of the lunar mare surface based on in situ measurements using the Panoramic Camera on board the Chang'E-3 mission. We aim to obtain photometric properties and to investigate the differences in measuring sites in various conditions. Methods. Data were collected with a vast range of phase angles from 0.5° to 160°, probing 0–5° phases to constrain the opposition effect (OE) and phases above 120° to constrain the phase function. Stray light and spectral corrections were conducted to calibrate the regolith’s bidirectional reflectance distribution function. We employed the Hapke photometric model to describe the regolith’s photometric properties. Results. Phase functions and Hapke parameters at three measuring sites were retrieved. The mare regolith at the measuring sites had a single-scattering albedo (SSA) of 0.17-0.21 for the green channel and exhibited backscattering and a strong OE. The coherent backscatter opposition effect (CBOE) represented the majority of OE, with a proportion of around 80% for pristine regolith. The phase ratio curves were arch-shaped, and the arch positions of the curves were associated with grain size.

Pre and post-landing atmospheric optical depths at the Zhurong landing site on Mars retrieved using a single-image-based approach

The atmosphere of Mars has complex photometric processes due to multiple scattering and absorption by the suspended aerosols. The atmospheric optical depth is an indicator of the aerosol concentrations and can be used to model the contribution of atmospheric scattering, thereby correcting surface spectra. The atmospheric optical depth is also important in analysing the variations of atmospheric dust and evaluating the risks facing landing and roving missions. Retrieving the atmospheric optical depth from a single image is desirable as simultaneous stereo observations of the Martian surface are not common. However, most of the existing single-image-based methods rely on shadows in the image, which can be challenging to identify when the site is smooth or when the atmosphere becomes turbid. In this paper, we present a method of retrieving the atmospheric optical depth from a single image based solely on non-shadowed surfaces. This method was validated using HiRISE images and measurements acquired by rovers on the Martian surface. The method achieved an RMSE of 2%–7% in most cases, depending on the different surface photometric models used. The results indicated that aerosol scattering parameters have less impact on the retrievals than the surface photometric properties, likely due to the fact that the data is optically thin. The optical depths at the Zhurong landing site before and after landing were estimated using the proposed method. The results show that the optical depths first decreased and then increased, with the turning point being around the landing date, indicating that the Zhurong rover landed at an appropriate time. The proposed method is of significance for the analysis of Martian atmospheric dust and surface spectra with better spatio-temporal resolutions.

ADOP

In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel. The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance. Because our pipeline includes photometric parameters, e.g. exposure and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, e.g. with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points. https://github.com/darglein/ADOP