arXiv subjects

Anton T. Jaelani

Publications and source records attributed to Anton T. Jaelani.

At least 19 recordsLinked to original sources

arXiv2026

Improving Lens Modelling from Ground-Based Imaging with Deconvolution

Accurate and precise mass modelling of strong lensing systems is essential for extracting insights into cosmology, galaxy environments, and the nature of dark matter. The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), which has mapped $\sim$1000 square degrees of the sky, discovered over 1000 definite or probable galaxy-scale gravitational lens candidates. As the resolution of ground-based imaging poses challenges for accurate lens modelling, high-resolution imaging is traditionally sought after for detailed analyses of these systems. In this work, we instead propose applying the STARRED algorithm, which leverages starlet wavelet regularisation, as a preprocessing step before lens modelling to maximise the scientific output of the large HSC-SSP sample. We test this approach on seven HSC-like mock lensing systems with different lensing configurations. Our results show that the resolution boost from $\sim$0.6" to $\sim$0.15" enables improved accuracy and sub-3% precision measurements of key lensing parameters such as the Einstein radius and the mass axis ratio for ground-based images. These results pave the way for accurate measurements of key strong lensing quantities from a large sample of ground-based observations.

astro-ph.GA↗

arXiv2025

GraViT: Transfer Learning with Vision Transformers and MLP-Mixer for Strong Gravitational Lens Discovery

Gravitational lensing offers a powerful probe into the properties of dark matter and is crucial to infer cosmological parameters. The Legacy Survey of Space and Time (LSST) is predicted to find O(10^5) gravitational lenses over the next decade, demanding automated classifiers. In this work, we introduce GraViT, a PyTorch pipeline for gravitational lens detection that leverages extensive pretraining of state-of-the-art Vision Transformer (ViT) models and MLP-Mixer. We assess the impact of transfer learning on classification performance by examining data quality (source and sample size), model architecture (selection and fine-tuning), training strategies (augmentation, normalization, and optimization), and ensemble predictions. This study reproduces the experiments in a previous systematic comparison of neural networks and provides insights into the detectability of strong gravitational lenses on that common test sample. We fine-tune ten architectures using datasets from HOLISMOKES VI and SuGOHI X, and benchmark them against convolutional baselines, discussing complexity and inference-time analysis.

cs.CV↗

arXiv2025

Combining neural networks with galaxy light subtraction for discovering strong lenses in the HSC SSP

Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used on large imaging datasets. In this work, we apply a new technique to improve the performance of supervised neural networks by subtracting the central (lensing) galaxy light from both the training and test datasets. We use multiband imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) as our training and test datasets. By subtracting the lensing galaxy light, we increase the contrast of the lensed source compared to the original imaging data. We also apply the light subtraction to non-lenses in order to compare them to the light-subtracted lenses. Residual features resulting from poor light subtraction can adversely affect the performance of networks trained on the subtracted images alone. We find that combining the light-subtracted images with the original gri-band images for training and classification can overcome this and improve the overall classification accuracy. We find the area under the receiver operating characteristic curve can be improved to 0.841 using the combination of the fiducial images and light-subtracted images, compared to 0.808 for the fiducial imaging dataset alone. This may be a promising technique for improving future lens searches using CNNs.

astro-ph.GA↗

arXiv2024

Systematic comparison of neural networks used in discovering strong gravitational lenses

Efficient algorithms are being developed to search for strong gravitational lens systems owing to increasing large imaging surveys. Neural networks have been successfully used to discover galaxy-scale lens systems in imaging surveys such as the Kilo Degree Survey, Hyper-Suprime Cam (HSC) Survey and Dark Energy Survey over the last few years. Thus, it has become imperative to understand how some of these networks compare, their strengths and the role of the training datasets as most of the networks make use of supervised learning algorithms. In this work, we present the first-of-its-kind systematic comparison and benchmarking of networks from four teams that have analysed the HSC Survey data. Each team has designed their training samples and developed neural networks independently but coordinated apriori in reserving specific datasets strictly for test purposes. The test sample consists of mock lenses, real (candidate) lenses and real non-lenses gathered from various sources to benchmark and characterise the performance of each of the network. While each team's network performed much better on their own constructed test samples compared to those from others, all networks performed comparable on the test sample with real (candidate) lenses and non-lenses. We also investigate the impact of swapping the training samples amongst the teams while retaining the same network architecture. We find that this resulted in improved performance for some networks. These results have direct implications on measures to be taken for lens searches with upcoming imaging surveys such as the Rubin-Legacy Survey of Space and Time, Roman and Euclid.

astro-ph.GA↗

arXiv2024

A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal{O}(10^3)$ to $\mathcal{O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong-lens experts to remove false positives which is only set to increase with these surveys. In this work we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC) survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false positive rate of $10^{-3}$ can be achieved with a completeness of $46\%$, compared to $34\%$ for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses.

astro-ph.GA↗

arXiv2023

Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) $-$ X. Strong Lens Finding in The HSC-SSP using Convolutional Neural Networks

We apply a novel model based on convolutional neural networks (CNNs) to identify gravitationally-lensed galaxies in multi-band imaging of the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a parent sample of 2 350 061 galaxies selected from the $\sim$ 800 deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC Wide are selected based on stringent pre-selection criteria, such as multiband magnitudes, stellar mass, star formation rate, extendedness limit, photometric redshift range, etc. Initially, the CNNs provide a total of 20 241 cutouts with a score greater than 0.9, but this number is subsequently reduced to 1 522 cutouts by removing definite non-lenses for further inspection by human eyes. We discover 43 definite and 269 probable lenses, of which 97 are completely new. In addition, out of 880 potential lenses, we recovered 289 known systems in the literature. We identify 143 candidates from the known systems that had higher confidence in previous searches. Our model can also recover 285 candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the deflector. Even though group-scale and cluster-scale lens systems were not included in the training, a sample of 32 SuGOHI-c (i.e., group/cluster-scale systems) lens candidates was retrieved. Our discoveries will be useful for ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus Spectrograph project, to measure lens and source redshifts in order to enable detailed lens modelling.

astro-ph.GA↗

arXiv2023

Survey of Gravitationally Lensed Objects in HSC Imaging (SuGOHI). IX. Discovery of Strongly Lensed Quasar Candidates

We report the discovery of new lensed quasar candidates in the imaging data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) DR4, covering $1\,310~{\rm deg}^2$ of the sky with seeing of $\approx0.6''$. In addition to two catalogs of MILLIQUAS and AllWISEAGN, which contain confirmed and candidate quasars, we preselect quasar sources using color cuts from the HSC ($grizy$) and unWISE ($W1+W2$) photometric data based on SDSS spectroscopic catalogs. We search for the presence of multiple point sources with similar color through the convolution of the Laplacian of the preselected quasar image cutouts with the Laplacian of the point spread function, resulting in a reduction of lens candidates from $1\,652\,329$ to $121\,511$ ($7.4\%$). After visual binary classification, we grade $6\,199$ ($0.4\%$) potential lenses on a scale of 0 to 3, with 3 indicating a lens and 0 indicating a non-lens. Finally we obtain 162 lens candidates with an average grade of $\geq2$, and among them, we successfully recover 18 known lenses. By fitting the light distribution and removing the known contaminants, we discover that 57 new systems contain at least two point sources and a galaxy in between, including 10 possible quadruply lensed quasars. This new sample of lens candidates exhibits a median separation of $1.26''$ and a magnitude limit of $i\approx22$. Spectroscopic or high-resolution imaging follow up on these newly discovered lensed quasar candidates will further allow their natures to be confirmed.

astro-ph.GA↗

arXiv2022

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VIII. New galaxy-scale lenses from the HSC SSP

We conduct a search for galaxy-scale strong gravitational lens systems in Data Release 4 of the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP), consisting of data taken up to the S21A semester. We select 103191 luminous red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample that have deep multiband imaging from the HSC SSP and use the YattaLens algorithm to automatically identify lens candidates with blue arc-like features. The candidates are visually inspected and graded based on their likelihood of being a lens. We find 8 definite lenses, 28 probable lenses, and 138 possible lenses. The new lens candidates generally have lens redshifts in the range $0.3 \lesssim z_{\mathrm{L}} \lesssim 0.9$, a key intermediate redshift range to study the evolution of galaxy structure. Follow-up spectroscopy will confirm these new lenses and measure source redshifts to enable detailed lens modeling.

astro-ph.GA↗

arXiv2022

HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program

We carry out a search for strong-lens systems containing high-redshift lens galaxies with the goal of extending strong-lensing-assisted galaxy evolutionary studies to earlier cosmic time. Two strong-lens classifiers are constructed from a deep residual network and trained with datasets of different lens-redshift and brightness distributions. We classify a sample of 5,356,628 pre-selected objects from the Wide-layer fields in the second public data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) by applying the two classifiers to their HSC $gri$-filter cutouts. Cutting off at thresholds that correspond to a false positive rate of $10^{-3}$ on our test set, the two classifiers identify 5,468 and 6,119 strong-lens candidates. Visually inspecting the cutouts of those candidates results in 735 grade-A or B strong-lens candidates in total, of which 277 candidates are discovered for the first time. This is the single largest set of galaxy-scale strong-lens candidates discovered with HSC data to date, and nearly half of it (331/735) contains lens galaxies with photometric redshifts above 0.6. Our discoveries will serve as a valuable target list for ongoing and scheduled spectroscopic surveys such as the Dark Energy Spectroscopic Instrument, the Subaru Prime Focus Spectrograph project, and the Maunakea Spectroscopic Explorer.

astro-ph.GA↗

arXiv2021

Properties of the Environment of Galaxies in Clusters of Galaxies CL 0024+1654 and RX J0152.7-1357

We report the results of combined analyses of X-ray and optical data of two galaxy clusters, CL 0024$+$1654 and RX J0152.7$-$1357 at redshift $z = 0.395$ and $z = 0.830$, respectively, which offer a holistic physical description of the two clusters. Our X-ray analysis yields temperature and density profile of the gas in the intra-cluster medium (ICM). Using optical photometric and spectroscopic data, complemented with mass distribution from gravitational lensing study, we investigate any possible correlation between the physical properties of the galaxy members, i.e., their color, morphology, and star formation rate (SFR) with their environments. We quantify the properties of the environment of each galaxy by galaxy number density, ICM temperature, and mass density. Although our result shows that the two clusters exhibit a weaker correlation compared to relaxed clusters, it still confirms the significant effect of the ICM on the SFRs in the galaxies. The closer relation between galaxy physical property and the condition of its immediate environment found in this work indicates the locality of galaxy evolution, even within a larger bound system such as cluster. Various physical mechanisms are suggested to explain the relation between the properties of galaxies and their environment.

astro-ph.GA↗

arXiv2021

Strongly lensed candidates from the HSC transient survey

We present a lensed quasar search based on the variability of lens systems in the HSC transient survey. Starting from 101,353 variable objects with i-band photometry in the HSC transient survey, we used a variability-based lens search method measuring the spatial extent in difference images to select potential lensed quasar candidates. We adopted conservative constraints in this variability selection and obtained 83,657 variable objects as possible lens candidates. We then ran CHITAH, a lens search algorithm based on the image configuration, on those 83,657 variable objects, and 2,130 variable objects were identified as potential lensed objects. We visually inspected the 2,130 variable objects, and seven of them are our final lensed quasar candidates. Additionally, we found one lensed galaxy candidate as a serendipitous discovery. Among the eight final lensed candidates, one is the only known quadruply lensed quasar in the survey field, HSCJ095921+020638. None of the other seven lensed candidates have been previously classified as a lens nor a lensed candidate. Three of the five final candidates with available HST images, including HSCJ095921+020638, show clues of a lensed feature in the HST images. A tightening of variability selection criteria might result in the loss of possible lensed quasar candidates, especially the lensed quasars with faint brightness or narrow separation, without efficiently eliminating the non-lensed objects; CHITAH is therefore important as an advanced examination to improve the lens search efficiency through the object configuration. The recovery of HSCJ095921+020638 proves the effectiveness of the variability-based lens search method, and this lens search method can be used in other cadenced imaging surveys, such as the upcoming Rubin Observatory Legacy Survey of Space and Time.

astro-ph.GA↗

arXiv2021

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VI. Crowdsourced lens finding with Space Warps

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. We selected a sample of $\sim300,000$ galaxies with photometric redshifts in the range $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform, as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses, for training purposes. Nearly 6,000 citizen volunteers participated in the experiment. In parallel, we used YattaLens, an automated lens finding algorithm, to look for lenses in the same sample of galaxies. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $\sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s.

astro-ph.IM↗

arXiv2021

SILVERRUSH X: Machine Learning-Aided Selection of $9318$ LAEs at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ from the HSC SSP and CHORUS Survey Data

We present a new catalog of $9318$ Ly$α$ emitter (LAE) candidates at $z = 2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to $25.0$ deg$^2$) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam (HSC SSP) and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS). We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of $94$% and a contamination rate of $1$%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include $177$ LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are $\simeq 80$-$100$% at bright NB magnitudes of $\lesssim 24$ mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage.

astro-ph.GA↗

arXiv2021

Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) -- VII. Discovery and Confirmation of Three Strongly Lensed Quasars

We present spectroscopic confirmation of three new two-image gravitationally lensed quasars, compiled from existing strong lens and X-ray catalogs. Images of HSC J091843.27$-$022007.5 show a red galaxy with two blue point sources at either side, separated by 2.26 arcsec. This system has a source and a lens redshifts $z_s=0.804$ and $z_{\ell}=0.459$, respectively, as obtained by our follow-up spectroscopic data. CXCO J100201.50$+$020330.0 shows two point sources separated by 0.85 arcsec on either side of an early-type galaxy. The follow-up spectroscopic data confirm the fainter quasar has the same redshift with the brighter quasar from the SDSS fiber spectrum at $z_s=2.016$. The deflecting foreground galaxy is a typical early-type galaxy at a redshift of $z_{\ell}=0.439$. SDSS J135944.21$+$012809.8 has two point sources with quasar spectra at the same redshift $z_s=1.096$, separated by 1.05 arcsec, and fits to the HSC images confirm the presence of a galaxy between these. These discoveries demonstrate the power of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP)'s deep imaging and wide sky coverage. Combined with existing X-ray source catalogues and follow-up spectroscopy, the HSC-SSP provides us unique opportunities to find multiple-image quasars lensed by a foreground galaxy.

astro-ph.GA↗

arXiv2020

Lensed quasar search via time variability with the HSC transient survey

Gravitationally lensed quasars are useful for studying astrophysics and cosmology, and enlarging the sample size of lensed quasars is important for multiple studies. In this work, we develop a lens search algorithm for four-image (quad) lensed quasars based on their time variability. In the development of the lens search algorithm, we constructed a pipeline simulating multi-epoch images of lensed quasars in cadenced surveys, accounting for quasar variabilities, quasar hosts, lens galaxies, and the PSF variation. Applying the simulation pipeline to the Hyper Suprime-Cam (HSC) transient survey, we generated HSC-like difference images of the mock lensed quasars from Oguri & Marshall's lens catalog. We further developed a lens search algorithm that picks out variable objects as lensed quasar candidates based on their spatial extent in the difference images. We tested our lens search algorithm with the mock lensed quasars and variable objects from the HSC transient survey. Using difference images from multiple epochs, our lens search algorithm achieves a high true-positive rate (TPR) of 90.1% and a low false-positive rate (FPR) of 2.3% for the bright quads with wide separation. With a preselection of the number of blobs in the difference image, we obtain a TPR of 97.6% and a FPR of 2.6% for the bright quads with wide separation. Even when difference images are only available in one single epoch, our lens search algorithm can still detect the bright quads with wide separation at high TPR of 97.6% and low FPR of 2.4% in the optimal seeing scenario, and at TPR of $\sim94%$ and FPR of $\sim5%$ in typical scenarios. Therefore, our lens search algorithm is promising and is applicable to ongoing and upcoming cadenced surveys, particularly the HSC transient survey and the Rubin Observatory Legacy Survey of Space and Time, for finding new lensed quasar systems. [abridged]

astro-ph.GA↗

arXiv2020

Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). V. Group-to-cluster scale lens search from the HSC-SSP Survey

We report the largest sample of candidate strong gravitational lenses belonging to the Survey of Gravitationally-lensed Objects in HSC Imaging for group-to-cluster scale (SuGOHI-c) systems. These candidates are compiled from the S18A data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. We visually inspect $\sim39,500$ galaxy clusters, selected from several catalogs, overlapping with the Wide, Deep, and UltraDeep fields, spanning the cluster redshift range $0.05<z_{cl}<1.38$. We discover 641 candidate lens systems, of which 536 are new. From the full sample, 47 are almost certainly bonafide lenses, 181 of them are highly probable lenses and 413 are possible lens systems. Additionally, we present 131 lens candidates at galaxy-scale serendipitously discovered during the inspection. We obtained spectroscopic follow-up of 10 candidates using the X-shooter. With this follow-up, we confirm 8 systems as strong gravitational lenses. Of the remaining two, one of the sources is too faint to detect any emission, and the other has a tentative redshift close to the lens redshift, but additional arcs in this system are yet to be observed spectroscopically. Since the HSC-SSP is an ongoing survey, we expect to find $\sim600$ definite or probable lenses using this procedure and even more if combined with other lens finding methods.

astro-ph.GA↗

arXiv2020

Discovery of an unusually compact lensed Lyman Break Galaxy from the Hyper Suprime-Cam Survey

We report the serendipitous discovery of HSC J0904$-$0102, a quadruply-lensed Lyman break galaxy (LBG) in the Survey of Gravitationally-lensed Objects in Hyper Suprime-Cam Imaging (SuGOHI). Owing to its point-like appearance, the source was thought to be a lensed active galactic nucleus. We obtained follow-up spectroscopic data with the Gemini Multi-Object Spectrographs on the Gemini South Telescope, which confirmed this to be a lens system. The deflecting foreground galaxy is a typical early-type galaxy at a high redshift of $z_{\ell} = 0.957$ with stellar velocity dispersion $σ_v=259\pm56$ km~s$^{-1}$. The lensed source is identified as an LBG at $z_{\rm s} = 3.403$, based on the sharp drop bluewards of Ly$α$ and other absorption features. A simple lens mass model for the system, assuming a singular isothermal ellipsoid, yields an Einstein radius of $θ_{\rm Ein} = 1. 23^{\prime\prime}$ and a total mass within the Einstein radius of $M_{\rm Ein} = (5.55\pm 0.24) \times 10^{11}M_{\odot}$ corresponding to a velocity dispersion of $σ_{\rm SIE}= 283\pm 3$ km~s$^{-1}$, which is in good agreement with the value derived spectroscopically. The most isolated lensed LBG image has a magnification of $\sim 6.5$. In comparison with other lensed LBGs and typical $z\sim4$ LBG populations, HSC J0904$-$0102 is unusually compact, an outlier at $>2σ$ confidence. Together with a previously discovered SuGOHI lens, HSC J1152$+$0047, that is similarly compact, we believe that the HSC Survey is extending LBG studies down to smaller galaxy sizes.

astro-ph.GA↗

arXiv2019

X-ray study of the double source plane gravitational lens system Eye of Horus observed with XMM-Newton

A double source plane (DSP) system is a precious probe for the density profile of distant galaxies and cosmological parameters. However, these measurements could be affected by the surrounding environment of the lens galaxy. Thus, it is important to evaluate the cluster-scale mass for detailed mass modeling. We observed the {\it Eye of Horus}, a DSP system discovered by the Subaru HSC--SSP, with XMM--Newton. We detected two X-ray extended emissions, originating from two clusters, one centered at the {\it Eye of Horus}, and the other located $\sim100$ arcsec northeast to the {\it Eye of Horus}. We determined the dynamical mass assuming hydrostatic equilibrium, and evaluated their contributions to the lens mass interior of the Einstein radius. The contribution of the former cluster is $1.1^{+1.2}_{-0.5}\times10^{12}~M_{\odot}$, which is $21-76\%$ of the total mass within the Einstein radius. The discrepancy is likely due to the complex gravitational structure along the line of sight. On the other hand, the contribution of the latter cluster is only $\sim2\%$ on the {\it Eye of Horus}. Therefore, the influence associated with this cluster can be ignored.

astro-ph.GA↗