Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figure

Neural Gas based classification of Globular Clusters

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.Comment: 15 pages, 3 figures, to appear in the Volume of Springer Communications in Computer and Information Science (CCIS). arXiv admin note: substantial text overlap with arXiv:1710.0390

A new high-precision strong lensing model of the galaxy cluster MACS J0416.1-2403

We present a new high-precision parametric strong lensing model of the galaxy cluster MACS J0416.1-2403, at z=0.396, which takes advantage of the MUSE Deep Lensed Field (MDLF), with 17.1h integration in the northeast region of the cluster, and Hubble Frontier Fields data. We spectroscopically identify 182 multiple images from 48 background sources at 0.9<z<6.2, and 171 cluster member galaxies. Several multiple images are associated to individual clumps in multiply lensed resolved sources. By defining a new metric, which is sensitive to the gradients of the deflection field, we show that we can accurately reproduce the positions of these star-forming knots despite their vicinity to the model critical lines. The high signal-to-noise ratio of the MDLF spectra enables the measurement of the internal velocity dispersion of 64 cluster galaxies, down to m(F160W)=22. This allowed us to independently estimate the contribution of the subhalo mass component of the lens model from the measured Faber-Jackson scaling relation. Our best reference model, which represents a significant step forward compared to our previous analyses, was selected from a comparative study of different mass parametrizations. The root-mean-square displacement between the observed and model-predicted image positions is only 0.40", which is 33% smaller than in all previous models. The mass model appears to be particularly well constrained in the MDLF region. We characterize the robustness of the magnification map at varying distances from the model critical lines and the total projected mass profile of the cluster.Comment: 15 pages, 15 figures. Accepted for publication in Astronomy & Astrophysics (A&A). Lens models are available at www.fe.infn.it/astro/lensin

A persistent excess of galaxy-galaxy strong lensing observed in galaxy clusters

Context. Previous studies have revealed that the estimated probability of galaxy-galaxy strong lensing in observed galaxy clusters exceeds the expectations from the Λ cold dark matter cosmological model by one order of magnitude. Aims. We aim to understand the origin of this excess by analyzing a larger set of simulated galaxy clusters, and investigating how the theoretical expectations vary under different adopted prescriptions and numerical implementations of star formation and feedback in simulations. Methods. We performed a ray-tracing analysis of 324 galaxy clusters from the THREE HUNDRED project, comparing the GADGET-X and GIZMO-SIMBA runs. These simulations, which start from the same initial conditions, were performed with different implementations of hydrodynamics and galaxy formation models tailored to match different observational properties of the intracluster medium and cluster galaxies. Results. We find that galaxies in the GIZMO-SIMBA simulations develop denser stellar cores than their GADGET-X counterparts. Consequently, their probability for galaxy-galaxy strong lensing is higher by a factor of ∼ 3. This increment is still insufficient to fill the gap with observations as a discrepancy by a factor ∼ 4 still persists. In addition, we find that several simulated galaxies have Einstein radii that are too large compared to observations. Conclusions. We conclude that a persistent excess of galaxy-galaxy strong lensing exists in observed galaxy clusters. The origin of this discrepancy with theoretical predictions is still unexplained in the framework of the cosmological hydrodynamical simulations. This might signal a hitherto unknown issue with either the simulation methods or our assumptions regarding the standard cosmological modelWe thank the anonymous referee for their constructive comments. MM was supported by INAF Grant “The Big-Data era of cluster lensing”. We acknowledge financial contributions from PRIN-MIUR2017WSCC32 and 2020SKSTHZ, INAF “main-stream” grants 1.05.01.86.20 and 1.05.01.86.31, by the ICSC National Recovery and Resilience Plan (PNRR) Project ID CN-00000013 “Italian Research Center on High-Performance Computing, Big Data and Quantum Computing” funded by MUR Missione 4 Componente 2 Investimento 1.4 – Next Generation EU (NGEU), by the INFN InDark grant and by ASI n.2018-23-HH.0 grant. CG and AR are supported by INAF Theory Grant “Illuminating Dark Matter using Weak Lensing by Cluster Satellites”. WC, AK and GY acknowledge Ministerio de Ciencia e Innovación (Spain) for partial financial support under research grant PID2021-122603NB-C21. WC is also supported by the STFC AGP Grant ST/V000594/1 and the Atracción de Talento Contract no. 2020-T1/TIC-19882 granted by the Comunidad de Madrid in Spain. We would also like to thank the Red Española de Supercomputación (RES) for granting us computing resources in the MareNostrum supercomputer at Barcelona Supercomputing Center, where all the simulations used in this work have been performed. AK further thanks The Charlatans for the only one I know. This work was in part performed at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-2210452. This material is partially supported by a grant from the Simons Foundatio

The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters

Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the LCDM cosmological model. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We quantify the impact of the numerical resolution and AGN feedback scheme adopted in cosmological simulations on the predicted GGSL probability and determine if varying these simulation properties can alleviate the gap with observations. We repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. We find that improving the mass resolution by a factor of ten and twenty-five, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor between three and six. However, we notice that such simulations form overly massive subhalos whose contribution to the lensing cross-section would be significant while their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross-sections are subhalos with smaller masses, that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent in simulations.Comment: 13 pages, 11 figures. Submitted for publication on Astronomy and Astrophysic

Deep Learning in Galaxy Clusters

Gli ammassi di galassie hanno un ruolo importante nella cosmologia e nell'astrofisica moderne. Essi figurano come laboratori cosmici nei quali è possibile studiare la formazione e l'evoluzione delle galassie, e migliorare la nostra comprensione della materia oscura usando metodi basati sulla dinamica o su lenti gravitazionali. Come potenti lenti gravitazionali, gli ammassi agiscono da telescopi cosmici estendendo il limite di rilevamento di sorgenti deboli e rivelando galassie lontane. In questo contesto, survey dedicate con il Telescopio Spaziale Hubble (HST) ed estese campagne spettroscopiche hanno fornito dati di straordinaria qualità. Tuttavia, la ricchezza di questi dati non può essere paragonata all'impressionante volume che i futuri telescopi (come Euclid, Vera Rubin Observatory o James Webb Space Telescope) genereranno nei prossimi anni. Il volume e la complessità di questi nuovi dataset possono essere gestiti in modo efficiente con metodi di machine learning e deep learning, che consentono l'esplorazione di correlazioni nascoste all'interno di spazi multidimensionali. Come prima applicazione, abbiamo implementato architetture di deep learning per selezionare i membri di ammassi di galassie, con redshift in 0.2 - 0.6, un primo passo fondamentale per una varietà di studi, come l'evoluzione delle galassie in ambienti densi, stime di massa degli ammassi, modelli di strong lensing. Una volta addestrate con un ampio campione di sorgenti spettroscopicamente confermate (osservazioni VLT VIMOS e MUSE), le reti neurali convolutive (CNN) sono state utilizzate per separare i membri dalle sorgenti di background e foreground, utilizzando solo immagini multi-banda HST, evitando così il complicato e time-consuming processo di estrazione di misure fotometriche. Abbiamo eseguito diversi esperimenti, determinando che le CNN possono classificare i membri con un tasso di purezza-completezza ~90%, mostrando risultati stabili nello spazio dei parametri. Come secondo passo, ci siamo concentrati sull'identificazione dei galaxy-galaxy strong-lenses (GGSL) in ammassi, che possono essere utilizzati per studiare la distribuzione di massa degli ammassi, tracciare la popolazione di sub-aloni attorno ai membri. In questo lavoro, abbiamo optato per una metodologia che combina la necessità di simulare un gran numero di GGSL per addestrare reti neurali, mantenendo la complessità delle osservazioni reali. Abbiamo utilizzato le mappe di deflection angle stimate da modelli ad alta precisione di lensing dell’ammasso, disponibili per 8 cluster (con redshift in 0.2 - 0.6), per simulare migliaia di esempi realistici nelle immagini HST. Abbaimo determinato che le CNN possono rilevare un'ampia frazione di GGSL reali, con un numero limitato di falsi negativi. Abbiamo processato centinaia di membri (spettroscopicamente confermati o selezionati con la CNN), per testare la capacità di generalizzazione delle CNN e per cercare candidati GGSL. Infine, abbiamo implementato uno strumento di cross-correlazione 3D per i dati dello integral field spectrograph MUSE per misurare redshift in modo automatizzato e computazionalmente efficiente. L'estrazione di informazioni spettroscopiche ci consente di costruire dataset per addestrare reti neurali, confermare la membership di galassie, o misurare i redshift della lente e della sorgente in eventi di lensing. Ottimizzato per essere eseguito su processori grafici, questo strumento può elaborare un intero cubo MUSE in poche decine di secondi, cross-correlando 90000 spettri con un set di template. Anche se lo strumento è ancora in fase di sviluppo, i risultati preliminari sembrano piuttosto promettenti e saranno presto applicati di routine sui dati MUSE. Le metodologie sviluppate possono essere estese oltre i dati HST con uno sforzo relativamente modesto e promettono di avere importanti applicazioni per le imminenti survey di prossima generazione.Galaxy clusters play an important role in modern cosmology and astrophysics. They act as cosmic laboratories where we can study galaxy formation and evolution, and improve our understanding of the nature of Dark Matter using dynamical and gravitational lensing methods. As powerful gravitational lenses, clusters can be used as natural cosmic telescopes thus extending our detection limit of faint sources and revealing the most distant galaxies. In this context, dedicated surveys with Hubble Space Telescope (HST) and ground-based extensive spectroscopic campaigns have provided data with extraordinary quality. The richness of these data sets, however, cannot be compared with the impressive data volume that upcoming surveys (such as Euclid, Vera Rubin Observatory or James Webb Space Telescope) will generate over the next years. The volume and the complexity of these new datasets can be efficiently dealt using machine learning and deep learning methods, which enable the exploration of hidden correlations within a multi-dimensional parameter space. In this thesis, we take advantage of this multidisciplinary tool to enable many scientific investigations of cluster internal structure and background source population. As a first application, we implemented deep learning architectures to select galaxy cluster members in galaxy clusters, in the redshift range 0.2 - 0.6, which is a critical first step for a variety of studies, such as galaxy evolution in dense environments, cluster mass estimates, strong lensing models. By using HST multi-band images alone, convolution neural networks (CNNs) were used to disentangle members from background and foreground sources, once they were trained with a large sample of spectroscopically confirmed sources (VLT VIMOS and MUSE observations), thus avoiding the complicated and time consuming photometric measurement process. We performed several experiments, finding that CNNs can classify members with a purity-completeness rate of ~90%, and showing stable results across the parameter space. As a second step, we focused on the identification of galaxy-galaxy strong lenses (GGSL) in galaxy clusters, which can be used to study the internal mass distribution of clusters, traced by the sub-halo population around cluster member galaxies, and can later be compared with cosmological simulations. In this work, we opted for a methodology that combines the need to simulate a large number of GGSL to train deep neural networks, while maintaining the imaging complexity of real observations. By exploiting high-precision cluster lens models available for 8 clusters (with redshift in 0.2 - 0.6), we used the estimated deflection angle maps to simulate thousands of realistic strong-lenses in real HST. We found that deep networks can detect a large fraction of real GGSLs, with a limited number of false negative events. We processed hundreds of members (spectroscopically confirmed or selected with the CNN), to test deep model generalization capabilities and to search for GGSL candidates. Finally, we implemented a 3D spectroscopy cross-correlation tool on the MUSE integral field spectrograph data to measure redshifts in an automated and computationally efficient fashion. The mining of spectroscopic information allows us to build datasets used to train neural networks, confirm cluster galaxy membership, measure the redshift of lens and source in lensing events. Optimized to be executed on graphic processors, this tool can process an entire MUSE dataset in a few tens of seconds, by cross-correlating 90000 spectra included in the data cube with a sample of spectral templates. Even though the tool is still under development our preliminary results appear rather promising and will soon be applied routinely on MUSE data. The methodologies developed in this thesis can be extended beyond the HST imaging data with a relatively modest effort and promise to have important applications with the upcoming next-generation facilities

The powerful lens galaxy cluster PLCK G287.0+32.9 (θE ∼ 43″)

We present a new high-precision strong-lensing model of PLCK G287.0+32.9, a massive lens galaxy cluster at z = 0:383, with the aim of obtaining an accurate estimation of its effective Einstein radius and total mass distribution.We also present a spectroscopic catalog containing accurate redshift measurements for close to 500 objects up to redshift z = 6, including multiply lensed sources and cluster member galaxies. Methods. We exploited high-quality spectroscopic data from the Multi Unit Spectroscopic Explorer (MUSE), covering a central 3 arcmin2 region of the cluster. We supplemented the spectroscopic catalog by including redshift measurements from VIsible MultiObject Spectrograph (VIMOS) and DEep Imaging Multi-Object Spectrograph (DEIMOS). We identified 129 spectroscopic cluster member galaxies with redshift values of 0:360 z 0:405, and mF160W 21. We complemented this galaxy cluster member sample with 24 photometric members identified with a convolutional neural network (CNN) approach.We also identified 114 multiple images from 28 background sources, of which 84 images from 16 sources are new and the remaining ones have already been identified in previous works. From these, we extracted 'golden sample' of 47 secure multiple images and used them, together with the selected cluster member, to build and optimize several strong-lensing models with the software lenstool. Results. The best-fitting lens model shows a root mean square (RMS) separation value between the predicted and observed positions of the multiple images of 0 0:0 75. Using its predictive power, we found three new multiple images and we confirm the configuration of three systems of multiple images that were not used for the optimization of the model. For a source at a redshift of zs = 2, we found a cluster with an Einstein radius of E = 43:400 0:100. This value is in agreement with previous estimates and corresponds to a total mass enclosed in the critical curve of ME = 3:33+0:02 0:07 1014 M. Conclusions. The combined application of ancillary Hubble Space Telescope (HST) imaging, VIMOS and DEIMOS data, and the new MUSE spectroscopic observations allowed us to build a new lens model of the galaxy cluster PLCK G287.0+32.9, with an improvement in terms of reconstructing the observed positions of the multiple images of a factor of 2:5 with respect to previous models. The derived total mass distribution confirms this cluster to be a very prominent gravitational lens, with an effective Einstein radius of E 4300. We were also able to construct an extensive spectroscopic catalog containing 490 objects, of which 153 are bright cluster members with mF160W 21, and 114 are multiple images.ISSN:0004-6361ISSN:1432-074