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We search for unresolved X-ray emission from lensed sources in the FOV of 11 CLASH clusters with Chandra data. We consider the solid angle in the lens plane corresponding to a magnification $\mu>1.5$, that amounts to a total of ~100 arcmin$^2$. Our main goal is to assess the efficiency of massive clusters as cosmic telescopes to explore the faint end of X-ray extragalactic source population. We search for X-ray emission from strongly lensed sources identified in the optical, and perform an untargeted detection of lensed X-ray sources. We detect X-ray emission only in 9 out of 849 lensed/background optical sources. The stacked emission of the sources without detection does not reveal any signal in any band. Based on the untargeted detection, we find 66 additional X-ray sources that are consistent with being lensed sources. After accounting for completeness and sky coverage, we measure for the first time the soft- and hard-band number counts of lensed X-ray sources. The results are consistent with current modelization of the AGN population distribution. The distribution of de-lensed fluxes of the sources identified in moderately deep CLASH fields reaches a flux limit of ~$10^{-16}$ and ~$10^{-15}$ erg/s/cm$^{2}$ in the soft and hard bands, respectively. We conclude that, in order to match the depth of the CDFS exploiting massive clusters as cosmic telescopes, the required number of cluster fields is about two orders of magnitude larger than that offered by the 20 years Chandra archive. A significant step forward will be made when future X-ray facilities, with ~1' angular resolution and large effective area, will allow the serendipitous discovery of rare, strongly lensed high-$z$ X-ray sources, enabling the study of faint AGN activity in early Universe and the measurement of gravitational time delays in the X-ray variability of multiply imaged AGN., Comment: Accepted for publication in A&A

We report on ALMA observations of D1, a system at z~6.15 with stellar mass M_* ~ 10^7 M_sun containing globular cluster (GC) precursors, strongly magnified by the galaxy cluster MACS J0416.1-2403. Since the discovery of GC progenitors at high redshift, ours is the first attempt to probe directly the physical properties of their neutral gas through infrared observations. A careful analysis of our dataset, performed with a suitable procedure designed to identify faint narrow lines and which can test various possible values for the unknown linewidth value, allowed us to identify a 4-sigma tentative detection of [CII] emission with intrinsic luminosity L_CII=(2.9 +/- 1.4) 10^6 L_sun, one of the lowest values ever detected at high redshift. This study offers a first insight on previously uncharted regions of the L_CII-SFR relation. Despite large uncertainties affecting our measure of the star formation rate, if taken at face value our estimate lies more than 1 dex below the values observed in local and high redshift systems. Our weak detection indicates a deficiency of [CII] emission, possibly ascribed to various explanations, such as a low-density gas and/or a strong radiation field caused by intense stellar feedback, and a low metal content. From the non-detection in the continuum we derive constraints on the dust mass, with 3-sigma upper limit values as low as a few 10^4 M_sun, consistent with the values measured in local metal-poor galaxies., Comment: MNRAS, accepted. 15 pages, 5 figures

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

Context: A census of faint and tiny star forming complexes at high redshift is key to improving our understanding of reionizing sources, galaxy growth and the formation of globular clusters. Aims: We present the MUSE Deep Lensed Field (MDLF) program. Methods: We describe Deep MUSE observations of 17.1 hours integration on a single pointing over the Hubble Frontier Field galaxy cluster MACS~J0416. Results: We confirm spectroscopic redshifts for all 136 multiple images of 48 source galaxies at 0.910 for individual objects down to de-lensed magnitude 28-30 suggesting that they are common in such faint sources. Conclusions:Deep MUSE observations, in combination with existing HST imaging, allowed us to:(1) confirm redshifts for extremely faint high-z sources;(2) peer into their internal clumps (down to 100-200 pc scale);(3) in some cases break down such clumps into bound star clusters (<20 pc scale);(4) double the number of constraints for the lens model,reaching an unprecedented set of 182 bona-fide multiple images and confirming up to 213 galaxy cluster members. These results demonstrate the power that JWST and future ELTs will have when combined to study gravitational telescopes.[abridged], Comment: 43 pages total (17 pages main body); 5 appendices. Accepted for publication in A&A on October 26

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the classification capabilities of convolution neural networks (CNNs) to identify galaxy cluster members (CLMs) by using Hubble Space Telescope (HST) images of 15 galaxy clusters at redshift 0.19

Cold dark matter (CDM) constitutes most of the matter in the Universe. The interplay between dark and luminous matter in dense cosmic environments like galaxy clusters is studied theoretically using cosmological simulations. Observed gravitational lensing is used to test and characterize the properties of substructures - the small-scale distribution of dark matter - in clusters. An apt metric, the probability of strong lensing events produced by dark matter substructure, is devised and computed for 11 galaxy clusters. We report that observed cluster substructures are more efficient lenses than predicted by CDM simulations, by more than an order of magnitude. We suggest that hitherto undiagnosed systematic issues with simulations or incorrect assumptions about the properties of dark matter could explain our results., Comment: 56 pages, 15 figures. Published in Science

Lensing by galaxy clusters is a versatile probe of cosmology and extragalactic astrophysics, but the accuracy of some of its predictions is limited by the simplified models adopted to reduce the (otherwise untractable) number of degrees of freedom. We aim at cluster lensing models where the parameters of all cluster-member galaxies are free to vary around some common scaling relations with non-zero scatter, and deviate significantly from them if and only if the data require it. We have devised a Bayesian hierarchical inference framework, which enables the determination of all lensing parameters and of the scaling-relation hyperparameters, including intrinsic scatter, from lensing constraints and (if given) stellar kinematic measurements. We achieve this through BayesLens, a purpose-built wrapper around common parametric lensing codes for the lensing likelihood that can sample the posterior on parameters and hyperparameters, which we release with this paper. We have run functional tests of our code against simple mock cluster lensing datasets with realistic uncertainties. The parameters and hyperparameters are recovered within their 68% credibility ranges, and the positions of all the "observed" multiple images are accurately reproduced by the BayeLens best-fit model, without overfitting. We have shown that an accurate description of cluster member galaxies is attainable, despite the large number of degrees of freedom, through fast and tractable inference. This extends beyond the state-of-the-art of current cluster lensing models. The precise impact on studies of cosmography, galaxy evolution and high-redshift galaxy populations can then be quantified on real galaxy clusters. While other sources of systematics exist and may be significant in real clusters, our results show that the contribution of intrinsic scatter in cluster-member populations can now be controlled., Comment: A&A in press (first subm.to A&A on 19/11/2019)

To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion. However, these solutions face a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring temporal information from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising from a set of views depicting the target object. Our proposal - Views Knowledge Distillation (VKD) - pins this visual variety as a supervision signal within a teacher-student framework, where the teacher educates a student who observes fewer views. As a result, the student outperforms not only its teacher but also the current state-of-the-art in Image-To-Video by a wide margin (6.3% mAP on MARS, 8.6% on Duke-Video-ReId and 5% on VeRi-776). A thorough analysis - on Person, Vehicle and Animal Re-ID - investigates the properties of VKD from a qualitatively and quantitatively perspective. Code is available at https://github.com/aimagelab/VKD., Comment: Accepted by ECCV 2020

In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset., Comment: Accepted at ICPR2020

Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California, over a four-month period. It consists of 170,000 scenes, where each scene is 25 seconds long and captures the perception output of the self-driving system, which encodes the precise positions and motions of nearby vehicles, cyclists, and pedestrians over time. On top of this, the dataset contains a high-definition semantic map with 15,242 labelled elements and a high-definition aerial view over the area. We show that using a dataset of this size dramatically improves performance for key self-driving problems. Combined with the provided software kit, this collection forms the largest and most detailed dataset to date for the development of self-driving machine learning tasks, such as motion forecasting, motion planning and simulation. The full dataset is available at http://level5.lyft.com/., Comment: Presente at CoRL2020

Context: The shape of the mass density profiles of cosmological halos informs us of the nature of DM and DM-baryons interactions. Previous estimates of the inner slope of the mass density profiles of clusters of galaxies are in opposition to predictions derived from numerical simulations of CDM. Aims: We determine the inner slope of the DM density profile of a massive cluster of galaxies, Abell S1063 (RXC J2248.7$-$4431) at $z=0.35$, with a dynamical analysis based on an extensive spectroscopic campaign carried out with the VIMOS and MUSE spectrographs at the ESO VLT. This new data set provides an unprecedented sample of 1234 spectroscopic members, 104 of which are located in the cluster core (R < 200 kpc), extracted from the MUSE integral field spectroscopy. The latter also allows the stellar velocity dispersion profile of the brightest cluster galaxy (BCG) to be measured out to 40 kpc. Methods: We used an upgraded version of the MAMPOSSt technique to perform a joint maximum likelihood fit to the velocity dispersion profile of the BCG and to the velocity distribution of cluster member galaxies over a radial range from 1 kpc to the virial radius (~ 2.7 Mpc). Results: We find a value of $\gamma_{DM} =0.99 \pm 0.04$ for the inner logarithmic slope of the DM density profile after marginalizing over all the other parameters. The newly determined dynamical mass profile is found to be in excellent agreement with the mass density profiles obtained from the independent X-ray hydrostatic analysis based on deep Chandra data, as well as the strong and weak lensing analyses. Our value of \gamma_{DM} is in very good agreement with predictions from cosmological CDM simulations. We will extend our analysis to more clusters in future works. If confirmed on a larger cluster sample, our result makes this DM model more appealing than alternative models., Comment: Accepted on Astronomy and Astrophysics

We review methods for coherently controlling Rydberg quantum states of atomic ensembles using Adiabatic Rapid Passage and Stimulated Raman Adiabatic Passage. These methods are commonly used for population inversion in simple two-level and three-level systems. We show that adiabatic techniques allow us to control population and phase dynamics of complex entangled states of mesoscopic atomic ensembles for quantum information processing with Rydberg atoms. We also propose several schemes of single-qubit and two-qubit gates based on adiabatic passage, Rydberg blockade and F\"{o}rster resonances in Rydberg atoms., Comment: 13 pages, 15 figures. This is a tutorial, submitted to Journal of Physics B. arXiv admin note: text overlap with arXiv:1606.08198, arXiv:1310.3921

Oligothiophenes are pi-conjugated semiconducting and fluorescent molecules whose self-assembly properties are widely investigated for application in organic electronics, optoelectronics, biophotonics and sensing. We report here an approach to the preparation of crystalline oli-gothiophene nano/micro-fibers based on the use of a sulfur overrich quaterthiophene building block, -T4S4-, containing in its covalent network all the information needed to promote the di-rectional, pi-pi stacking driven, self-assembly of Ar-T4S4-Ar oligomers into fibers with hierar-chical supramolecular arrangement from nano- to microscale. We show that when Ar varies from unsubstituted thiophene to thiophene substituted with electron withdrawing groups, a wide redistribution of the molecular electronic charge takes place without substantially affecting the aggregation modalities of the oligomer. In this way a structurally comparable series of fibers is obtained having progressively varying optical properties, redox potentials, photoconductivity and type of prevailing charge carriers (from p- to n-type). A thorough characterization of the fi-bers based on SEM, CD, CV, X-ray diffraction, UV-vis and PL spectroscopies, photoconductivi-ty and KPFM measurements is reported. With the aid of DFT calculations, combined with X-ray data, a model accounting for the growth of the fibers from molecular to nano- and microscale is proposed. We believe that the simple strategy outlined in this study allows to establish a straightforward correlation between the molecular structure of the components and the function-al properties of the corresponding self-assembled nano/micro-fibers.