Quasar microlensing light curve analysis using deep machine learning

We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analyzing hundreds of thousands of simulated light curves with respect to the accretion disc size and temperature profile. Our results indicate that it is possible to successfully classify very large numbers of diverse light curve data and measure the accretion disc structure. The detailed shape of the accretion disc brightness profile is found to play a negligible role, in agreement with Mortonson et al. (2005). The speed and efficiency of our deep machine learning approach is ideal for quantifying physical properties in a `big-data' problem setup. This proposed approach looks promising for analyzing decade-long light curves for thousands of microlensed quasars, expected to be provided by the Large Synoptic Survey Telescope.Comment: 11 pages, 7 figures, accepted for publication in MNRA

Dimensionality reduction and sparse representations in computer vision

The proliferation of camera equipped devices, such as netbooks, smartphones and game stations, has led to a significant increase in the production of visual content. This visual information could be used for understanding the environment and offering a natural interface between the users and their surroundings. However, the massive amounts of data and the high computational cost associated with them, encumbers the transfer of sophisticated vision algorithms to real life systems, especially ones that exhibit resource limitations such as restrictions in available memory, processing power and bandwidth. One approach for tackling these issues is to generate compact and descriptive representations of image data by exploiting inherent redundancies. We propose the investigation of dimensionality reduction and sparse representations in order to accomplish this task. In dimensionality reduction, the aim is to reduce the dimensions of the space where image data reside in order to allow resource constrained systems to handle them and, ideally, provide a more insightful description. This goal is achieved by exploiting the inherent redundancies that many classes of images, such as faces under different illumination conditions and objects from different viewpoints, exhibit. We explore the description of natural images by low dimensional non-linear models called image manifolds and investigate the performance of computer vision tasks such as recognition and classification using these low dimensional models. In addition to dimensionality reduction, we study a novel approach in representing images as a sparse linear combination of dictionary examples. We investigate how sparse image representations can be used for a variety of tasks including low level image modeling and higher level semantic information extraction. Using tools from dimensionality reduction and sparse representation, we propose the application of these methods in three hierarchical image layers, namely low-level features, mid-level structures and high-level attributes. Low level features are image descriptors that can be extracted directly from the raw image pixels and include pixel intensities, histograms, and gradients. In the first part of this work, we explore how various techniques in dimensionality reduction, ranging from traditional image compression to the recently proposed Random Projections method, affect the performance of computer vision algorithms such as face detection and face recognition. In addition, we discuss a method that is able to increase the spatial resolution of a single image, without using any training examples, according to the sparse representations framework. In the second part, we explore mid-level structures, including image manifolds and sparse models, produced by abstracting information from low-level features and offer compact modeling of high dimensional data. We propose novel techniques for generating more descriptive image representations and investigate their application in face recognition and object tracking. In the third part of this work, we propose the investigation of a novel framework for representing the semantic contents of images. This framework employs high level semantic attributes that aim to bridge the gap between the visual information of an image and its textual description by utilizing low level features and mid level structures. This innovative paradigm offers revolutionary possibilities including recognizing the category of an object from purely textual information without providing any explicit visual example

Quasar microlensing light-curve analysis using deep machine learning

We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analysing hundreds of thousands of simulated light curves with respect to the accretion disc size and temperature profile. Our results indicate that it is possible to successfully classify very large numbers of diverse light-curve data and measure the accretion disc structure. The detailed shape of the accretion disc brightness profile is found to play a negligible role. The speed and efficiency of our deep machine learning approach is ideal for quantifying physical properties in a `big-data' problem set-up. This proposed approach looks promising for analysing decade-long light curves for thousands of microlensed quasars, expected to be provided by the Large Synoptic Survey Telescope

Compressed sensing reconstruction of convolved sparse signals

Abstract—This paper addresses the problem of efficient sam-pling and reconstruction of sparse spike signals, which have been convolved with low-pass filters. A modified compressed sensing (CS) framework is proposed, termed dictionary-based deconvolution CS (DDCS) to achieve this goal. DDCS builds on the assumption that a low-pass filter can be represented sparsely in a dictionary of blurring atoms. Identification of both the sparse spike signal and the sparsely parameterized blurring function is performed by an alternating scheme that minimizes each variable independently, while keeping the other constant. Simulation results reveal that the proposed DDSS scheme achieves an improved reconstruction performance when compared to traditional CS recovery. I

ELGAR—a European Laboratory for Gravitation and Atom-interferometric Research

Gravitational waves (GWs) were observed for the first time in 2015, one century after Einstein predicted their existence. There is now growing interest to extend the detection bandwidth to low frequency. The scientific potential of multi-frequency GW astronomy is enormous as it would enable to obtain a more complete picture of cosmic events and mechanisms. This is a unique and entirely new opportunity for the future of astronomy, the success of which depends upon the decisions being made on existing and new infrastructures. The prospect of combining observations from the future space-based instrument LISA together with third generation ground based detectors will open the way toward multi-band GW astronomy, but will leave the infrasound (0.1–10 Hz) band uncovered. GW detectors based on matter wave interferometry promise to fill such a sensitivity gap. We propose the European Laboratory for Gravitation and Atom-interferometric Research (ELGAR), an underground infrastructure based on the latest progress in atomic physics, to study space–time and gravitation with the primary goal of detecting GWs in the infrasound band. ELGAR will directly inherit from large research facilities now being built in Europe for the study of large scale atom interferometry and will drive new pan-European synergies from top research centers developing quantum sensors. ELGAR will measure GW radiation in the infrasound band with a peak strain sensitivity of $3.3{\times}1{0}^{-22}/\sqrt{\text{Hz}}$ at 1.7 Hz. The antenna will have an impact on diverse fundamental and applied research fields beyond GW astronomy, including gravitation, general relativity, and geology.AB acknowledges support from the ANR (project EOSBECMR), IdEx Bordeaux—LAPHIA (project OE-TWR), theQuantERA ERA-NET (project TAIOL) and the Aquitaine Region (projets IASIG3D and USOFF).XZ thanks the China Scholarships Council (No. 201806010364) program for financial support. JJ thanks ‘AssociationNationale de la Recherche et de la Technologie’ for financial support (No. 2018/1565).SvAb, NG, SL, EMR, DS, and CS gratefully acknowledge support by the German Space Agency (DLR) with funds provided by the Federal Ministry for Economic Affairs and Energy (BMWi) due to an enactment of the German Bundestag under Grants No. DLR∼50WM1641 (PRIMUS-III), 50WM1952 (QUANTUS-V-Fallturm), and 50WP1700 (BECCAL), 50WM1861 (CAL), 50WM2060 (CARIOQA) as well as 50RK1957 (QGYRO)SvAb, NG, SL, EMR, DS, and CS gratefully acknowledge support by ‘Niedersächsisches Vorab’ through the ‘Quantum- and Nano-Metrology (QUANOMET)’ initiative within the project QT3, and through ‘Förderung von Wissenschaft und Technik in Forschung und Lehre’ for the initial funding of research in the new DLR-SI Institute, the CRC 1227 DQ-mat within the projects A05 and B07DS gratefully acknowledges funding by the Federal Ministry of Education and Research (BMBF) through the funding program Photonics Research Germany under contract number 13N14875.RG acknowledges Ville de Paris (Emergence programme HSENS-MWGRAV), ANR (project PIMAI) and the Fundamental Physics and Gravitational Waves (PhyFOG) programme of Observatoire de Paris for support. We also acknowledge networking support by the COST actions GWverse CA16104 and AtomQT CA16221 (Horizon 2020 Framework Programme of the European Union).The work was also supported by the German Space Agency (DLR) with funds provided by the Federal Ministry for Economic Affairs and Energy (BMWi) due to an enactment of the German Bundestag under Grant Nos.∼50WM1556, 50WM1956 and 50WP1706 as well as through the DLR Institutes DLR-SI and DLR-QT.PA-S, MN, and CFS acknowledge support from contracts ESP2015-67234-P and ESP2017-90084-P from the Ministry of Economy and Business of Spain (MINECO), and from contract 2017-SGR-1469 from AGAUR (Catalan government).SvAb, NG, SL, EMR, DS, and CS gratefully acknowledge support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2123 QuantumFrontiers—390837967 (B2) andCRC1227 ‘DQ-mat’ within projects A05, B07 and B09.LAS thanks Sorbonne Universités (Emergence project LORINVACC) and Conseil Scientifique de l'Observatoire de Paris for funding.This work was realized with the financial support of the French State through the ‘Agence Nationale de la Recherche’ (ANR) in the frame of the ‘MRSEI’ program (Pre-ELGAR ANR-17-MRS5-0004-01) and the ‘Investissement d'Avenir’ program (Equipex MIGA: ANR-11-EQPX-0028, IdEx Bordeaux—LAPHIA: ANR-10-IDEX-03-02).Peer Reviewe

Sparse representations and distance learning for attribute based category recognition

Abstract. While traditional approaches in object recognition require the specification of training examples from each class and the application of class specific classifiers, in real world situations, the immensity of the number of image classes makes this task daunting. A novel approach in object recognition is attribute based classification, where instead of training classifiers for the recognition of specific object class instances, classifiers are trained on attributes of the object images and these attributes are subsequently used for the object recognition. The attributes based paradigm offers significant advantages including the ability to train classifiers without any visual examples. We begin by discussing a scenario for object recognition on mobile devices where the attribute prediction and the attribute-to-class mapping are decoupled in order to meet the specific resource constraints of mobile systems. We next present two extensions on the attribute based classification paradigm by introducing alternative approaches in attribute prediction and attribute-to-class mapping. For the attribute prediction, we employ the recently proposed Sparse Representations Classification scheme that offers significant benefits compared to the previous SVM based approaches, such as increased accuracy and elimination of the training stage. For the attribute-to-class mapping, we employ a Distance Metric Learning algorithm that automatically infers the significance of each attribute instead of assuming uniform attribute importance. The benefits of the proposed extensions are validated through experimental results