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Monocular depth estimation has greatly improved in the recent years but models predicting metric depth still struggle to generalize across diverse camera poses and datasets. While recent supervised methods mitigate this issue by leveraging ground prior information at inference, their adaptability to self-supervised settings is limited due to the additional challenge of scale recovery. Addressing this gap, we propose in this paper a novel constraint on ground areas designed specifically for the self-supervised paradigm. This mechanism not only allows to accurately recover the scale but also ensures coherence between the depth prediction and the ground prior. Experimental results show that our method surpasses existing scale recovery techniques on the KITTI benchmark and significantly enhances model generalization capabilities. This improvement can be observed by its more robust performance across diverse camera rotations and its adaptability in zero-shot conditions with previously unseen driving datasets such as DDAD.

Exploring quantum electrodynamics in the most extreme conditions, where electron-positron pairs can emerge in the presence of a strong background field, is now becoming possible in Compton collisions between ultraintense lasers and energetic electrons. In the strong-field regime, the colliding electron emits $\gamma$ rays that decay into pairs in the strong laser field. While the combination of conventional accelerators and lasers of sufficient power poses significant challenges, laser-plasma accelerators offer a promising alternative for producing the required multi-GeV electron beams. To overcome the complexities of colliding these beams with another ultraintense laser pulse, we propose a novel scheme in which a single laser pulse both accelerates the electrons and collides with them after self-focusing in a dedicated plasma section and reflecting off a plasma mirror. The laser intensity boost in the plasma allows the quantum interaction parameter to be greatly increased. Using full-scale numerical simulations, we demonstrate that a single 100 J laser pulse can achieve a deep quantum regime with electric fields in the electron rest frame as high as $\chi_e\sim 5$ times the Schwinger critical field, resulting in the production of about 40 pC of positrons.

Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing comprehensive environmental information - remains unexplored. We introduce MULi-Ev, the first online, deep learning-based framework tailored for the extrinsic calibration of event cameras with LiDAR. This advancement is instrumental for the seamless integration of LiDAR and event cameras, enabling dynamic, real-time calibration adjustments that are essential for maintaining optimal sensor alignment amidst varying operational conditions. Rigorously evaluated against the real-world scenarios presented in the DSEC dataset, MULi-Ev not only achieves substantial improvements in calibration accuracy but also sets a new standard for integrating LiDAR with event cameras in mobile platforms. Our findings reveal the potential of MULi-Ev to bolster the safety, reliability, and overall performance of event-based perception systems in autonomous driving, marking a significant step forward in their real-world deployment and effectiveness., Comment: Accepted at CVPR 2024 Workshop on Autonomous Driving. Copyright 2024 IEEE

Compact laser-plasma acceleration of fast ions has made great strides since its discovery over two decades ago, resulting in the current generation of high-energy ($\geq 100\,\rm MeV$) ultracold beams over ultrashort ($\leq 1\,\rm ps$) durations. To unlock broader applications of these beams, we need the ability to tailor the ion energy spectrum. Here, we present a scheme that achieves precisely this by accelerating protons in a "lighthouse" fashion, whereby the highest-energy component of the beam is emitted in a narrow cone, well separated from the lower-energy components. This is made possible by a two-stage interaction in which the rear surface of the target is first set into rapid motion before the main acceleration phase. This approach offers the additional advantages of leveraging a robust sheath acceleration process in standard micron-thick targets and being optically controllable., Comment: 9 pages, 6 figures

Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems, such as self-driving vehicles and mobile robots. Traditional techniques often require manual intervention or specific environments, making them labour-intensive and error-prone. Existing deep learning-based self-calibration methods focus on small realignments and still rely on initial estimates, limiting their practicality. In this paper, we present PseudoCal, a novel self-calibration method that overcomes these limitations by leveraging the pseudo-LiDAR concept and working directly in the 3D space instead of limiting itself to the camera field of view. In typical autonomous vehicle and robotics contexts and conventions, PseudoCal is able to perform one-shot calibration quasi-independently of initial parameter estimates, addressing extreme cases that remain unsolved by existing approaches., Comment: British Machine Vision Conference (BMVC) 2023

Terahertz (THz) emissions from fast electron and ion currents driven in relativistic, femtosecond laser-foil interactions are examined theoretically. We first consider the radiation from the energetic electrons exiting the backside of the target. Our kinetic model takes account of the coherent transition radiation due to these electrons crossing the plasma-vacuum interface as well as of the synchrotron radiation due to their deflection and deceleration in the sheath field they set up in vacuum. After showing that both mechanisms tend to largely compensate each other when all the electrons are pulled back into the target, we investigate the scaling of the net radiation with the sheath field strength. We then demonstrate the sensitivity of this radiation to a percent-level fraction of escaping electrons. We also study the influence of the target thickness and laser focusing. The same sheath field that confines most of the fast electrons around the target rapidly sets into motion the surface ions. We describe the THz emission from these accelerated ions and their accompanying hot electrons by means of a plasma expansion model that allows for finite foil size and multidimensional effects. Again, we explore the dependencies of this radiation mechanism on the laser-target parameters. Under conditions typical of current ultrashort laser-solid experiments, we find that the THz radiation from the expanding plasma is much less energetic -- by one to three orders of magnitude -- than that due to the early-time motion of the fast electrons.

Deciphering the conditions under which neutron captures occur in the Universe to synthesize heavy elements is an endeavour pursued since the 1950s, but that has proven elusive up to now due to the experimental difficulty of generating the extreme neutron fluxes required. It has been evoked that laser-driven (pulsed) neutron sources could produce neutron beams with characteristics suitable to achieve nucleosynthesis in the laboratory. In this scheme, the laser first generates an ultra-high-current, high-energy proton beam, which is subsequently converted into a dense neutron beam. Here we model, in a self-consistent manner, the transport of laser-accelerated protons through the neutron converter, the subsequent neutron generation and propagation, and finally the neutron capture reactions in a gold ($^{197}$Au) chosen as an illustrative example. Using the parameters of present-day available lasers, as well as of those foreseeable in the near future, we find that the final yield of the isotopes containing two more neutrons than the seed nuclei is negligible. Our investigation highlights that the areal density of the laser-driven neutron source is a critical quantity and that it would have to be increased by several orders of magnitude over the current state of the art in order to offer realistic prospects for laser-based generation of neutron-rich isotopes., Comment: 10 pages, 5 figures

Event cameras do not produce images, but rather a continuous flow of events, which encode changes of illumination for each pixel independently and asynchronously. While they output temporally rich information, they lack any depth information which could facilitate their use with other sensors. LiDARs can provide this depth information, but are by nature very sparse, which makes the depth-to-event association more complex. Furthermore, as events represent changes of illumination, they might also represent changes of depth; associating them with a single depth is therefore inadequate. In this work, we propose to address these issues by fusing information from an event camera and a LiDAR using a learning-based approach to estimate accurate dense depth maps. To solve the "potential change of depth" problem, we propose here to estimate two depth maps at each step: one "before" the events happen, and one "after" the events happen. We further propose to use this pair of depths to compute a depth difference for each event, to give them more context. We train and evaluate our network, ALED, on both synthetic and real driving sequences, and show that it is able to predict dense depths with an error reduction of up to 61% compared to the current state of the art. We also demonstrate the quality of our 2-depths-to-event association, and the usefulness of the depth difference information. Finally, we release SLED, a novel synthetic dataset comprising events, LiDAR point clouds, RGB images, and dense depth maps., Comment: Accepted for SCIA 2023. For the project page, see https://vbrebion.github.io/ALED/

Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception of different view points, namely cooperative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total volume of information received from others, to limit communication costs and message processing time. For this, we propose a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different generative models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm, and compared them to a broadcasting policy and a policy randomly selecting areas. In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL. Experiments were conducted in the driving simulator CARLA. Our best models reached on average a gain of 25% of the total complementary information, while only requesting about 5% of the ego-vehicle's perceptual field. This trade-off is adjustable through the interpretable hyperparameters of our reward function., Comment: Standalone version of the last chapter of Maxime Chaveroche's doctoral thesis, published in October 2021

Ongoing progress in laser and accelerator technology opens new possibilities in high-field science, notably to investigate the largely unexplored strong-field quantum electrodynamics (SFQED) regime where electron-positron pairs can be created directly from light-matter or even light-vacuum interactions. Laserless strategies such as beam-beam collisions have also been proposed to access the nonperturbative limit of SFQED. Here we report on a concept to probe SFQED by harnessing the interaction between a high-charge, ultrarelativistic electron beam and a solid conducting target. When impinging onto the target surface, the beam self fields are reflected, partly or fully, depending on the beam shape; in the rest frame of the beam electrons, these fields can exceed the Schwinger field, thus triggering SFQED effects such as quantum nonlinear inverse Compton scattering and nonlinear Breit-Wheeler electron-positron pair creation. Through reduced modeling and kinetic numerical simulations, we show that this single-beam setup can achieve interaction conditions similar to those envisioned in beam-beam collisions, but in a simpler and more controllable way owing to the automatic overlap of the beam and driving fields. This scheme thus eases the way to precision studies of SFQED and is also a promising milestone towards laserless studies of nonperturbative SFQED.

Plasma wakefield acceleration (PWFA) has shown illustrious progress and resulted in an impressive demonstration of tens of GeV particle acceleration in meter-long single structures. To reach even higher energies in the 1 TeV to 10 TeV range, a promising scheme is channeling acceleration in solid-density plasmas within crystals or nanostructures. The E336 experiment studies the beam-nanotarget interaction with the highly compressed electron bunches available at the FACET-II accelerator. These studies furthermore involve an in-depth research on dynamics of beam-plasma instabilities in ultra-dense plasma, its development and suppression in structured media like carbon nanotubes and crystals, and its potential use to transversely modulate the electron bunch., Comment: submitted to Snowmass'2021 Accelerator Frontier (AF6), 16 pages, 9 Figs

Contemporary ultraintense, short-pulse laser systems provide extremely compact setups for the production of high-flux neutron beams, such as those required for nondestructive probing of dense matter, research on neutron-induced damage in fusion devices or laboratory astrophysics studies. Here, by coupling particle-in-cell and Monte Carlo numerical simulations, we examine possible strategies to optimise neutron sources from ion-induced nuclear reactions using 1-PW, 20-fs-class laser systems. To improve the ion acceleration, the laser-irradiated targets are chosen to be ultrathin solid foils, either standing alone or preceded by a plasma layer of near-critical density to enhance the laser focusing. We compare the performance of these single- and double-layer targets, and determine their optimum parameters in terms of energy and angular spectra of the accelerated ions. These are then sent into a converter to generate neutrons via nuclear reactions on beryllium and lead nuclei. Overall, we identify configurations that result in neutron yields as high as $\sim 10^{10}\,\rm n\,sr^{-1}$ in $\sim 1$-cm-thick converters or instantaneous neutron fluxes above $10^{23}\,\rm n\,cm^{-2}\,s^{-1}$ at the backside of $\lesssim 100$-$\mu$m-thick converters. Considering a realistic repetition rate of one laser shot per minute, the corresponding time-averaged neutron yields are predicted to reach values ($\gtrsim 10^7\,\rm n \,sr^{-1}\,s^{-1}$) well above the current experimental record, and this even with a mere thin foil as a primary target. A further increase in the time-averaged yield up to above $10^8\,\rm sr^{-1}\,s^{-1}$ is foreseen using double-layer targets., Comment: 16 pages, 8 figures, 2 tables

Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased sensors show their limits with blur and over- or underexposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras. We formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low- and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346 x 260 pixels and 77Hz at 1280 x 720 pixels., Comment: 13 pages, journal paper

Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency, which is actually a kind of postprocessing. In this paper, we reconsider the CRF in feature space for point cloud segmentation because it can capture the structure of features well to improve the representation ability of features rather than simply smoothing. Therefore, we first model the point cloud features with a continuous quadratic energy model and formulate its solution process as a message-passing graph convolution, by which it can be easily integrated into a deep network. We theoretically demonstrate that the message passing in the graph convolution is equivalent to the mean-field approximation of a continuous CRF model. Furthermore, we build an encoder-decoder network based on the proposed continuous CRF graph convolution (CRFConv), in which the CRFConv embedded in the decoding layers can restore the details of high-level features that were lost in the encoding stage to enhance the location ability of the network, thereby benefiting segmentation. Analogous to the CRFConv, we show that the classical discrete CRF can also work collaboratively with the proposed network via another graph convolution to further improve the segmentation results. Experiments on various point cloud benchmarks demonstrate the effectiveness and robustness of the proposed method. Compared with the state-of-the-art methods, the proposed method can also achieve competitive segmentation performance., Comment: 20 pages + 8 pages (supplemental material)

We present the results of the first commissioning phase of the ``short focal length'' area (SFA) of the Apollon laser facility (located in Saclay, France), which was performed with the first available laser beam (F2), scaled to a nominal power of one petawatt. Under the conditions that were tested, this beam delivered on target pulses of 10 J average energy and 24 fs duration. Several diagnostics were fielded to assess the performance of the facility. The on-target focal spot, its spatial stability, the temporal intensity profile prior to the main pulse, as well as the resulting density gradient formed at the irradiated side of solid targets, have been thoroughly characterized, with the goal of helping users design future experiments. Emissions of energetic electrons, ions, and electromagnetic radiation were recorded, showing good laser-to-target coupling efficiency and an overall performance comparable with that of similar international facilities. This will be followed in 2022 by a further commissioning stage at the multi-petawatt level.

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Dempster's rule. The main approaches exploit either the structure of Boolean lattices or the information contained in belief sources. Each has its merits depending on the situation. In this paper, we propose sequences of graphs for the computation of the zeta and M\"obius transformations that optimally exploit both the structure of distributive semilattices and the information contained in belief sources. We call them the Efficient M\"obius Transformations (EMT). We show that the complexity of the EMT is always inferior to the complexity of algorithms that consider the whole lattice, such as the Fast M\"obius Transform (FMT) for all DST transformations. We then explain how to use them to fuse two belief sources. More generally, our EMTs apply to any function in any finite distributive lattice, focusing on a meet-closed or join-closed subset. This article extends our work published at the international conference on Scalable Uncertainty Management (SUM). It clarifies it, brings some minor corrections and provides implementation details such as data structures and algorithms applied to DST., Comment: Extension of an article published in the proceedings of the international conference on Scalable Uncertainty Management (SUM) in 2019

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Dempster's rule. Yet, few research had been conducted to reduce the complexity of computations for the conjunctive and disjunctive decompositions of evidence, which are at the core of other important methods of information fusion. In this paper, we propose a method designed to exploit the actual evidence (information) contained in these decompositions in order to compute them. It is based on a new notion that we call focal point, derived from the notion of focal set. With it, we are able to reduce these computations up to a linear complexity in the number of focal sets in some cases. In a broader perspective, our formulas have the potential to be tractable when the size of the frame of discernment exceeds a few dozen possible states, contrary to the existing litterature. This article extends (and translates) our work published at the french conference GRETSI in 2019., Comment: Extension of an article published in the proceedings of the french conference GRETSI 2019

An electron or electron-positron beam streaming through a plasma is notoriously prone to micro-instabilities. For a dilute ultrarelativistic infinite beam, the dominant instability is a mixed mode between longitudinal two-stream and transverse filamentation modes, with a phase velocity oblique to the beam velocity. A spatiotemporal theory describing the linear growth of this oblique mixed instability is proposed, which predicts that spatiotemporal effects generally prevail for finite-length beams, leading to a significantly slower instability evolution than in the usually assumed purely temporal regime. These results are accurately supported by particle-in-cell (PIC) simulations. Furthermore, we show that the self-focusing dynamics caused by the plasma wakefields driven by finite-width beams can compete with the oblique instability. Analyzed through PIC simulations, the interplay of these two processes in realistic systems bears important implications for upcoming accelerator experiments on ultrarelativistic beam-plasma interactions.

Due to their high cost of acquisition and operation, there are still a limited number of high-yield, high-flux neutron source facilities worldwide. In this context, laser-driven neutron sources offer a promising, cheaper alternative to those based on large-scale accelerators, with, in addition, the potential of generating compact neutron beams of high brightness and ultra-short duration. In particular, the predicted capability of next-generation petawatt (PW)-class lasers to accelerate protons beyond the 100 MeV range should unlock efficient neutron generation through spallation reactions. In this paper, this scenario is investigated numerically through particle-in-cell and Monte Carlo simulations, modeling, respectively, the laser acceleration of protons from thin-foil targets and their subsequent conversion into neutrons in secondary heavy-ion targets. Laser parameters relevant to the 1 PW LMJ-PETAL and 1-10 PW Apollon systems are considered. Under such conditions, neutron fluxes exceeding $10^{23}\,\rm n\,cm^{-2}\,s^{-1}$ are predicted, opening up attractive fundamental and applicative prospects., Comment: 16 pages, 13 figures

LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such point-level classification results need to be converted into a dense representation, that can be used by an autonomous vehicle. We thus secondly present an evidential road mapping algorithm, that fuses consecutive road detection results. We benefitted from a reinterpretation of logistic classifiers, which can be seen as generating a collection of simple evidential mass functions. An evidential grid map that depicts the road can then be obtained, by projecting the classification results from RoadSeg into grid cells, and by handling moving objects via conflict analysis. The system was trained and evaluated on real-life data. A python implementation maintains a 10 Hz framerate. Since road labels were needed for training, a soft labelling procedure, relying lane-level HD maps, was used to generate coarse training and validation sets. An additional test set was manually labelled for evaluation purposes. So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features., Comment: 39 pages

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has been done to reduce the time complexity of information fusion with Dempster's rule, which is a pointwise multiplication of two zeta transforms, and optimal general algorithms have been found to get the complete definition of these transforms. Yet, it is shown in this paper that the zeta transform and its inverse, the M\"obius transform, can be exactly simplified, fitting the quantity of information contained in belief functions. Beyond that, this simplification actually works for any function on any partially ordered set. It relies on a new notion that we call focal point and that constitutes the smallest domain on which both the zeta and M\"obius transforms can be defined. We demonstrate the interest of these general results for DST, not only for the reduction in complexity of most transformations between belief representations and their fusion, but also for theoretical purposes. Indeed, we provide a new generalization of the conjunctive decomposition of evidence and formulas uncovering how each decomposition weight is tied to the corresponding mass function., Comment: Accepted for publication in Elsevier Information Sciences Journal

We study numerically the mechanisms of proton acceleration in gas-foil targets driven by an ultraintense femtosecond laser pulse. The target consists of a near-critical-density hydrogen gas layer of a few tens of microns attached to a solid carbon foil with a contaminant thin proton layer at its back side. Two-dimensional particle-in-cell simulations show that, at optimal gas density, the maximum energy of the contaminant protons is increased by a factor of $\sim 4$ compared to a single foil target. This improvement originates from the near-complete laser absorption into relativistic electrons in the gas. Several energetic electron populations are identified, and their respective effect on the proton acceleration is quantified by computing the electrostatic fields that they generate at the protons' positions. While each of those electron groups is found to contribute substantially to the overall accelerating field, the dominant one is the relativistic thermal bulk that results from the nonlinear wakefield excited in the gas, as analyzed recently by Debayle et al. [New J. Phys. 19, 123013 (2017)]. Our analysis also reveals the important role of the neighboring ions in the acceleration of the fastest protons, and the onset of multidimensional effects caused by the time-increasing curvature of the proton layer., Comment: 9 pages, 5 figures

Sources of high-energy photons have important applications in almost all areas of research. However, the photon flux and intensity of existing sources is strongly limited for photon energies above a few hundred keV. Here we show that a high-current ultrarelativistic electron beam interacting with multiple submicrometer-thick conducting foils can undergo strong self-focusing accompanied by efficient emission of gamma-ray synchrotron photons. Physically, self-focusing and high-energy photon emission originate from the beam interaction with the near-field transition radiation accompanying the beam-foil collision. This near field radiation is of amplitude comparable with the beam self-field, and can be strong enough that a single emitted photon can carry away a significant fraction of the emitting electron energy. After beam collision with multiple foils, femtosecond collimated electron and photon beams with number density exceeding that of a solid are obtained. The relative simplicity, unique properties, and high efficiency of this gamma-ray source open up new opportunities for both applied and fundamental research including laserless investigations of strong-field QED processes with a single electron beam., Comment: 17 pages, 11 figures, matches published article

Using high-resolution, two-dimensional particle-in-cell simulations, we investigate numerically the mechanisms of terahertz (THz) emissions in submicron-thick carbon solid foils driven by ultraintense ($\sim 10^{20}\,\rm W\,cm^{-2}$), ultrashort ($30\,\rm fs$) laser pulses at normal incidence. The considered range of target thicknesses extends down to the relativistic transparency regime that is known to optimize ion acceleration by femtosecond laser pulses. By disentangling the fields emitted by longitudinal and transverse currents, our analysis reveals that, within the first picosecond after the interaction, THz emission occurs in bursts as a result of coherent transition radiation by the recirculating hot electrons and antenna-type emission by the shielding electron currents traveling along the fast-expanding target surfaces.

Terahertz pulse generation by ultra-intense two-color laser fields ionizing gases with near- to far-infrared carrier wavelength is studied from particle-in-cell (PIC) simulations. For long wavelength ($10.6\ \mu$m) promoting a large ratio of electron density over critical, photoionization is shown to catastrophically enhance the plasma wakefield, causing a net downshift in the optical spectrum and exciting THz fields with tens of GV/m amplitude in the laser direction. This emission is accompanied by coherent transition radiation (CTR) of comparable amplitude due to wakefield-driven electron acceleration. We analytically evaluate the fraction of CTR energy up to 30 % of the total radiated emission including the particle self-field and numerically calibrate the efficiency of the matched blowout regime for electron densities varied over three orders of magnitude., Comment: 5 pages, 5 figures

We present experimental measurements of the femtosecond time-scale generation of strong magnetic-field fluctuations during the interaction of ultrashort, moderately relativistic laser pulses with solid targets. These fields were probed using low-emittance, highly relativistic electron bunches from a laser wakefield accelerator, and a line-integrated $B$-field of $2.70 \pm 0.39\,\rm kT\,\mu m$ was measured. Three-dimensional, fully relativistic particle-in-cell simulations indicate that such fluctuations originate from a Weibel-type current filamentation instability developing at submicron scales around the irradiated target surface, and that they grow to amplitudes strong enough to broaden the angular distribution of the probe electron bunch a few tens of femtoseconds after the laser pulse maximum. Our results highlight the potential of wakefield-accelerated electron beams for ultrafast probing of relativistic laser-driven phenomena.

Laser wakefield acceleration of electrons usually offers an axisymmetry around the laser propagation axis. Thus, the accelerating electrons that are focused on axis often execute small transverse oscillations. In this Article, we propose a simple scheme to break this symmetry, which enhances the transverse wiggling of electrons and boosts the betatron radiation emission. Through 3D particle-in-cell simulations, we show that sending the laser with a small angle of incidence on a transverse plasma density gradient generates an asymmetric wakefield. It first provokes injection and then increases the wiggling of the electrons through the transverse shifting of the wakefield axis which occurs when the laser pulse leaves the gradient. Consequently, we show that the radiated energy per unit of charge can increase by a factor $>20$ when using this scheme, and that the critical energy of the radiation quintuples compared with a reference case without the transverse density gradient., Comment: 6 pages, 4 figures

We measure the emission of energetic electrons from the interaction between ultrashort laser pulses and a solid density plasma in the relativistic regime. We detect an electron beam that only appears with few-cycle pulses (< 10 fs) and large plasma scale lengths ($L > \lambda_0$). Numerical simulations, in agreement with the experiments, reveal that these electrons are accelerated by a laser wakefield. Plasma waves are indeed resonantly excited by the few-cycle laser pulses in the near-critical-density region of the plasma. Electrons are then injected by ionization into the plasma waves and accelerated to relativistic energies. This study provides an unprecedented insight into the physics happening in the few-cycle regime.

In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving the target task, but would be difficult to extract from the limited amount of data available on the target task. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in fine-tuning for retaining the features learned on the source task. In this paper, we investigate several regularization schemes that explicitly promote the similarity of the final solution with the initial model. We show the benefit of having an explicit inductive bias towards the initial model, and we eventually recommend a simple $L^2$ penalty with the pre-trained model being a reference as the baseline of penalty for transfer learning tasks., Comment: Accepted at ICML 2018

Photon engineering can be exploited to control the nonlinear evolution of the drive pulse in a laser-plasma accelerator (LPA), offering new avenues to tailor electron beam phase space on a femtosecond time scale. One promising option is to drive an LPA with an incoherent stack of two sub-Joule, multi-TW pulses of different colors. Slow self-compression of the bi-color optical driver delays electron dephasing, boosting electron beam energy without accumulation of a massive low-energy tail. The modest energy of the stack affords kHz-scale repetition rate at manageable laser average power. Propagating the stack in a preformed plasma channel induces periodic self-focusing in the trailing pulse, causing oscillations in the size of accelerating bucket. The resulting periodic injection generates, over a mm-scale distance, a train of GeV-scale electron bunches with 5D brightness exceeding $10^{17}$ A/m$^2$. This unconventional comb-like beam, with femtosecond synchronization and controllable energy spacing of components, emits, via Thomson scattering, a train of highly collimated gigawatt $\gamma$-ray pulses. Each pulse, corresponding to a distinct energy band between 2.5 and 25 MeV, contains over $10^6$ photons., Comment: 5 pages, 3 figures; to appear in Proceedings of Third European Advanced Accelerator Concepts Workshop, Nuclear Instruments and Methods in Physics Research Section A (2018)

Recent progress in laser-driven plasma acceleration now enables the acceleration of electrons to several gigaelectronvolts. Taking advantage of these novel accelerators, ultra-short, compact and spatially coherent X-ray sources called betatron radiation have been developed and applied to high-resolution imaging. However, the scope of the betatron sources is limited by a low energy efficiency and a photon energy in the 10's of kiloelectronvolt range, which for example prohibits the use of these sources for probing dense matter. Here, based on three-dimensional particle-in-cell simulations, we propose an original hybrid scheme that combines a low-density laser-driven plasma accelerator with a high-density beam-driven plasma radiator, and thereby considerably increases the photon energy and the radiated energy of the betatron source. The energy efficiency is also greatly improved, with about 1% of the laser energy transferred to the radiation, and the gamma-ray photon energy exceeds the megaelectronvolt range when using a 15 J laser pulse. This high-brilliance hybrid betatron source opens the way to a wide range of applications requiring MeV photons, such as the production of medical isotopes with photo-nuclear reactions, radiography of dense objects in the defense or industrial domains and imaging in nuclear physics., Comment: 5 pages, 4 figures

Terahertz (THz) emission by two-color, ultrashort optical pulses interacting with underdense helium gases at ultrahigh intensities ($> 10^{19}\,\mathrm{W/cm}^2$) is investigated by means of 3D particle-in-cell simulations. The THz field is shown to be produced by two mechanisms occurring sequentially, namely, photoionization-induced radiation (PIR) by the two-color pulse and coherent transition radiation (CTR) by the wakefield-accelerated electrons escaping the plasma. For plasmas of atomic densities $> 10^{17}\,\mathrm{cm}^{-3}$, CTR proves to be the dominant process, providing THz bursts with field strength as high as $100\,\mathrm{GV/m}$ and energy in excess of $1\,\mathrm{mJ}$. Analytical models are developed for both the PIR and CTR processes, which correctly reproduce the simulation data., Comment: 5 pages, 4 figures