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In the rapidly evolving field of structured light, the self-torque has been recently defined as an intrinsic property of light beams carrying time-dependent orbital angular momentum. In particular, extreme-ultraviolet (EUV) beams with self-torque -- exhibiting a topological charge that continuously varies on the subfemtosecond timescale -- are naturally produced in high-order harmonic generation (HHG) when driven by two time-delayed intense infrared vortex beams with different topological charges. Until now, the polarization state of such EUV beams carrying self-torque has been restricted to linear states due to the drastic reduction in the harmonic up-conversion efficiency with increasing the ellipticity of the driving field. In this work, we theoretically demonstrate how to control the polarization state of EUV beams carrying self-torque, from linear to circular. The extremely high sensitivity of HHG to the properties of the driving beam allows us to propose two different driving schemes to circumvent the current limitations to manipulate the polarization state of EUV beams with self-torque. Our advanced numerical simulations are complemented with the derivation of selection rules of angular momentum conservation, which enable precise tunability over the angular momentum properties of the harmonics with self-torque. The resulting high-order harmonic emission, carrying time-dependent orbital angular momentum with a custom polarization state, can expand the applications of ultrafast light-matter interactions, particularly in areas where dichroic or chiral properties are crucial, such as magnetic materials or chiral molecules.

Continuous variable quantum key distribution (CVQKD) with discrete modulation combines advantages of CVQKD, such as the implementability using readily available technologies, with advantages of discrete variable quantum key distribution, such as easier error correction procedures. We consider a prepare-and-measure CVQKD protocol, where Alice chooses from a set of four coherent states and Bob performs a heterodyne measurement, the result of which is discretised in both key and test rounds. We provide a security proof against coherent attacks in the finite-size regime, and compute the achievable key rate. To this end, we employ the generalised entropy accumulation theorem, as well as recent advances in conic optimisation, yielding improved key rates compared to previous works. At metropolitan distances, our method can provide positive key rates for the order of $10^8$ rounds., Comment: 24 pages, 5 figures

We assess the usefulness of gradient-based samplers, such as the No-U-Turn Sampler (NUTS), by comparison with traditional Metropolis-Hastings algorithms, in tomographic $3 \times 2$ point analyses. Specifically, we use the DES Year 1 data and a simulated future LSST-like survey as representative examples of these studies, containing a significant number of nuisance parameters (20 and 32, respectively) that affect the performance of rejection-based samplers. To do so, we implement a differentiable forward model using JAX-COSMO (Campagne et al. 2023), and we use it to derive parameter constraints from both datasets using the NUTS algorithm as implemented in {\S}4, and the Metropolis-Hastings algorithm as implemented in Cobaya (Lewis 2013). When quantified in terms of the number of effective number of samples taken per likelihood evaluation, we find a relative efficiency gain of $\mathcal{O}(10)$ in favour of NUTS. However, this efficiency is reduced to a factor $\sim 2$ when quantified in terms of computational time, since we find the cost of the gradient computation (needed by NUTS) relative to the likelihood to be $\sim 4.5$ times larger for both experiments. We validate these results making use of analytical multi-variate distributions (a multivariate Gaussian and a Rosenbrock distribution) with increasing dimensionality. Based on these results, we conclude that gradient-based samplers such as NUTS can be leveraged to sample high dimensional parameter spaces in Cosmology, although the efficiency improvement is relatively mild for moderate $(\mathcal{O}(50))$ dimension numbers, typical of tomographic large-scale structure analyses., Comment: 14 pages and 9 figures

Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. However, their high computational complexity and vulnerability to adversarial attacks are ongoing challenges. In this letter, approximate multipliers are used to explore DNN robustness improvement against adversarial attacks. By uniformly replacing accurate multipliers for state-of-the-art approximate ones in DNN layer models, we explore the DNNs robustness against various adversarial attacks in a feasible time. Results show up to 7% accuracy drop due to approximations when no attack is present while improving robust accuracy up to 10% when attacks applied.

High-order harmonic generation driven by intense extreme ultraviolet (EUV) fields merges quantum optics and attosecond science, giving rise to an appealing route for the generation of coherent EUV and soft X-ray light for high-resolution imaging and spectroscopies. We theoretically investigate ultrafast resonant dynamics during the interaction of He atoms with strong extreme ultraviolet pulses. At high driving intensities, we identify record fast attosecond Rabi oscillations imprinting observable signatures in the high harmonic spectrum. At field strengths suppressing the Coulomb potential barrier for all the bounded states, we demonstrate the survival of the attosecond two-level dynamics for several Rabi cycles. Consequently, this intense EUV laser-atom interaction reveals a new strong-field scenario where the resonant coupling of two-level bound-bound transitions prevails, contrasting with the dominance of bound-continuum transitions in the conventional strong-field infrared regimes. These findings set an interesting perspective for extreme attosecond nonlinear optics with intense short-wavelength fields., Comment: Main text (6 pages) and supplementary materials (5 pages)

High harmonic generation traditionally combines ~100 near-infrared laser photons, to generate bright, phase matched, extreme ultraviolet beams when the emission from many atoms adds constructively. Here we show that by guiding a mid-infrared femtosecond laser in a high pressure gas, ultrahigh harmonics can be generated up to orders > 5000, that emerge as a bright supercontinuum that spans the entire electromagnetic spectrum from the ultraviolet to > 1.6 keV, allowing in-principle the generation of pulses as short as 2.5 attoseconds. The multi-atmosphere gas pressures required for bright, phase matched emission also supports laser beam self-confinement, further enhancing the x-ray yield. Finally, the x-ray beam exhibits high spatial coherence, even though at high gas density, the recolliding electrons responsible for high harmonic generation encounter other atoms during the emission process., Comment: 32 pages, 9 figures (4 in main text, 5 in supplemental materials)

We present a cosmological analysis of the combination of the DES-Y3, KiDS-1000 and HSC-DR1 weak lensing samples under a joint harmonic-space pipeline making use of angular scales down to $\ell_{\rm max}=4500$, corresponding to significantly smaller scales ($\delta\theta\sim2.4'$) than those commonly used in cosmological weak lensing studies. We are able to do so by accurately modelling non-linearities and the impact of baryonic effects using Baccoemu. We find $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}=0.795^{+0.015}_{-0.017}$, in relatively good agreement with CMB constraints from Planck (less than $\sim1.8\sigma$ tension), although we obtain a low value of $\Omega_{\rm m}=0.212^{+0.017}_{-0.032}$, in tension with Planck at the $\sim3\sigma$ level. We show that this can be recast as an $H_0$ tension if one parametrises the amplitude of fluctuations and matter abundance in terms of variables without hidden dependence on $H_0$. Furthermore, we find that this tension reduces significantly after including a prior on the distance-redshift relationship from BAO data, without worsening the fit. In terms of baryonic effects, we show that failing to model and marginalise over them on scales $\ell\lesssim2000$ does not significantly affect the posterior constraints for DES-Y3 and KiDS-1000, but has a mild effect on deeper samples, such as HSC-DR1. This is in agreement with our ability to only mildly constrain the parameters of the Baryon Correction Model with these data, Comment: v1: 57 pages, 24 figures, comments welcome. v2: accepted version in JCAP

The landscape of ultrafast structured light pulses has recently evolved driven by the capability of high-order harmonic generation (HHG) to up-convert orbital angular momentum (OAM) from the infrared to the extreme-ultraviolet (EUV) spectral regime. Accordingly, HHG has been proven to produce EUV vortex pulses at the femtosecond timescale. Here we demonstrate the generation of attosecond vortex pulse trains, i.e. a succession of attosecond pulses with a helical wavefront, resulting from the synthesis of a comb of EUV high-order harmonics with the same OAM. By driving HHG with a polarization tilt-angle fork grating, two spatially separated circularly polarized high-order harmonic beams with order-independent OAM are created. Our work opens the route towards attosecond-resolved OAM light-matter interactions., Comment: Main text (1-14 pages) and supplemental material (14-16 pages)

Open-shell polycyclic aromatic hydrocarbons (PAHs) represent promising building blocks for carbon-based functional magnetic materials. Their magnetic properties stem from the presence of unpaired electrons localized in radical states of $\pi$ character. Consequently, these materials are inclined to exhibit spin delocalization, form extended collective states, and respond to the flexibility of the molecular backbones. However, they are also highly reactive, requiring structural strategies to protect the radical states from reacting with the environment. Here, we demonstrate that the open-shell ground state of the diradical 2-OS survives on a Au(111) substrate as a global singlet formed by two unpaired electrons with anti-parallel spins coupled through a conformational dependent interaction. The 2-OS molecule is a protected derivative of the Chichibabin's diradical, featuring a non-planar geometry that destabilizes the closed-shell quinoidal structure. Using scanning tunneling microscopy (STM), we localized the two interacting spins at the molecular edges, and detected an excited triplet state a few millielectronvolts above the singlet ground state. Mean-field Hubbard simulations reveal that the exchange coupling between the two spins strongly depends on the torsional angles between the different molecular moieties, suggesting the possibility of influencing the molecule's magnetic state through structural changes. This was demonstrated here using the STM tip to manipulate the molecular conformation, while simultaneously detecting changes in the spin excitation spectrum. Our work suggests the potential of these PAHs for a new class of all-carbon spin-crossover materials., Comment: 8 pages in the main manuscript, including 4 figures, 21 pages in the supporting info, including 19 figures

This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost Asymmetric Multicore Processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the existing features on big.LITTLE ARM processors, including: (I) source-code adaptation for parallel computing, which enables code acceleration by applying the OmpSs programming model, a task-based programming model that handles data-dependencies between tasks in a transparent fashion; (II) different OmpSs task allocation policies which take into account the processor asymmetry and can dynamically set processing resources in a more efficient way based on their particular features. The proposed mechanism can be efficiently applied to take advantage of the processing elements existing on low-cost and low-energy multi-core embedded devices executing object detection algorithms based on cascading classifiers. Although these classifiers yield the best results for detection algorithms in the field of computer vision, their high computational requirements prevent them from being used on these devices under real-time requirements. Finally, we compare the energy efficiency of a heterogeneous architecture based on asymmetric multicore processors with a suitable task scheduling, with that of a homogeneous symmetric architecture.

The ability to spatially separate the electric and magnetic fields of a light beam enables the inspection of laser-matter interactions driven solely by optical magnetic fields. However, magnetic field excitations are commonly orders of magnitude weaker than those driven by the electric field. Several studies have already demonstrated the isolation of an intense, linearly polarized magnetic field using structured light. In this work, we report the generation of isolated high intensity magnetic fields with controlled polarization state in the non-paraxial regime using structured laser beams. Our theoretical findings highlight a significant enhancement in the amplitude of the longitudinal magnetic field carried by an azimuthally polarized laser under tight-focusing conditions. Furthermore, by implementing a multiple-beam configuration, we achieve precise control over the polarization state and amplitude of the spatially isolated magnetic field. We report the generation of polarization-controlled magnetic fields reaching up to tens of Tesla, even from moderately intense laser beams of $\sim 10^{12} \, \mathrm{W}/\mathrm{cm}^2$. Our study paves the way for ultraintense interactions with circularly polarized magnetic fields from a feasible experimental setup point of view, particularly interesting to probe ferromagnetic materials and chiral media., Comment: 10 pages, 7 figures

High-order harmonic generation (HHG) arising from the non-perturbative interaction of intense light fields with matter constitutes a well-established tabletop source of coherent extreme-ultraviolet and soft X-ray radiation, which is typically emitted as attosecond pulse trains. However, ultrafast applications increasingly demand isolated attosecond pulses (IAPs), which offer great promise for advancing precision control of electron dynamics. Yet, the direct generation of IAPs typically requires the synthesis of near-single-cycle intense driving fields, which is technologically challenging. In this work, we theoretically demonstrate a novel scheme for the straightforward and compact generation of IAPs from multi-cycle infrared drivers using hollow capillary fibers (HCFs). Starting from a standard, intense multi-cycle infrared pulse, a light transient is generated by extreme soliton self-compression in a HCF with decreasing pressure, and is subsequently used to drive HHG in a gas target. Owing to the sub-cycle confinement of the HHG process, high-contrast IAPs are continuously emitted almost independently of the carrier-envelope phase (CEP) of the optimally self-compressed drivers. This results in a CEP-robust scheme which is also stable under macroscopic propagation of the high harmonics in a gas target. Our results open the way to a new generation of integrated all-fiber IAP sources, overcoming the efficiency limitations of usual gating techniques for multi-cycle drivers., Comment: 18 pages, 5 figures

Structured ultrafast laser beams offer unique opportunities to explore the interplay of the angular momentum of light with matter at the femtosecond scale. Linearly polarized vector beams are paradigmatic examples of structured beams whose topology is characterized by a well-defined Poincar\'e index. It has been demonstrated that the Poincar\'e index is a topological invariant during high-order harmonic generation from isotropic targets, such as noble gases. As a result, harmonics are produced as extreme-ultraviolet vector beams, with the same topology as the driver. We demonstrate that this simple conservation rule does not apply to crystalline solids, characterized by their anisotropic non-linear response to the driving excitation. In this context, we identify the topological properties of the harmonic field as unique probes, sensitive to both the microscopic and macroscopic features of the target's complex non-linear response. Our simulations, performed in single-layer graphene but extendable to other solid targets, show that the harmonic field is split into a multi-beam structure whose topology -- different from that of the driver -- encodes information about laser-driven electronic dynamics. Our work opens the route towards using the topological analysis of the high-order harmonic field as a novel spectroscopic tool to reveal the coupling of light and target symmetries in the non-linear response of matter.

We report a first detection, at very high significance ($25\sigma$), of the cross-correlation between cosmic shear and the diffuse X-ray background, using data from the Dark Energy Survey and the ROSAT satellite. The X-ray cross-correlation signal is sensitive to the distribution of the surrounding gas in dark matter haloes. This allows us to use our measurements to place constraints on key physical parameters that determine the impact of baryonic effects in the matter power spectrum. In particular, we determine the mass of haloes in which feedback has expelled half of their gas content on average to be $\log_{10}(M_c/M_\odot)=13.643^{+0.081}_{-0.12}$, and the polytropic index of the gas to be $\Gamma = 1.231^{+0.015}_{-0.011}$. This represents a first step in the direct use of X-ray cross-correlations to obtain improved constraints on cosmology and the physics of the intergalactic gas., Comment: 6 pages, 3 figures, 1 table

Coherent control over electron dynamics in atoms and molecules using high-intensity circularly-polarized laser pulses gives rise to current loops, resulting in the emission of magnetic fields. We propose and demonstrate with ab-initio calculations ``current-gating" schemes to generate direct or alternating-current magnetic pulses in the infrared spectral region, with highly tunable waveform and frequency, and showing femtosecond-to-attosecond pulse duration. In optimal conditions, the magnetic pulse can be highly isolated from the driving laser and exhibits a high flux density ($\sim1$ Tesla at few hundred nanometers from the source, with a pulse duration of 787 attoseconds) for application in forefront experiments of ultrafast spectroscopy. Our work paves the way toward the generation of attosecond magnetic fields to probe ultrafast magnetization, chiral responses, and spin dynamics., Comment: Main text (7 pages including references, 3 figures), Supplementary material (8 pages, 3 figures)

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the LSST bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Y10 data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian PT (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of $k_{\mathrm{max}}=0.4 \; \mathrm{Mpc}^{-1}$ for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. [abridged], Comment: 47 pages, 19 figures, 1 table, to be submitted to JCAP

We study the angular clustering of Quaia, a Gaia- and unWISE-based catalog of over a million quasars with an exceptionally well-defined selection function. With it, we derive cosmology constraints from the amplitude and growth of structure across cosmic time. We divide the sample into two redshift bins, centered at $z=1.0$ and $z=2.1$, and measure both overdensity auto-correlations and cross-correlations with maps of the Cosmic Microwave Background convergence measured by Planck. From these data, and including a prior from measurements of the baryon acoustic oscillations scale, we place constraints on the amplitude of the matter power spectrum $\sigma_8=0.766\pm 0.034$, and on the matter density parameter $\Omega_m=0.343^{+0.017}_{-0.019}$. These measurements are in reasonable agreement with \planck at the $\sim$ 1.4$\sigma$ level, and are found to be robust with respect to observational and theoretical uncertainties. We find that our slightly lower value of $\sigma_8$ is driven by the higher-redshift sample, which favours a low amplitude of matter fluctuations. We present plausible arguments showing that this could be driven by contamination of the CMB lensing map by high-redshift extragalactic foregrounds, which should also affect other cross-correlations with tracers of large-scale structure beyond $z\sim1.5$. Our constraints are competitive with those from state-of-the-art 3$\times$2-point analyses, but arise from a range of scales and redshifts that is highly complementary to those covered by cosmic shear data and most galaxy clustering samples. This, coupled with the unprecedented combination of volume and redshift precision achieved by Quaia allows us to break the usual degeneracy between $\Omega_m$ and $\sigma_8$., Comment: Submitted to JCAP, corrects links to public data release. The per-bin selection functions used in this analysis, and the complete set of power spectra, covariance, and associated metadata used in our fiducial analysis are publicly available at https://doi.org/10.5281/zenodo.8098635 . The public Quaia catalog can be found at https://doi.org/10.5281/zenodo.8060755

Optical fibres aim to image in-vivo biological processes. In this context, high spatial resolution and stability to fibre movements are key to enable decision-making processes (e.g., for microendoscopy). Recently, a single-pixel imaging technique based on a multicore fibre photonic lantern has been designed, named computational optical imaging using a lantern (COIL). A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy. In this work, we develop a data-driven approach for COIL. We replace the sparsity prior in the proximal algorithm by a learned denoiser, leading to a plug-and-play (PnP) algorithm. The resulting PnP method, based on a proximal primal-dual algorithm, enables to solve the Morozov formulation of the inverse problem. We use recent results in learning theory to train a network with desirable Lipschitz properties, and we show that the resulting primal-dual PnP algorithm converges to a solution to a monotone inclusion problem. Our simulations highlight that the proposed data-driven approach improves the reconstruction quality over variational SARA-COIL method on both simulated and real data.

Battery models generally fall into two categories: physics-based models and ECM models. Physics-based Doyle-Fuller-Newman (DFN) models can accurately simulate the battery internal electrochemical processes, but to properly account for thermal effects requires a strong coupling between a DFN model and a 3D thermal model, which is computationally unaffordable. Distributed Equivalent Circuit Network (ECN) models can perform simulations with high speed and reasonable accuracy. However, these models rely heavily on the characterisation experiments for ECN parameter identification, which is resource-intensive and can lead to inaccurate parametrisation outcomes due to internal thermal inhomogeneity. To harness the strengths of both models, we propose a computational framework to integrate electrochemical DFN model and 3D distributed ECN model together. Using this framework, we simulate constant current discharge experiments of Kokam 7.5 Ah pouch cell (Model SLPB75106100) and compare the simulations with the commonly-used lumped DFN-thermal model. The computational model outperforms the lumped DFN model at low-temperature and/or high C-rate scenarios significantly. The largest predicting error of the framework at 3 C-rate &Tam = 25oC and at 1 C-rate &Tam = 0 oC is approximately 1/3 of that for DFN model. At 3 C-rate &Tam = 5oC, the difference between these two can rise to 377 mV. By integrating DFN and 3D-distributed ECN together, the computational framework can simulate the complicated interplay between electrochemistry, thermal process, and electricity within a cell fast and accurately. We anticipate this computational framework to be a valuable toolset to assist researchers and engineers in the design and control of Li-ion batteries.

Continuous variable quantum key distribution with discrete modulation has the potential to provide information-theoretic security using widely available optical elements and existing telecom infrastructure. While their implementation is significantly simpler than that for protocols based on Gaussian modulation, proving their finite-size security against coherent attacks poses a challenge. In this work we prove finite-size security against coherent attacks for a discrete-modulated quantum key distribution protocol involving four coherent states and heterodyne detection. To do so, and contrary to most of the existing schemes, we first discretize all the continuous variables generated during the protocol. This allows us to use the entropy accumulation theorem, a tool that has previously been used in the setting of discrete variables, to construct the finite-size security proof. We then compute the corresponding finite-key rates through semi-definite programming and under a photon-number cutoff. Our analysis provides asymptotic rates in the range of $0.1-10^{-4}$ bits per round for distances up to hundred kilometres, while in the finite case and for realistic parameters, we get of the order of $10$ Gbits of secret key after $n\sim10^{11}$ rounds and distances of few tens of kilometres., Comment: Accepted version

The competition between shape and perpendicular magnetic anisotropies (PMA) in magnetic thin films gives rise to unusual magnetic behaviors. In ferromagnetic films with moderate PMA the magnetic domain configuration transitions from planar to stripe-like domains above a critical thickness, $t_c$. In this article, we present a detailed study of the magnetization switching mechanism in FePt thin films, where this phenomenon is observed. Using micromagnetic simulations and experiments, we found that below $t_c$ the reversal mechanism is well described by the two-phase model while above this thickness the magnetization within each stripe reverses by coherent rotation. We also analyzed the PMA and its temperature dependence, probing that substrate-induced strains are responsible for the abnormal coercive field behavior observed for FePt films with $t>t_c$., Comment: 7 pages, 13 figures

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems., Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition)

As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties and then marginalizing over the additional model parameters. The resulting high dimensionality of the total parameter spaces makes inferring the cosmological parameters significantly more costly using traditional Monte-Carlo sampling methods. A particularly relevant example is the redshift distribution, $p(z)$, of the source samples, which may require tens of parameters to describe fully. However, relatively tight priors can be usually placed on these parameters through calibration of the associated systematics. In this paper we show, quantitatively, that a linearisation of the theoretical prediction with respect to these calibratable systematic parameters allows us to analytically marginalise over these extra parameters, leading to a factor $\sim30$ reduction in the time needed for parameter inference, while accurately recovering the same posterior distributions for the cosmological parameters that would be obtained through a full numerical marginalisation over 160 $p(z)$ parameters. We demonstrate that this is feasible not only with current data and current achievable calibration priors but also for future Stage-IV datasets., Comment: 11 pages, 8 figures, prepared for submission to MNRAS, comments welcome

The analysis of photometric large-scale structure data is often complicated by the need to account for many observational and astrophysical systematics. The elaborate models needed to describe them often introduce many ``nuisance parameters'', which can be a major inhibitor of an efficient parameter inference. In this paper we introduce an approximate method to analytically marginalise over a large number of nuisance parameters based on the Laplace approximation. We discuss the mathematics of the method, its relation to concepts such as volume effects and profile likelihood, and show that it can be further simplified for calibratable systematics by linearising the dependence of the theory on the associated parameters. We quantify the accuracy of this approach by comparing it with traditional sampling methods in the context of existing data from the Dark Energy Survey, as well as futuristic Stage-IV photometric data. The linearised version of the method is able to obtain parameter constraints that are virtually equivalent to those found by exploring the full parameter space for a large number of calibratable nuisance parameters, while reducing the computation time by a factor 3-10. Furthermore, the non-linearised approach is able to analytically marginalise over a large number of parameters, returning constraints that are virtually indistinguishable from the brute-force method in most cases, accurately reproducing both the marginalised uncertainty on cosmological parameters, and the impact of volume effects associated with this marginalisation. We provide simple recipes to diagnose when the approximations made by the method fail and one should thus resort to traditional methods. The gains in sampling efficiency associated with this method enable the joint analysis of multiple surveys, typically hindered by the large number of nuisance parameters needed to describe them., Comment: 17 pages, 6 figures, 3 tables