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Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008., 15 pages, 6 figures, preprint submitted to Physica A

Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures enables us to compare the standard model with alternative cosmologies, constrain the dark energy equation of state, and distinguish between different gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing observables to recover the large-scale structure, paving the way for a more versatile and effective search for troughs., 12 pages, 5 figures, accepted for publication in MNRAS

While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hydrodynamic simulations that are computationally costly to run. We explore the merging of an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework. In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties. While there exists a trade-off between the reached accuracy and the speed advantage this approach offers, our results outperform an approach using only machine learning for a subset of baryonic properties. We demonstrate that this novel hybrid system enables the fast completion of dark matter-only information by mimicking the properties of a full hydrodynamic suite to a reasonable degree, and discuss the advantages and disadvantages of hybrid versus machine learning-only frameworks. In doing so, we offer an acceleration of commonly deployed simulations in cosmology., Comment: 15 pages, 8 figures, accepted for publication in MNRAS

The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50%. Current deblending approaches are in most cases either too slow or not accurate enough to reach the level of requirements. This work explores the use of deep neural networks to estimate the photometry of blended pairs of galaxies in monochrome space images, similar to the ones that will be delivered by the Euclid space telescope. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim$7% accuracy without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that forcing the network to simultaneously estimate a binary segmentation map results in a slightly improved photometry. All data products and codes will be made public to ease the comparison with other approaches on a common data set., Comment: 16 pages, 12 figures, submitted to MNRAS, comments welcome

We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources. Gaussbock uses Bayesian nonparametrics and truncated importance sampling to accurately draw samples from posterior distributions with an orders-of-magnitude speed-up in wall time over alternative methods. Contemporary problems in this area often suffer from both increased computational costs due to high-dimensional parameter spaces and consequent excessive time requirements, as well as the need for fine tuning of proposal distributions or sampling parameters. Gaussbock is designed specifically with these issues in mind. We explore and validate the performance and convergence of the algorithm on a fast approximation to the Dark Energy Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the number of parameters. We then test on the full DES Y1 posterior using large-scale supercomputing facilities, and recover reasonable agreement with previous chains, although the algorithm can underestimate the tails of poorly-constrained parameters. Additionally, we discuss and demonstrate how Gaussbock recovers complex posterior shapes very well at lower dimensions, but faces challenges to perform well on such distributions in higher dimensions. In addition, we provide the community with a user-friendly software tool for accelerated cosmological parameter estimation based on the methodology described in this paper., 19 pages, 10 figures, accepted for publication in ApJ

Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disk. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter claim to be challenged. We perform a systematic search for new OCs in the direction of Perseus using precise and accurate astrometry from Gaia DR2. We implement a coarse-to-fine search method. First, we exploit spatial proximity using a fast density-aware partitioning of the sky via a k-d tree in the spatial domain of Galactic coordinates, (l, b). Secondly, we employ a Gaussian mixture model in the proper motion space to quickly tag fields around OC candidates. Thirdly, we apply an unsupervised membership assignment method, UPMASK, to scrutinise the candidates. We visually inspect colour-magnitude diagrams to validate the detected objects. Finally, we perform a diagnostic to quantify the significance of each identified overdensity in proper motion and in parallax space We report the discovery of 41 new stellar clusters. This represents an increment of at least 20% of the previously known OC population in this volume of the Milky Way. We also report on the clear identification of NGC 886, an object previously considered an asterism. This letter challenges the previous claim of a near-complete sample of open clusters up to 1.8 kpc. Our results reveal that this claim requires revision, and a complete census of nearby open clusters is yet to be found., Comment: accepted for publication in A&A

This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard Lambda-CDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the Lambda-CDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight., Comment: 14 pages, 9 figures

Objectives: We introduce a new method for reducing crime in hot spots and across cities through ridge estimation. In doing so, our goal is to explore the application of density ridges to hot spots and patrol optimization, and to contribute to the policing literature in police patrolling and crime reduction strategies. Methods: We make use of the subspace-constrained mean shift algorithm, a recently introduced approach for ridge estimation further developed in cosmology, which we modify and extend for geospatial datasets and hot spot analysis. Our experiments extract density ridges of Part I crime incidents from the City of Chicago during the year 2018 and early 2019 to demonstrate the application to current data. Results: Our results demonstrate nonlinear mode-following ridges in agreement with broader kernel density estimates. Using early 2019 incidents with predictive ridges extracted from 2018 data, we create multi-run confidence intervals and show that our patrol templates cover around 94% of incidents for 0.1-mile envelopes around ridges, quickly rising to near-complete coverage. We also develop and provide researchers, as well as practitioners, with a user-friendly and open-source software for fast geospatial density ridge estimation. Conclusions: We show that ridges following crime report densities can be used to enhance patrolling capabilities. Our empirical tests show the stability of ridges based on past data, offering an accessible way of identifying routes within hot spots instead of patrolling epicenters. We suggest further research into the application and efficacy of density ridges for patrolling., 16 pages, 4 figures, accepted for publication in Decision Support Systems

Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions of directional trend changes via complex lagged correlations between them, excluding any information about the target series from the respective inputs to achieve predictions purely based on such correlations with other series. We propose the use of deep neural networks that employ step-wise linear regressions with exponential smoothing in the preparatory feature engineering for this task, with regression slopes as trend strength indicators for a given time interval. We apply this method to historical stock market data from 2011 to 2016 as a use case example of lagged correlations between large numbers of time series that are heavily influenced by externally arising new information as a random factor. The results demonstrate the viability of the proposed approach, with state-of-the-art accuracies and accounting for the statistical significance of the results for additional validation, as well as important implications for modern financial economics., Comment: 11 pages, 4 figures

Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically identify, classify, segment and deblend various astronomical objects. In this paper, we explore the use of chained generative adversarial networks (GANs), a class of generative models that learn mappings from latent spaces to data distributions by modelling the joint distribution of the data, to produce physically realistic galaxy images as one use case of such models. In cosmology, such datasets can aid in the calibration of shape measurements for weak lensing by augmenting data with synthetic images. By measuring the distributions of multiple physical properties, we show that images generated with our approach closely follow the distributions of real galaxies, further establishing state-of-the-art GAN architectures as a valuable tool for modern-day astronomy., Comment: 13 pages, 9 figures

We introduce an emulator approach to predict the non-linear matter power spectrum for broad classes of beyond-$\Lambda$CDM cosmologies, using only a suite of $\Lambda$CDM $N$-body simulations. By including a range of suitably modified initial conditions in the simulations, and rescaling the resulting emulator predictions with analytical `halo model reactions', accurate non-linear matter power spectra for general extensions to the standard $\Lambda$CDM model can be calculated. We optimise the emulator design by substituting the simulation suite with non-linear predictions from the standard {\sc halofit} tool. We review the performance of the emulator for artificially generated departures from the standard cosmology as well as for theoretically motivated models, such as $f (R)$ gravity and massive neutrinos. For the majority of cosmologies we have tested, the emulator can reproduce the matter power spectrum with errors $\lesssim 1\%$ deep into the highly non-linear regime. This work demonstrates that with a well-designed suite of $\Lambda$CDM simulations, extensions to the standard cosmological model can be tested in the non-linear regime without any reliance on expensive beyond-$\Lambda$CDM simulations., Comment: 16 pages, 13 figures, accepted for publication in MNRAS