arXiv subjects

David Martin

Publications and source records attributed to David Martin.

At least 19 recordsLinked to original sources

arXiv2026

Type I X-Ray Burst Models With Rotation

Type I X-ray bursts are powered by unstable thermonuclear burning on the surface of accreting neutron stars in close binary systems. These brief X-ray flashes, with light curves featuring rise times of $1-10$ s, durations of $10-100$ s, and recurrence periods of hours to days, represent the most frequent stellar explosions in our Galaxy. With typical energies of $\sim 10^{39}-10^{40}$ erg, they rank among the most powerful astrophysical transients after supernovae and classical novae. To date, roughly 120 bursting X-ray binaries have been identified in the Milky Way. Several studies have been conducted to characterize the dynamics of these events, with emphasis on reproducing the observed recurrence periods and light curve shapes. In this paper we show, for the first time, that rotation is a key factor shaping the properties of Type I X-ray bursts in rapidly spinning systems. The inclusion of centrifugal forces, together with a suite of rotationally-induced mixing mechanisms, such as meridional circulation and shear-induced turbulent diffusion, reduce surface gravity, shortening the recurrence times and lowering burst energies. Rotation also modifies the extent of the nuclear activity during these events and affects the morphology of their light curves, which are distinctly broader for rapidly rotating neutron stars.

astro-ph.HE↗

arXiv2026

Resilient-to-Fragile Transition and Excess Volatility in Supply Chain Networks

We study a production network in which firms use non-substitutable (Leontief) inputs, hold precautionary inventories and face idiosyncratic productivity shocks, with adjustment occurring through quantities rather than prices. We show analytically and numerically that a critical boundary exists in the space of shock volatility and inventory holdings: above this threshold, the economy absorbs shocks and fluctuates mildly while below it, cascading shortages make system-wide crises inevitable. Close to the threshold, aggregate output volatility diverges through network-mediated amplification of purely idiosyncratic shocks, providing a concrete mechanism for the ``small shocks, large business cycles'' puzzle. Because inventories are costly, competitive pressure drives firms toward the fragility boundary: a resilience-efficiency trade-off emerges, putting the gains from lean supply chains at risk. Finally, we show that supplier diversification shifts the threshold and, depending on its abundance, can eliminate the fragile regime entirely.

physics.soc-ph↗

arXiv2025

When is nonreciprocity relevant?

Nonreciprocal interactions are widely observed in nonequilibrium systems, from biological or sociological dynamics to open quantum systems. Despite the ubiquity of nonreciprocity, its impact on phase transitions is not fully understood. In this work, we derive criteria to perturbatively assess whether nonreciprocity changes the universality class of two-species systems undergoing a phase transition. These criteria, stated in terms of the unperturbed critical exponents in the spirit of the Harris criterion for disordered systems, assess whether static critical exponents change at first order under a given perturbation. For example, in the case of a nonreciprocal version of model A with two species, a homogeneous nonreciprocal perturbation is relevant whenever the two parts are initially identical and uncoupled, and irrelevant otherwise. Our results agree with existing renormalization group calculations and with numerical simulations.

cond-mat.stat-mech↗

arXiv2025

The Multiple Time-Stepping Method for 3-Body Interactions in High Performance Molecular Dynamics Simulations

Understanding the complex behavior of molecular systems is fundamental to fields such as physics, materials science, and biology. Molecular dynamics (MD) simulations are crucial tools for studying atomic-level dynamics. This work focuses on improving the efficiency of MD simulations involving two-body and three-body interactions. Traditional two-body potentials often can not fully capture the complexity of molecular systems, making the inclusion of three-body interactions important. However, these interactions are in a cubic complexity class, compared to a quadratic one for two-body interactions, and therefore are computationally expensive, even when a cutoff distance is applied. One way to improve efficiency is to use the r-RESPA multiple time-stepping algorithm to reduce the number of three-body interaction calculations. In this work, we investigate this method in the context of High Performance Computing (HPC) methods that parallelize the calculations. In particular, we investigate a communication-reducing distributed-memory parallel method from literature and present a novel shared-memory parallel cutoff method, implemented in the particle simulation library AutoPas. The results and methods are discussed, providing insights into potential advancements in MD simulation efficiency.

cs.CE↗

arXiv2025

Quorum sensing of light-activated colloids in nematic liquid crystals

Motile living organisms routinely probe their surroundings to adapt in ever-evolving environments. Although synthetic microswimmers offer surrogates for self-propelled living entities, they often lack the complex feedback mechanisms that enable organisms to adapt. In this work, we present an experimental platform in which light-activated colloids dispersed in a nematic liquid crystal can (i) switch from directed to active Brownian motion depending on the nematic anchoring and (ii) mechanically adjust their motility in response to crowding, effectively enforcing quorum-sensing interactions. Both features are caused by a distinctive self-propulsion mechanism as unveiled through experiments, simulations, and theory. We characterize the dynamics of a single colloid and demonstrate that its motion is captured by an active Brownian particle model if the nematic anchoring is homeotropic, and by directed self-propulsion along the nematic director if the anchoring is planar. Next, we investigate the many-body dynamics, showing that it undergoes a clustering phase separation through effective quorum-sensing interactions. Our work suggests how to create adaptive materials with life-like capabilities using readily accessible properties of liquid crystals and colloids without explicitly engineering any of the needed mechano-chemical feedbacks.

cond-mat.soft↗

arXiv2024

Dynamical phase transitions in the nonreciprocal Ising model

Nonreciprocal interactions in many-body systems lead to time-dependent states, commonly observed in biological, chemical, and ecological systems. The stability of these states in the thermodynamic limit and the critical behavior of the phase transition from static to time-dependent states are not yet fully understood. To address these questions, we study a minimalistic system endowed with nonreciprocal interactions: an Ising model with two spin species having opposing goals. The mean-field equation predicts three stable phases: disorder, static order, and a time-dependent swap phase. Large scale numerical simulations support the following: (i) in 2D, the swap phase is destabilized by defects; (ii) in 3D, the swap phase is stable, and has the properties of a time crystal; (iii) the transition from disorder to swap in 3D is characterized by the critical exponents of the 3D XY model, and corresponds to the breaking of a continuous symmetry, time translation invariance; (iv) when the two species have fully anti-symmetric couplings, the static-order phase is unstable in any finite dimension due to droplet growth; (v) in the general case of asymmetric couplings, static order can be restored by a droplet-capture mechanism preventing the droplets from growing indefinitely. We provide details on the full phase diagram which includes first- and second-order-like phase transitions and study how the system coarsens into swap and static-order states.

cond-mat.stat-mech↗

arXiv2024

Discovery of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named \texttt{SMDET}, applied to time series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate's spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T subdwarf candidate, two extreme T subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for 2 objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine learning tools as a new large-scale discovery technique.

astro-ph.SR↗

arXiv2024

Fluctuation-Induced First Order Transition to Collective Motion

The nature of the transition to collective motion in assemblies of aligning self-propelled particles remains a long-standing matter of debate. In this article, we focus on dry active matter and show that weak fluctuations suffice to generically turn second-order mean-field transitions into a `discontinuous' coexistence scenario. Our theory shows how fluctuations induce a density-dependence of the polar-field mass, even when this effect is absent at mean-field level. In turn, this dependency on density triggers a feedback loop between ordering and advection that ultimately leads to an inhomogeneous transition to collective motion and the emergence of inhomogeneous travelling bands. Importantly, we show that such a fluctuation-induced first order transition is present in both metric models, in which particles align with neighbors within a finite distance, and in `topological' ones, in which alignment is based on more complex constructions of neighbor sets. We compute analytically the noise-induced renormalization of the polar-field mass using stochastic calculus, which we further back up by a one-loop field-theoretical analysis. Finally, we confirm our analytical predictions by numerical simulations of fluctuating hydrodynamics as well as of topological particle models with either k-nearest neighbors or Voronoi alignment.

cond-mat.soft↗

arXiv2023

Sociohydrodynamics: data-driven modelling of social behavior

Living systems display complex behaviors driven by physical forces as well as decision-making. Hydrodynamic theories hold promise for simplified universal descriptions of socially-generated collective behaviors. However, the construction of such theories is often divorced from the data they should describe. Here, we develop and apply a data-driven pipeline that links micromotives to macrobehavior by augmenting hydrodynamics with individual preferences that guide motion. We illustrate this pipeline on a case study of residential dynamics in the United States, for which census and sociological data is available. Guided by Census data, sociological surveys, and neural network analysis, we systematically assess standard hydrodynamic assumptions to construct a sociohydrodynamic model. Solving our simple hydrodynamic model, calibrated using statistical inference, qualitatively captures key features of residential dynamics at the level of individual US counties. We highlight that a social memory, akin to hysteresis in magnets, emerges in the segregation-integration transition even with memory-less agents. This suggests an explanation for the phenomenon of neighborhood tipping, whereby a small change in a neighborhood's population leads to a rapid demographic shift. Beyond residential segregation, our work paves the way for systematic investigations of decision-guided motility in real space, from micro-organisms to humans, as well as fitness-mediated motion in more abstract genomic spaces.

cond-mat.soft↗

arXiv2023

Nonreciprocal Ising model

Systems with nonreciprocal interactions generically display time-dependent states. These are routinely observed in finite systems, from neuroscience to active matter, in which globally ordered oscillations exist. However, the stability of these uniform nonreciprocal phases in noisy spatially-extended systems, their fate in the thermodynamic limit, and the critical behavior of the corresponding phase transitions are not fully understood. Here, we address these questions by introducing a nonreciprocal generalization of the Ising model and study its phase transitions by means of numerical and analytical approaches. While the mean-field equations predict three stable homogeneous phases (disordered, ordered and a time-dependent swap phase), our large scale numerical simulations reveal a more complex picture. Static order is destroyed in any finite dimension due to the growth of rare droplets unless the symmetry between the two spin types is broken triggering a stabilizing droplet-capture mechanism. The swap phase is destroyed by fluctuations in two dimensions through the proliferation of spiral defects but stabilized in three dimensions where nonreciprocity changes the critical exponents from Ising to XY, thus giving rise to a robust spatially-distributed clock.

cond-mat.stat-mech↗

arXiv2023

The transition to collective motion in nonreciprocal active matter: coarse graining agent-based models into fluctuating hydrodynamics

Two hallmarks of non-equilibrium systems, from active colloids to animal herds, are agents motility and nonreciprocal interactions. Their interplay creates feedback loops leading to complex spatiotemporal dynamics crucial to understand and control the nonlinear response of active systems. Here, we introduce a minimal model that captures these two features at the microscopic scale, while admitting an exact hydrodynamic theory valid also in the fully-nonlinear regime. Our goal is to account for the fact that animal herds and colloidal swarms are rarely in the thermodynamic limit where particle number fluctuations can be completely ignored. Using statistical mechanics techniques we exactly coarse-grain a nonreciprocal microscopic model into a fluctuating hydrodynamics and use dynamical systems insights to analyze the resulting equations. In the absence of motility, we find two transitions to oscillatory phases occurring via distinct mechanisms: a Hopf bifurcation and a Saddle-Node on Invariant Circle (SNIC) bifurcation. In the presence of motility, this rigorous approach, complemented by numerical simulations, allows us to quantitatively assess the hitherto neglected impact of inter-species nonreciprocity on a paradigmatic transition in active matter: the emergence of collective motion. When nonreciprocity is weak, we show that flocking is accelerated and bands tend to synchronize with a spatial overlap controlled by nonlinearities. When nonreciprocity is strong, flocking is superseded by a Chase & Rest dynamical phase where each species alternates between a chasing state, when they propagate, and a resting state, when they stand still. Finally, we demonstrate how fluctuations in finite systems can be harnessed to characterize microscopic non-reciprocity from macroscopic time-correlation functions, even in phases where nonreciprocal interactions do not affect the thermodynamic steady-state.

cond-mat.stat-mech↗

arXiv2023

A Causal Roadmap for Generating High-Quality Real-World Evidence

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs; it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases.

stat.ME↗

arXiv2021

TIC 172900988: A Transiting Circumbinary Planet Detected in One Sector of TESS Data

We report the first discovery of a transiting circumbinary planet detected from a single sector of TESS data. During Sector 21, the planet TIC 172900988b transited the primary star and then 5 days later it transited the secondary star. The binary is itself eclipsing, with a period of P = 19.7 days and an eccentricity of e = 0.45. Archival data from ASAS-SN, Evryscope, KELT, and SuperWASP reveal a prominent apsidal motion of the binary orbit, caused by the dynamical interactions between the binary and the planet. A comprehensive photodynamical analysis of the TESS, archival and follow-up data yields stellar masses and radii of M1 = 1.2384 +/- 0.0007 MSun and R1 = 1.3827 +/- 0.0016 RSun for the primary and M2 = 1.2019 +/- 0.0007 MSun and R2 = 1.3124 +/- 0.0012 RSun for the secondary. The radius of the planet is R3 = 11.25 +/- 0.44 REarth (1.004 +/- 0.039 RJup). The planet's mass and orbital properties are not uniquely determined - there are six solutions with nearly equal likelihood. Specifically, we find that the planet's mass is in the range of 824 < M3 < 981 MEarth (2.65 < M3 < 3.09 MJup), its orbital period could be 188.8, 190.4, 194.0, 199.0, 200.4, or 204.1 days, and the eccentricity is between 0.02 and 0.09. At a V = 10.141 mag, the system is accessible for high-resolution spectroscopic observations, e.g. Rossiter-McLaughlin effect and transit spectroscopy.

astro-ph.EP↗

arXiv2020

Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.

cs.LG↗

arXiv2020

Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.

cs.LG↗

arXiv2020

AOUP in the presence of Brownian noise: a perturbative approach

By working in the small persistence time limit, we determine the steady-state distribution of an Active Ornstein Uhlenbeck Particle (AOUP) experiencing, in addition to self-propulsion, a Gaussian white noise modelling a bath at temperature T. This allows us to derive analytical formulas for three quantities: the spatial density of a confined particle, the current induced by an asymmetric periodic potential and the entropy production rate. These formulas disentangle the respective roles of the passive and active noises on the steady state of AOUPs, showing that signatures of non-equilibrium can display surprising behaviors as the temperature is varied. Indeed, depending on the potential in which the particle evolves, both the current and the entropy production rate can be non-monotonic functions of T. The latter can even diverge at high temperature for steep enough confining potentials. Thus, depending on context, switching on translational diffusion may drive the particle closer to or further away from equilibrium. We then probe the range of validity of our quantitative derivations by numerical simulations. Finally, we explain how the method presented here to tackle perturbatively an Ornstein Uhlenbeck (OU) noise could be further generalized beyond the Brownian case.

cond-mat.stat-mech↗

arXiv2020

Statistical Mechanics of Active Ornstein Uhlenbeck Particles

We review and extend recent developments on the statistical properties of Active Ornstein Uhlenbeck particles (AOUPs). In this simplest of models, the Gaussian white noise of overdamped Brownian colloids is replaced by a Gaussian colored noise. This suffice to grant this system the hallmark properties of active matter, while still allowing for analytical progress. We first detail the perturbative derivation of the steady state of AOUPs in the small persistence time limit. We show the existence of an effective equilibrium regime in which detailed-balance is obeyed with respect to a non-Boltzmann distribution and detail the corresponding fluctuation-dissipation theorem. We then characterize the departure from equilibrium by computing several relevant observables (entropy production, ratchet current). At the collective level, we show AOUPs to experience motility-induced phase separation both in the presence of pairwise forces or due to quorum-sensing interactions. The latter can be accounted for by considering the steady-state of AOUPs with spatially varying propulsion speed or persistence time. Finally, we discuss how the emerging properties of AOUPs can be characterized from the dynamics of their collective modes, which we construct explicitly.

cond-mat.stat-mech↗

arXiv2020

Wikidata on MARS

Multi-attributed relational structures (MARSs) have been proposed as a formal data model for generalized property graphs, along with multi-attributed rule-based predicate logic (MARPL) as a useful rule-based logic in which to write inference rules over property graphs. Wikidata can be modelled in an extended MARS that adds the (imprecise) datatypes of Wikidata. The rules of inference for the Wikidata ontology can be modelled as a MARPL ontology, with extensions to handle the Wikidata datatypes and functions over these datatypes. Because many Wikidata qualifiers should participate in most inference rules in Wikidata a method of implicitly handling qualifier values on a per-qualifier basis is needed to make this modelling useful. The meaning of Wikidata is then the extended MARS that is the closure of running these rules on the Wikidata data model. Wikidata constraints can be modelled as multi-attributed predicate logic (MAPL) formulae, again extended with datatypes, that are evaluated over this extended MARS. The result models Wikidata in a way that fixes several of its major problems.

cs.AI↗