@inproceedings{20299, abstract = {Deterministic Markov Decision Processes (DMDPs) are a mathematical framework for decision-making where the outcomes and future possible actions are deterministically determined by the current action taken. DMDPs can be viewed as a finite directed weighted graph, where in each step, the controller chooses an outgoing edge. An objective is a measurable function on runs (or infinite trajectories) of the DMDP, and the value for an objective is the maximal cumulative reward (or weight) that the controller can guarantee. We consider the classical mean-payoff (aka limit-average) objective, which is a basic and fundamental objective. Howard's policy iteration algorithm is a popular method for solving DMDPs with mean-payoff objectives. Although Howard's algorithm performs well in practice, as experimental studies suggested, the best known upper bound is exponential and the current known lower bound is as follows: For the input size I, the algorithm requires (math formular) iterations, where (math formular) hides the poly-logarithmic factors, i.e., the current lower bound on iterations is sub-linear with respect to the input size. Our main result is an improved lower bound for this fundamental algorithm where we show that for the input size I, the algorithm requires (math formular) iterations.}, author = {Asadi, Ali and Chatterjee, Krishnendu and De Raaij, Jakob}, booktitle = {The 41st Conference on Uncertainty in Artificial Intelligence}, issn = {2640-3498}, location = {Rio de Janeiro, Brazil}, pages = {223--232}, publisher = {ML Research Press}, title = {{Lower bound on Howard policy iteration for deterministic Markov Decision Processes}}, volume = {286}, year = {2025}, } @inproceedings{20297, abstract = {A standard model that arises in several applications in sequential decision-making is partially observable Markov decision processes (POMDPs) where a decision-making agent interacts with an uncertain environment. A basic objective in POMDPs is the reachability objective, where given a target set of states, the goal is to eventually arrive at one of them. The limit-sure problem asks whether reachability can be ensured with probability arbitrarily close to 1. In general, the limit-sure reachability problem for POMDPs is undecidable. However, in many practical cases, the most relevant question is the existence of policies with a small amount of memory. In this work, we study the limit-sure reachability problem for POMDPs with a fixed amount of memory. We establish that the computational complexity of the problem is NP-complete.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Saona Urmeneta, Raimundo J and Shafiee, Ali}, booktitle = {The 41st Conference on Uncertainty in Artificial Intelligence}, issn = {2640-3498}, location = {Rio de Janeiro, Brazil}, pages = {238--247}, publisher = {ML Research Press}, title = {{Limit-sure reachability for small memory policies in POMDPs is NP-complete}}, volume = {286}, year = {2025}, } @inproceedings{20296, abstract = {Learning-based systems are increasingly deployed across various domains, yet the complexity of traditional neural networks poses significant challenges for formal verification. Unlike conventional neural networks, learned Logic Gate Networks (LGNs) replace multiplications with Boolean logic gates, yielding a sparse, netlist-like architecture that is inherently more amenable to symbolic verification, while still delivering promising performance. In this paper, we introduce a SAT encoding for verifying global robustness and fairness in LGNs. We evaluate our method on five benchmark datasets, including a newly constructed 5-class variant, and find that LGNs are both verification-friendly and maintain strong predictive performance.}, author = {Kresse, Fabian and Yu, Zhengqi and Lampert, Christoph and Henzinger, Thomas A}, booktitle = {2nd International Conferenceon Neuro-Symbolic Systems}, issn = {2640-3498}, location = {Philadephia, PA, United States}, publisher = {ML Research Press}, title = {{Logic gate neural networks are good for verification}}, volume = {288}, year = {2025}, } @article{19508, abstract = {We consider random two-player zero-sum dynamic games with perfect information on a class of infinite directed graphs. Starting from a fixed vertex, the players take turns to move a token along the edges of the graph. Every vertex is assigned a payoff known in advance by both players. Every time the token visits a vertex, Player 2 pays Player 1 the corresponding payoff. We consider a distribution over such games by assigning i.i.d. payoffs to the vertices. On the one hand, for acyclic directed graphs of bounded degree and sub-exponential expansion, we show that, when the duration of the game tends to infinity, the value converges almost surely to a constant at an exponential rate dominated in terms of the expansion. On the other hand, for the infinite d-ary tree (that does not fall into the previous class of graphs), we show convergence at a double-exponential rate.}, author = {Attia, Luc and Lichev, Lyuben and Mitsche, Dieter and Saona Urmeneta, Raimundo J and Ziliotto, Bruno}, issn = {2153-0793}, journal = {Dynamic Games and Applications}, pages = {1517--1535}, publisher = {Springer Nature}, title = {{Random zero-sum dynamic games on infinite directed graphs}}, doi = {10.1007/s13235-025-00636-4}, volume = {15}, year = {2025}, } @inproceedings{20302, abstract = {LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated learning. However, rigorously establishing their theoretical advantages over simpler methods, such as minibatch SGD (MbSGD), has proven challenging, as existing analyses often rely on strong assumptions, unrealistic premises, or overly restrictive scenarios. In this work, we revisit the convergence properties of LocalSGD and SCAFFOLD under a variety of existing or weaker conditions, including gradient similarity, Hessian similarity, weak convexity, and Lipschitz continuity of the Hessian. Our analysis shows that (i) LocalSGD achieves faster convergence compared to MbSGD for weakly convex functions without requiring stronger gradient similarity assumptions; (ii) LocalSGD benefits significantly from higher-order similarity and smoothness; and (iii) SCAFFOLD demonstrates faster convergence than MbSGD for a broader class of non-quadratic functions. These theoretical insights provide a clearer understanding of the conditions under which LocalSGD and SCAFFOLD outperform MbSGD.}, author = {Luo, Ruichen and Stich, Sebastian U. and Horváth, Samuel and Takáč, Martin}, booktitle = {The 28th International Conference on Artificial Intelligence and Statistics}, issn = {2640-3498}, location = {Mai Khao, Thailand}, pages = {2539--2547}, publisher = {ML Research Press}, title = {{Revisiting LocalSGD and SCAFFOLD: Improved rates and missing analysis}}, volume = {258}, year = {2025}, } @article{20646, abstract = {Describing general quantum many-body dynamics is a challenging task due to the exponential growth of the Hilbert space with system size. The time-dependent variational principle (TDVP) provides a powerful tool to tackle this task by projecting quantum evolution onto a classical dynamical system within a variational manifold. In classical systems, periodic orbits play a crucial role in understanding the structure of the phase space and the long-term behavior of the system. However, finding periodic orbits is generally difficult, and their existence and properties in generic TDVP dynamics over matrix product states have remained largely unexplored. In this work, we develop an algorithm to systematically identify and characterize periodic orbits in TDVP dynamics. Applying our method to the periodically kicked Ising model, we uncover both stable and unstable periodic orbits. We characterize the Kolmogorov-Arnold-Moser tori in the vicinity of stable periodic orbits and track the change of the periodic orbits as we modify the Hamiltonian parameters. We observe that periodic orbits exist at any value of the coupling constant of the kicked Ising model between prethermal and fully thermalizing regimes, but their relevance to quantum dynamics and imprint on quantum eigenstates diminishes as the system leaves the prethermal regime. Our results demonstrate that periodic orbits provide valuable insights into the TDVP approximation of quantum many-body evolution and establish a closer connection between quantum and classical chaos.}, author = {Petrova, Elena and Ljubotina, Marko and Yalniz, Gökhan and Serbyn, Maksym}, issn = {2691-3399}, journal = {PRX Quantum}, number = {4}, publisher = {American Physical Society}, title = {{Finding periodic orbits in projected quantum many-body dynamics}}, doi = {10.1103/tldp-kvkd}, volume = {6}, year = {2025}, } @article{20709, abstract = {Non-Hermitian many-body localization (NH MBL) has emerged as a possible scenario for stable localization in open systems, as suggested by spectral indicators identifying a putative transition for finite system sizes. In this work, we shift the focus to dynamical probes, specifically the steady-state spin current, to investigate transport properties in a disordered, non-Hermitian XXZ spin chain. Through exact diagonalization for small systems and tensor-network methods for larger chains, we demonstrate that the steady-state current remains finite and decays exponentially with disorder strength, showing no evidence of a transition up to disorder values far beyond the previously claimed critical point. Our results reveal a stark discrepancy between spectral indicators, which suggest localization, and transport behavior, which indicates delocalization. This highlights the importance of dynamical observables in characterizing NH MBL and suggests that traditional spectral measures may not fully capture the physics of non-Hermitian systems. Additionally, we observe a noncommutativity of limits in system size and time, further complicating the interpretation of finite-size studies. These findings challenge the existence of NH MBL in the studied model and underscore the need for alternative approaches to understanding localization in non-Hermitian settings.}, author = {Brighi, Pietro and Ljubotina, Marko and Roccati, Federico and Balducci, Federico}, issn = {2643-1564}, journal = {Physical Review Research}, number = {4}, publisher = {American Physical Society}, title = {{Finite steady-state current defies non-Hermitian many-body localization}}, doi = {10.1103/crwj-x7j8}, volume = {7}, year = {2025}, } @inproceedings{20648, abstract = {Polynomial quantified entailments with existentially and universally quantified variables arise in many problems of verification and program analysis. We present PolyQEnt which is a tool for solving polynomial quantified entailments in which variables on both sides of the implication are real valued or unbounded integers. Our tool provides a unified framework for polynomial quantified entailment problems that arise in several papers in the literature. Our experimental evaluation over a wide range of benchmarks shows the applicability of the tool as well as its benefits as opposed to simply using existing SMT solvers to solve such constraints.}, author = {Chatterjee, Krishnendu and Goharshady, Amir Kafshdar and Kafshdar Goharshadi, Ehsan and Karrabi, Mehrdad and Saadat, Milad and Seeliger, Maximilian and Zikelic, Dorde}, booktitle = {23rd International Symposium on Automated Technology for Verification and Analysis}, isbn = {9783032087065}, issn = {1611-3349}, location = {Bengaluru, India}, pages = {411--424}, publisher = {Springer Nature}, title = {{PolyQEnt: A polynomial quantified entailment solver}}, doi = {10.1007/978-3-032-08707-2_19}, volume = {16145}, year = {2025}, } @inproceedings{20690, abstract = {Cumulative prospect theory (CPT) is the first theory for decision-making under uncertainty that combines full theoretical soundness and empirically realistic features [1], [Page 2]. While CPT was originally considered in one-shot settings for risk-aware decision-making, we consider CPT in sequential decision-making. The most fundamental and well-studied models for sequential decision-making are Markov chains (MCs), and their generalization Markov decision processes (MDPs). The complexity theoretic study of MCs and MDPs with CPT is a fundamental problem that has not been addressed in the literature.Our contributions are as follows: First, we present an alternative viewpoint for the CPT-value of MCs and MDPs. This allows us to establish a connection with multi-objective reachability analysis and conclude the strategy complexity result that memoryless randomized strategies are necessary and sufficient for optimality. Second, based on this connection, we provide an algorithm for computing the CPT-value in MDPs with infinite-horizon objectives. We show that the problem is in EXPTIME and fixed-parameter tractable. Moreover, we provide a polynomial-time algorithm for the special case of MCs.}, author = {Brihaye, Thomas and Chatterjee, Krishnendu and Mohr, Stefanie and Weininger, Maximilian}, booktitle = {2025 40th Annual ACM/IEEE Symposium on Logic in Computer Science}, location = {Singapore, Singapore}, pages = {458--471}, publisher = {IEEE}, title = {{Risk-aware Markov decision processes using cumulative prospect theory}}, doi = {10.1109/lics65433.2025.00041}, year = {2025}, } @inproceedings{20689, abstract = {This paper studies the expected value of multiplicative rewards, where rewards obtained in each step are multiplied (instead of the usual addition), in Markov chains (MCs) and Markov decision processes (MDPs). One of the key differences to additive rewards is that the expected value may diverge to ∞ not only due to recurrent, but also due to transient states.For MCs, computing the value is shown to be possible in polynomial time given an oracle for the comparison of succinctly represented integers (CSRI), which is only known to be solvable in polynomial time subject to number-theoretic conjectures. Interestingly, distinguishing whether the value is ∞ or 0 is at least as hard as CSRI, while determining if it is one of these two can be done in polynomial time. In MDPs, the optimal value can be computed in polynomial space. Further refined complexity results and results on the complexity of optimal schedulers are presented. The techniques developed for MDPs additionally allow to solve the multiplicative variant of the stochastic shortest path problem. Finally, for MCs and MDPs where an absorbing state is reached almost surely, all considered problems are solvable in polynomial time.}, author = {Baier, Christel and Chatterjee, Krishnendu and Meggendorfer, Tobias and Piribauer, Jakob}, booktitle = {2025 40th Annual ACM/IEEE Symposium on Logic in Computer Science}, location = {Singapore, Singapore}, pages = {499--512}, publisher = {IEEE}, title = {{Multiplicative rewards in Markovian models}}, doi = {10.1109/lics65433.2025.00044}, year = {2025}, } @inproceedings{20817, abstract = {We present Mechanistic PDE Networks -- a model for discovery of governing partial differential equations from data. Mechanistic PDE Networks represent spatiotemporal data as space-time dependent linear partial differential equations in neural network hidden representations. The represented PDEs are then solved and decoded for specific tasks. The learned PDE representations naturally express the spatiotemporal dynamics in data in neural network hidden space, enabling increased modeling power. Solving the PDE representations in a compute and memory-efficient way, however, is a significant challenge. We develop a native, GPU-capable, parallel, sparse and differentiable multigrid solver specialized for linear partial differential equations that acts as a module in Mechanistic PDE Networks. Leveraging the PDE solver we propose a discovery architecture that can discovers nonlinear PDEs in complex settings, while being robust to noise. We validate PDE discovery on a number of PDEs including reaction-diffusion and Navier-Stokes equations.}, author = {Pervez, Adeel A and Gavves, Efstratios and Locatello, Francesco}, booktitle = {42nd International Conference on Machine Learning}, issn = {2640-3498}, location = {Vancouver, Canada}, pages = {48962--48973}, publisher = {ML Research Press}, title = {{Mechanistic PDE networks for discovery of governing equations}}, volume = {267}, year = {2025}, } @article{20857, abstract = {Evolutionary games provide a flexible mathematical framework for many problems in biology and social evolution. Prisoners’ dilemma, and in particular, the important special case of donation games, represents social dilemmas where cooperation is mutually beneficial, yet defection is preferred by selfish agents. In evolutionary games on networks, the agents interact over a population structure. The existence of population structures that promote cooperative behavior is a fascinating and active research topic. Previous research establishes structures promoting cooperation in the limit of weak selection where the benefit-to-cost ratio β exceeds 1.5. The existence of such structures for medium and strong selection for 1 < ß < 2 and for weak selection for 1 < ß < 1.5 has been a long-standing open question. First, we answer the open questions in the affirmative: For every selection strength and every ß > 1, we construct networks promoting cooperation. Second, we present a robustness result with respect to β and selection strength: Our structures promote cooperation for a range of these parameter values rather than specific parameter values. Finally, we supplement our theoretical results with simulation results on small population structures that show the effectiveness of our construction over well-studied population structures.}, author = {Svoboda, Jakub and Chatterjee, Krishnendu}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {51}, pages = {e2524109122}, publisher = {National Academy of Sciences}, title = {{Promoters of cooperation in evolutionary games}}, doi = {10.1073/pnas.2524109122}, volume = {122}, year = {2025}, } @inproceedings{21320, abstract = {Prophet inequalities are a central object of study in optimal stopping theory. In the iid model, a gambler sees values in an online fashion, sampled independently from a given distribution. Upon observing each value, the gambler either accepts it as a reward, or irrevocably rejects it and proceeds to observe the next value. The goal of the gambler, who cannot see the future, is to maximise the expected value of the reward while competing against the expectation of a prophet (the offline maximum). In other words, one seeks to maximise the gambler-to-prophet ratio of the expectations. This model has been studied with infinite, finite and unknown number of values. When the gambler faces a random number of values, the model is said to have a random horizon. We consider the model in which the gambler is given a priori knowledge of the horizon’s distribution. Alijani et al. (2020) designed a single-threshold algorithm achieving a ratio of 1/2 when the random horizon has an increasing hazard rate and is independent of the values. We prove that with a single threshold, a ratio of 1/2 is actually achievable for several larger classes of horizon distributions, with the largest being known as the 𝒢 class in reliability theory. Moreover, we show that this does not extend to its dual, the ̅𝒢 class (which includes the decreasing hazard rate class), while it can be extended to low-variance horizons. Finally, we construct the first example of a family of horizons, for which multiple thresholds are necessary to achieve a nonzero ratio. We establish that the Secretary Problem optimal stopping rule provides one such algorithm, paving the way towards the study of the model beyond single-threshold algorithms.}, author = {Giambartolomei, Giordano and Mallmann-Trenn, Frederik and Saona Urmeneta, Raimundo J}, booktitle = {52nd International Colloquium on Automata, Languages, and Programming}, isbn = {9783959773720}, location = {Aarhus, Denmark}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{IID prophet inequality with random horizon: Going beyond increasing hazard rates}}, doi = {10.4230/LIPIcs.ICALP.2025.87}, volume = {334}, year = {2025}, } @inproceedings{21326, abstract = {Neural Collapse is a phenomenon where the last-layer representations of a well-trained neural network converge to a highly structured geometry. In this paper, we focus on its first (and most basic) property, known as NC1: the within-class variability vanishes. While prior theoretical studies establish the occurrence of NC1 via the data-agnostic unconstrained features model, our work adopts a data-specific perspective, analyzing NC1 in a three-layer neural network, with the first two layers operating in the mean-field regime and followed by a linear layer. In particular, we establish a fundamental connection between NC1 and the loss landscape: we prove that points with small empirical loss and gradient norm (thus, close to being stationary) approximately satisfy NC1, and the closeness to NC1 is controlled by the residual loss and gradient norm. We then show that (i) gradient flow on the mean squared error converges to NC1 solutions with small empirical loss, and (ii) for well-separated data distributions, both NC1 and vanishing test loss are achieved simultaneously. This aligns with the empirical observation that NC1 emerges during training while models attain near-zero test error. Overall, our results demonstrate that NC1 arises from gradient training due to the properties of the loss landscape, and they show the co-occurrence of NC1 and small test error for certain data distributions.}, author = {Wu, Diyuan and Mondelli, Marco}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, issn = {2640-3498}, location = {Vancouver, Canada}, pages = {67499--67536}, publisher = {ML Research Press}, title = {{Neural collapse beyond the unconstrained features model: Landscape, dynamics, and generalization in the mean-field regime}}, volume = {267}, year = {2025}, } @inproceedings{21268, abstract = {We consider multiple-environment Markov decision processes (MEMDP), which consist of a finite set of MDPs over the same state space, representing different scenarios of transition structure and probability. The value of a strategy is the probability to satisfy the objective, here a parity objective, in the worst-case scenario, and the value of an MEMDP is the supremum of the values achievable by a strategy. We show that deciding whether the value is 1 is a PSPACE-complete problem, and even in P when the number of environments is fixed, along with new insights to the almost-sure winning problem, which is to decide if there exists a strategy with value 1. Pure strategies are sufficient for theses problems, whereas randomization is necessary in general when the value is smaller than 1. We present an algorithm to approximate the value, running in double exponential space. Our results are in contrast to the related model of partially-observable MDPs where all these problems are known to be undecidable.}, author = {Chatterjee, Krishnendu and Doyen, Laurent and Raskin, Jean-Francois and Sankur, Ocan}, booktitle = {52nd International Colloquium on Automata, Languages, and Programming}, isbn = {9783959773720}, location = {Aarhus, Denmark}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{The value problem for multiple-environment MDPs with parity objective}}, doi = {10.4230/LIPIcs.ICALP.2025.150}, year = {2025}, } @inproceedings{21412, abstract = {Payment channel networks (PCNs) are a promising technology that alleviates blockchain scalability by shifting the transaction load from the blockchain to the PCN. Nevertheless, the network topology has to be carefully designed to maximise the transaction throughput in PCNs. Additionally, users in PCNs also have to make optimal decisions on which transactions to forward and which to reject to prolong the lifetime of their channels. In this work, we consider an input sequence of transactions over p parties. Each transaction consists of a transaction size, source, and target, and can be either accepted or rejected (entailing a cost). The goal is to design a PCN topology among the p cooperating parties, along with the channel capacities, and then output a decision for each transaction in the sequence to minimise the cost of creating and augmenting channels, as well as the cost of rejecting transactions. Our main contribution is an 𝒪(p) approximation algorithm for the problem with p parties. We further show that with some assumptions on the distribution of transactions, we can reduce the approximation ratio to 𝒪(√p). We complement our theoretical analysis with an empirical study of our assumptions and approach in the context of the Lightning Network.}, author = {Chatterjee, Krishnendu and Křišťan, Jan Matyáš and Schmid, Stefan and Svoboda, Jakub and Yeo, Michelle X}, booktitle = {39th International Symposium on Distributed Computing}, isbn = {9783959774024}, issn = {1868-8969}, location = {Berlin, Germany}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Boosting payment channel network liquidity with topology optimization and transaction selection}}, doi = {10.4230/LIPIcs.DISC.2025.23}, volume = {356}, year = {2025}, } @inproceedings{20819, abstract = {Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many cases traditional clustering methods are not applicable, because data are increasingly being produced and stored in a distributed way, e.g. on edge devices, and privacy concerns prevent it from being transferred to a central server. To address this challenge, we present FedDP-KMeans, a new algorithm for -means clustering that is fully-federated as well as differentially private. Our approach leverages (potentially small and out-of-distribution) server-side data to overcome the primary challenge of differentially private clustering methods: the need for a good initialization. Combining our initialization with a simple federated DP-Lloyds algorithm we obtain an algorithm that achieves excellent results on synthetic and real-world benchmark tasks. We also provide a theoretical analysis of our method that provides bounds on the convergence speed and cluster identification success.}, author = {Scott, Jonathan A and Lampert, Christoph and Saulpic, David}, booktitle = {42nd International Conference on Machine Learning}, issn = {2640-3498}, location = {Vancouver, Canada}, pages = {53757--53790}, publisher = {ML Research Press}, title = {{Differentially private federated k-means clustering with server-side data}}, volume = {267}, year = {2025}, } @unpublished{21207, abstract = {Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approaches require a client to have labeled data for training or finetuning in order to obtain their own personalized model. In this paper we address this by proposing FLowDUP, a novel method that is able to generate a personalized model using only a forward pass with unlabeled data. The generated model parameters reside in a low-dimensional subspace, enabling efficient communication and computation. FLowDUP's learning objective is theoretically motivated by our new transductive multi-task PAC-Bayesian generalization bound, that provides performance guarantees for unlabeled clients. The objective is structured in such a way that it allows both clients with labeled data and clients with only unlabeled data to contribute to the training process. To supplement our theoretical results we carry out a thorough experimental evaluation of FLowDUP, demonstrating strong empirical performance on a range of datasets with differing sorts of statistically heterogeneous clients. Through numerous ablation studies, we test the efficacy of the individual components of the method.}, author = {Zakerinia, Hossein and Scott, Jonathan A and Lampert, Christoph}, booktitle = {arXiv}, title = {{Federated learning with unlabeled clients: Personalization can happen in low dimensions}}, doi = {10.48550/ARXIV.2505.15579}, year = {2025}, } @inproceedings{22828, abstract = {We propose Joint Moment Estimation (JME), a method for continually and privately estimating both the first and second moments of a data stream with reduced noise compared to naive approaches. JME supports the matrix mechanism and exploits a joint sensitivity analysis to identify a privacy regime in which the second-moment estimation incurs no additional privacy cost, thereby improving accuracy while maintaining privacy. We demonstrate JME’s effectiveness in two applications: estimating the running mean and covariance matrix for Gaussian density estimation and model training with DP-Adam.}, author = {Kalinin, Nikita and Upadhyay, Jalaj and Lampert, Christoph}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9798331338275}, issn = {1049-5258}, location = {San Diego, CA, United States}, pages = {64002--64047}, publisher = {Neural Information Processing Systems Foundation}, title = {{Continual release moment estimation with differential privacy}}, doi = {10.52202/085713-1924}, volume = {38}, year = {2025}, } @article{19841, abstract = {Context. The blue supergiant (BSG) domain contains a large variety of stars whose past and future evolutionary paths are still highly uncertain. Since binary interaction plays a crucial role in the fate of massive stars, investigating the multiplicity among BSGs helps shed light on the fate of such objects. Aims. We aim to estimate the binary fraction of a large sample of BSGs in the Small Magellanic Cloud (SMC) within the Binarity at LOw Metallicity (BLOeM) survey. In total, we selected 262 targets with spectral types B0-B3 and luminosity classes I-II. Methods. This work is based on spectroscopic data collected by the FLAMES instrument, mounted on the Very Large Telescope, which gathered nine epochs over three months. Our spectroscopic analysis for each target includes the individual and peak-to-peak radial velocity measurements, an investigation of the line profile variability, and a periodogram analysis to search for possible short- and long-period binaries. Results. By applying a 20 km s−1 threshold on the peak-to-peak radial velocities above which we would consider the star to be binary, the resulting observed spectroscopic binary fraction for our BSG sample is 23 ± 3%. An independent analysis of line profile variability reveals 11 (plus 5 candidates) double-lined spectroscopic binaries and 32 (plus 41 candidates) single-lined spectroscopic binaries. Based on these results, we estimated the overall observed binary fraction in this sample to be 34 ± 3%, which is close to the computed intrinsic binary fraction of 40 ± 4%. In addition, we derived reliable orbital periods for 41 spectroscopic binaries and potential binary candidates, among which there are 17 eclipsing binaries, including 20 SB1 and SB2 systems with periods of less than 10 days. We reported a significant drop in the binary fraction of BSGs with spectral types later than B2 and effective temperatures less than 18 kK, which could indicate the end of the main sequence phase in this temperature regime. We found no metallicity dependence in the binary fraction of BSGs, compared to existing spectroscopic surveys of the Galaxy and Large Magellanic Cloud.}, author = {Britavskiy, N. and Mahy, L. and Lennon, D. J. and Patrick, L. R. and Sana, H. and Villaseñor, J. I. and Shenar, T. and Bodensteiner, J. and Bernini-Peron, M. and Berlanas, S. R. and Bowman, D. M. and Crowther, P. A. and De Mink, S. E. and Evans, C. J. and Götberg, Ylva Louise Linsdotter and Holgado, G. and Johnston, C. and Keszthelyi, Z. and Klencki, J. and Langer, N. and Mandel, I. and Menon, A. and Moe, M. and Oskinova, L. M. and Pauli, D. and Pawlak, M. and Ramachandran, V. and Renzo, M. and Sander, A. A.C. and Schneider, F. R.N. and Schootemeijer, A. and Sen, K. and Simón-Díaz, S. and Van Loon, J. T. and Vink, J. S.}, issn = {1432-0746}, journal = {Astronomy & Astrophysics}, publisher = {EDP Sciences}, title = {{Binarity at LOw Metallicity (BLOeM): Multiplicity of early B-type supergiants in the Small Magellanic Cloud}}, doi = {10.1051/0004-6361/202452963}, volume = {698}, year = {2025}, }