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Matteo Migliorini

Publications and source records attributed to Matteo Migliorini.

14 recordsLinked to original sources
arXiv2026

Improved algebraic fibrations of high-dimensional hyperbolic groups

For every $d \geq 3$, we construct infinitely many quasi-isometry classes of hyperbolic groups $G$ of cohomological dimension $d$ that algebraically fibre with finitely presented kernel. All our groups arise as finite-index subgroups of right-angled Coxeter groups. In many cases, the $L^2$-Betti numbers of the groups $G$ provide obstructions to higher finiteness properties of the kernel. Our groups therefore expand the list of subgroups of hyperbolic groups with exotic finiteness properties.

math.GR↗
arXiv2025

Complete classification of the Dehn functions of Bestvina-Brady groups

We prove that the Dehn function of every finitely presented Bestvina-Brady group grows as a linear, quadratic, cubic, or quartic polynomial. In fact, we provide explicit criteria on the defining graph to determine the degree of this polynomial. As a consequence, we identify an obstruction that prevents certain Bestvina-Brady groups from admitting a CAT(0) structure.

math.GR↗
arXiv2025

Perfect circle-valued Morse functions on hyperbolic 6-manifolds

We build the first example of a hyperbolic 6-manifold that admits a perfect circle-valued Morse function, which can be considered as the analogue of a fibration over the circle for manifolds with non-vanishing Euler characteristic. As a consequence, we obtain a new example of a subgroup of a hyperbolic group which is of type $\mathcal{F}_2$ but not $\mathcal{F}_3$.

math.GT↗
arXiv2025

Dehn functions of subgroups of products of free groups. Part II: Precise computations

We prove that the Bridson-Dison group has quartic Dehn function, thereby providing the first precise computation of the Dehn function of a subgroup of a direct product of free groups with super-quadratic Dehn function. We also prove that coabelian subgroups of direct products of $n$ free groups of finiteness type $\mathcal{F}_{n-1}$ and of corank $r\leq n-2$ have quadratic Dehn functions.

math.GR↗
arXiv2024

Dehn functions of subgroups of products of free groups. Part I: Uniform upper bounds

Subgroups of direct products of finitely many finitely generated free groups form a natural class that plays an important role in geometric group theory. Its members include fundamental examples, such as the Stallings-Bieri groups. This raises the problem of understanding their geometric invariants. We prove that finitely presented subgroups of direct products of three free groups, as well as subgroups of finiteness type $\mathcal{F}_{n-1}$ in a direct product of $n$ free groups, have Dehn function bounded above by $N^9$. This gives a positive answer to a question of Dison within these important subclasses and provides new insights in the context of Bridson's conjecture stating that finitely presented subgroups of direct products of free groups have polynomially bounded Dehn function. To prove our results we generalise techniques for "pushing fillings" into normal subgroups.

math.GR↗
arXiv2023

Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection

This work describes an online processing pipeline designed to identify anomalies in a continuous stream of data collected without external triggers from a particle detector. The processing pipeline begins with a local reconstruction algorithm, employing neural networks on an FPGA as its first stage. Subsequent data preparation and anomaly detection stages are accelerated using GPGPUs. As a practical demonstration of anomaly detection, we have developed a data quality monitoring application using a cosmic muon detector. Its primary objective is to detect deviations from the expected operational conditions of the detector. This serves as a proof-of-concept for a system that can be adapted for use in large particle physics experiments, enabling anomaly detection on datasets with reduced bias.

hep-ex↗
arXiv2022

Hyperbolic manifolds that fiber algebraically up to dimension 8

We construct some cusped finite-volume hyperbolic $n$-manifolds $M_n$ that fiber algebraically in all the dimensions $5\leq n \leq 8$. That is, there is a surjective homomorphism $π_1(M_n) \to \mathbb Z$ with finitely generated kernel. The kernel is also finitely presented in the dimensions $n=7, 8$, and this leads to the first examples of hyperbolic $n$-manifolds $\widetilde M_n$ whose fundamental group is finitely presented but not of finite type. These $n$-manifolds $\widetilde M_n$ have infinitely many cusps of maximal rank and hence infinite Betti number $b_{n-1}$. They cover the finite-volume manifold $M_n$. We obtain these examples by assigning some appropriate colours and states to a family of right-angled hyperbolic polytopes $P_5, \ldots, P_8$, and then applying some arguments of Jankiewicz, Norin, Wise and Bestvina, Brady. We exploit in an essential way the remarkable properties of the Gosset polytopes dual to $P_n$, and the algebra of integral octonions for the crucial dimensions $n=7,8$.

math.GT↗
arXiv2022

A horizontally scalable online processing system for trigger-less data acquisition

The majority of high energy physics experiments rely on data acquisition and hardware-based trigger systems performing a number of stringent selections before storing data for offline analysis. The online reconstruction and selection performed at the trigger level are bound to the synchronous nature of the data acquisition system, resulting in a trade-off between the amount of data collected and the complexity of the online reconstruction performed. Exotic physics processes, such as long-lived and slow-moving particles, are rarely targeted by online triggers as they require complex and nonstandard online reconstruction, usually incompatible with the time constraints of most data acquisition systems. The online trigger selection can thus impact as one of the main limiting factors to the experimental reach for exotic signatures. Alternative data acquisition solutions based on the continuous and asynchronous processing of the stream of data from the detectors are therefore foreseeable. Trigger-less data readout systems, paired with efficient streaming data processing solutions, can provide a viable alternative. In this document, an end-to-end implementation of a fully trigger-less data acquisition and online processing system is discussed. An easily scalable and deployable implementation of such an architecture is proposed, based on open-source distributed computing frameworks capable of performing asynchronous online processing of streaming data. The proposed schema can be suitable for deployment as a fully integrated system for small-scale experimental apparatus, or to complement the trigger-based data acquisition systems of larger experiments. A muon telescope setup consisting of a set of gaseous detectors is used as the experimental development testbed in this work, and a fully integrated online processing pipeline deployed on cloud computing resources is implemented and described.

physics.ins-det↗
arXiv2021

Hyperbolic 5-manifolds that fiber over $S^1$

We exhibit some finite-volume cusped hyperbolic 5-manifolds that fiber over the circle. These include the smallest hyperbolic 5-manifold known, discovered by Ratcliffe and Tschantz. As a consequence, we build a finite type subgroup of a hyperbolic group that is not hyperbolic.

math.GT↗
arXiv2021

Muon trigger with fast Neural Networks on FPGA, a demonstrator

The online reconstruction of muon tracks in High Energy Physics experiments is a highly demanding task, typically performed with programmable logic boards, such as FPGAs. Complex analytical algorithms are executed in a quasi-real-time environment to identify, select and reconstruct local tracks in often noise-rich environments. A novel approach to the generation of local triggers based on an hybrid combination of Artificial Neural Networks and analytical methods is proposed, targeting the muon reconstruction for drift tube detectors. The proposed algorithm exploits Neural Networks to solve otherwise computationally expensive analytical tasks for the unique identification of coherent signals and the removal of the geometrical ambiguities. The proposed approach is deployed on state-of-the-art FPGA and its performances are evaluated on simulation and on data collected from cosmic rays.

hep-ex↗
arXiv2020

Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics

The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases poses several technological challenges, most importantly on the actual implementation of dedicated end-to-end data pipelines. A solution to these challenges is presented, which allows training neural network classifiers using solutions from the Big Data and data science ecosystems, integrated with tools, software, and platforms common in the HEP environment. In particular, Apache Spark is exploited for data preparation and feature engineering, running the corresponding (Python) code interactively on Jupyter notebooks. Key integrations and libraries that make Spark capable of ingesting data stored using ROOT format and accessed via the XRootD protocol, are described and discussed. Training of the neural network models, defined using the Keras API, is performed in a distributed fashion on Spark clusters by using BigDL with Analytics Zoo and also by using TensorFlow, notably for distributed training on CPU and GPU resourcess. The implementation and the results of the distributed training are described in detail in this work.

cs.DC↗
arXiv2018

On the shape factor of interaction laws for a non-local approximation of the Sobolev norm and the total variation

We consider the family of non-local and non-convex functionals introduced by H. Brezis and H.-M. Nguyen in a recent paper. These functionals Gamma-converge to a multiple of the Sobolev norm or the total variation, depending on a summability exponent, but the exact values of the constants are unknown in many cases. We describe a new approach to the Gamma-convergence result that leads in some special cases to the exact value of the constants, and to the existence of smooth recovery families.

math.FA↗
arXiv2018

Optimal constants for a non-local approximation of Sobolev norms and total variation

We consider the family of non-local and non-convex functionals proposed and investigated by J. Bourgain, H. Brezis and H.-M. Nguyen in a series of papers of the last decade. It was known that this family of functionals Gamma-converges to a suitable multiple of the Sobolev norm or the total variation, depending on the summability exponent, but the exact constants and the structure of recovery families were still unknown, even in dimension one. We prove a Gamma-convergence result with explicit values of the constants in any space dimension. We also show the existence of recovery families consisting of smooth functions with compact support. The key point is reducing the problem first to dimension one, and then to a finite combinatorial rearrangement inequality.

math.FA↗