arXiv subjects

Marco Serra

Publications and source records attributed to Marco Serra.

5 recordsLinked to original sources

arXiv2026

Alternative neural-network follow-up for all-sky FrequencyHough in continuous gravitational-wave searches

Continuous gravitational waves are long-lived signals emitted by spinning neutron stars (NSs) or boson clouds around black holes. While the Milky Way is expected to host $\mathrm{O}(10^{8}-10^{9})$ NSs, only $\mathrm{O}(10^{3})$ are currently identified through electromagnetic observations. This large observational gap motivates searches based on alternative messengers. In particular, all-sky searches for continuous gravitational waves (GWs) offer a unique opportunity to detect NSs that are electromagnetically silent or otherwise undiscovered. By exploring a broad region of parameter space without any a priori source information, these searches can probe the vast hidden NS population of our Galaxy. In this work, we present a novel way to include a Neural Network (NN) classifier into an all-sky search strategy for isolated NSs. Starting from candidates produced by the FrequencyHough pipeline running on data from ground-based detectors such as LIGO and Virgo, the proposed method improves sensitivity without increasing computational cost and can be parallelized across multiple detectors to enhance detection probability and reduce false alarms. We analyze data from the third observing run (O3) --- April 1, 2019 to March 27, 2020 --- and focus on the frequency range [129, 229] Hz and spin-down interval [$-2.5\cdot10^{-9}, 1.0\cdot10^{-9}$] Hz/s. The model is trained on noise constructed to be statistically consistent with real O3 data and tested on real O3 data. Results show robust performance in distinguishing signal from noise, even at low strain, and successful identification of hardware injections outside the training spin-down range.

gr-qc↗

arXiv2025

The Cosmological Constant and Dark Dimensions from Non-Supersymmetric Strings

We present a string theory construction in which the particle physics contributions to the one-loop vacuum energy exactly cancel, whilst the gravitational contributions are suppressed in the size of one or two large extra dimensions. This provides an ultraviolet realisation of the Dark Dimension and Supersymmetric Large Extra Dimensions scenarios, with, moreover, an explanation as to why the Standard Model contributions to the vacuum energy cancel without the need of eV mass-splittings. Gravity propagates in micron sized dark dimension(s), whilst the visible and hidden sectors are supported on D-branes. Supersymmetry is broken in the dark dimension(s) \`a la Scherk-Schwarz, whereas supersymmetry is broken at the string scale, \`a la Brane Supersymmetry Breaking, in the D-branes sector, without inducing tadpoles, similarly to a different construction proposed a long time ago by Angelantonj and Antoniadis. Vacuum energy from the visible sector is cancelled by the vacuum energy of the hidden sector branes. We also discuss moduli stabilization in this set-up, finding that the interplay between the Scherk-Schwarz one-loop contribution and non-perturbative effects can fix the size of the dark dimension(s) to be exponentially large in the inverse string-coupling, leading to an exponentially small total vacuum energy, with all moduli stabilised in a dS saddle.

hep-th↗

arXiv2025

Investigating all-sky Frequency Hough performances for neutron stars

Between the estimated population of Neutron Stars (NSs) and the actual number present in the catalogs, there is a huge gap: O(10$^{8-9}$) vs O(10$^3$). Among the different search techniques for Continuous gravitational waves (CWs), the all-sky could help to reduce the discrepancy. We focus on the all-sky CW pipeline Frequency Hough (FH), which operates without prior knowledge of the source parameters ($f,\dot{f}, \lambda, \beta$). Here, we present a Machine Learning strategy, diverging from the standard follow-up(FU) of the FH pipeline. We study the performance with real interferometer data, until reaching $h$ value subthreshold for the standard FU procedure ($CR_{thr}=5$), with encouraging classification results.

gr-qc↗

arXiv2024

On (A)dS Solutions from Scherk-Schwarz Orbifolds

We investigate the existence of dS vacua in supersymmetry-breaking Scherk-Schwarz toroidal compactifications of type II string theory, using the well-understood ingredients of curvature, fluxes and 1-loop Casimir energy. Starting from the 10d equations, we derive a series of no-go theorems and existence conditions for dS, and present two explicit, fully-backreacted, solutions: a dS one, which turns out to be not under control, and an AdS one, which can be chosen at arbitrarily weak coupling and large volume by dialling the unbounded fluxes. We then use a lower-dimensional EFT description to show that any dS solution has a universal tachyon and no parametric control. The simplest AdS solutions are also perturbatively unstable. We extend the no-go theorems to slow-roll acceleration and test various swampland conjectures in our non-supersymmetric string setup. The question of numerically controlled, unstable dS is left open.

hep-th↗

arXiv2024

Neural network method to search for long transient gravitational waves

We present a new method to search for long transient gravitational waves signals, like those expected from fast spinning newborn magnetars, in interferometric detector data. Standard search techniques are computationally unfeasible (matched filtering) or very demanding (sub-optimal semi-coherent methods). We explored a different approach by means of machine learning paradigms, to define a fast and inexpensive procedure. We used convolutional neural networks to develop a classifier that is able to discriminate between the presence or the absence of a signal. To complement the classification and enhance its effectiveness, we also developed a denoiser. We studied the performance of both networks with simulated colored noise, according to the design noise curve of LIGO interferometers. We show that the combination of the two models is crucial to increase the chance of detection. Indeed, as we decreased the signal initial amplitude (from $10^{-22}$ down to $10^{-23}$) the classification task became more difficult. In particular, we could not correctly tag signals with an initial amplitude of $2 \times 10^{-23}$ without using the denoiser. By studying the performance of the combined networks, we found a good compromise between the search false alarm rate (2$\%$) and efficiency (90$\%$) for a single interferometer. In addition, we demonstrated that our method is robust with respect to changes in the power law describing the time evolution of the signal frequency. Our results highlight the computationally low cost of this method to generate triggers for long transient signals. The study carried out in this work lays the foundations for further improvements, with the purpose of developing a pipeline able to perform systematic searches of long transient signals.

astro-ph.IM↗