Even if laboratory practice is essential for all scientific branches of knowledge, it is often neglected at High School, due to lack of time and/or resources. To establish a closer contact between school and experimental sciences, Sapienza Universit`
a di Roma and the Istituto Nazionale di Fisica Nucleare (INFN) launched the Lab2Go project, with the goal of spreading laboratory practice among students and teachers in high schools.
We adapt a method, originally developed for searches for quasi-monochromatic, quasi-infinite gravitational-wave signals, to directly detect new light gauge bosons with laser interferometers, which could be candidates for dark matter. To search for th
ese particles, we optimally choose the analysis coherence time as a function of boson mass, such that all of the signal power will be confined to one frequency bin. We focus on the dark photon, a gauge boson that could couple to baryon or baryon-lepton number, and explain that its interactions with gravitational-wave interferometers result in a narrow-band, stochastic signal. We provide an end-to-end analysis scheme, estimate its computational cost, and investigate follow-up techniques to confirm or rule out dark matter candidates. Furthermore, we derive a theoretical estimate of the sensitivity, and show that it is consistent with both the empirical sensitivity determined through simulations, and results from a cross-correlation search. Finally, we place Feldman-Cousins upper limits using data from LIGO Livingstons second observing run, which give a new and strong constraint on the coupling of gauge bosons to the interferometer.
textit{Superradiance} can trigger the formation of an ultra-light boson cloud around a spinning black hole. Once formed, the boson cloud is expected to emit a nearly periodic, long-duration, gravitational-wave signal. For boson masses in the range $(
10^{-13}-10^{-11})$ eV, and stellar mass black holes, such signals are potentially detectable by gravitational wave detectors, like Advanced LIGO and Virgo. In this {it Letter} we present full band upper limits for a generic all-sky search for periodic gravitational waves in LIGO O2 data, and use them to derive - for the first time - direct constraints on the ultra-light scalar boson field mass.
We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust towards si
gnal morphologies that differ from the training set, and they do not require many training injections/data to guarantee good detection efficiency and low false alarm probability. In fact, we only need to train one CNN on signal/noise maps in a single 150 Hz band; afterwards, the CNN can distinguish signals/noise well in any band, though with different efficiencies and false alarm probabilities due to the non-stationary noise in LIGO/Virgo. We demonstrate that we can control the false alarm probability for the CNNs by selecting the optimal threshold on the outputs of the CNN, which appears to be frequency dependent. Finally we compare the detection efficiencies of the networks to a well-established algorithm, the Generalized FrequencyHough (GFH), which maps curves in the time/frequency plane to lines in a plane that relates to the initial frequency/spindown of the source. The networks have similar sensitivities to the GFH but are orders of magnitude faster to run and can detect signals to which the GFH is blind. Using the results of our analysis, we propose strategies to apply CNNs to a real search using LIGO/Virgo data to overcome the obstacles that we would encounter, such as a finite amount of training data. We then use our networks and strategies to run a real search for a remnant of GW170817, making this the first time ever that a machine learning method has been applied to search for a gravitational wave signal from an isolated neutron star.
We describe a method to detect gravitational waves lasting $O(hours-days)$ emitted by young, isolated neutron stars, such as those that could form after a supernova or a binary neutron star merger, using advanced LIGO/Virgo data. The method is based
on a generalization of the FrequencyHough (FH), a pipeline that performs hierarchical searches for continuous gravitational waves by mapping points in the time/frequency plane of the detector to lines in the frequency/spindown plane of the source. We show that signals whose spindowns are related to their frequencies by a power law can be transformed to coordinates where the behavior of these signals is always linear, and can therefore be searched for by the FH. We estimate the sensitivity of our search across different braking indices, and describe the portion of the parameter space we could explore in a search using varying fast Fourier Transform (FFT) lengths.
We describe a novel, very fast and robust, directed search incoherent method for periodic gravitational waves (GWs) from neutron stars in binary systems. As directed search, we assume the source sky position to be known with enough accuracy, but all
other parameters are supposed to be unknown. We exploit the frequency-modulation due to source orbital motion to unveil the signal signature by commencing from a collection of time and frequency peaks. We validate our pipeline adding 131 artificial continuous GW signals from pulsars in binary systems to simulated detector Gaussian noise, characterized by a power spectral density Sh = 4x10^-24 Hz^-1/2 in the frequency interval [70, 200] Hz, which is overall commensurate with the advanced detector design sensitivities. The pipeline detected 128 signals, and the weakest signal injected and detected has a GW strain amplitude of ~10^-24, assuming one month of gapless data collected by a single advanced detector. We also provide sensitivity estimations, which show that, for a single- detector data covering one month of observation time, depending on the source orbital Doppler modulation, we can detect signals with an amplitude of ~7x10^-25. By using three detectors, and one year of data, we would easily gain more than a factor 3 in sensitivity, translating into being able to detect weaker signals. We also discuss the parameter estimate proficiency of our method, as well as computational budget, which is extremely cheap. In fact, sifting one month of single-detector data and 131 Hz-wide frequency range takes roughly 2.4 CPU hours. Due to the high computational speed, the current procedure can be readily applied in ally-sky schemes, sieving in parallel as many sky positions as permitted by the available computational power.
We describe the extension to multiple datasets of a coherent method for the search of continuous gravitational wave signals, based on the computation of 5-vectors. In particular, we show how to coherently combine different datasets belonging to the s
ame detector or to different detectors. In the latter case the coherent combination is the way to have the maximum increase in signal-to-noise ratio. If the datasets belong to the same detector the advantage comes mainly from the properties of a quantity called {it coherence} which is helpful (in both cases, in fact) in rejecting false candidates. The method has been tested searching for simulated signals injected in Gaussian noise and the results of the simulations are discussed.
