Related papers: Machine Learning applied to Multifrequency Data in…

We study a sample of 30 high-redshift blazars ($z>2.5$) by means of spectra and the radiation mechanism with Fermi Large Area Telescope $\gamma$-ray observations spanning 15 years. Three models -- the power law, power law with an…

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster…

Multilayer Extreme Learning Machine (ML-ELM) and its variants have proven to be an effective technique for the classification of different natural signals such as audio, video, acoustic and images. In this paper, a Hybrid Multilayer Extreme…

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated…

Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction…

It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model…

Understanding the robustness of deep learning models for multivariate long-term time series forecasting (M-LTSF) remains challenging, as evaluations typically rely on real-world datasets with unknown noise properties. We propose a…

This contribution describes the experience with the application of different Machine Learning (ML) techniques to a physics analysis case. The use case chosen is the classification of top-antitop events coming from BSM or from SM using data…

Interpreting the spectral energy distributions (SEDs) of astrophysical objects with physically motivated models is computationally expensive. These models require solving coupled differential equations in high-dimensional parameter spaces,…

We present the results of a multi-wavelength study of a population of X-ray bright ($\rm log(F_{0.2-12 \ keV})>-12.5$), non-$\gamma$-ray detected high and extreme high synchrotron peak (HSP, EHSP; $\rm log(\nu_{\rm peak,\ Hz})>16$) BL Lacs…

The growing number of man-made debris in Earth's orbit poses a threat to active satellite missions due to the risk of collision. Characterizing unknown debris is, therefore, of high interest. Light Curves (LCs) are temporal variations of…

We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as…

This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both…

The machine learning (ML) techniques to predict unitarity (UNI) and bounded from below (BFB) constraints in multi-scalar models is employed. The effectiveness of this approach is demonstrated by applying it to the two and three Higgs…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

The fourth Fermi Large Area Telescope (LAT) catalog (4FGL) contains 5064 $\gamma$-ray sources detected at high significance, but 26% of them still lack associations at other wavelengths. The SPT-SZ survey, conducted between 2008 and 2011…

Blazars are highly variable sources over timescales that can be as low as minutes. This is the case of the High Energy Peaked BL Lac (HBL) objects showing strong variability in X-rays, which highly correlate with that of the TeV emission.…

Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in…

We present new, simultaneous, multifrequency observations of 45 out of the 55 candidate High Frequency Peakers (HFP) selected by Dallacasa et al. (2000), carried out 3 to 4 years after a first set of observations. Our sub-sample consists of…

A sample of 312 low-frequency peaked BL Lacertae objects (LBLs) and 694 flat spectrum radio quasars (FSRQs) with the parameters both redshift and $\gamma$-ray photon spectral index ($\Gamma _\gamma$) is compiled from the active galactic…