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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…

We report the discovery of a single-lined spectroscopic binary with an Ultra Cool Dwarf (UCD) primary with a spectral type between M8 and L0.5. This system was discovered during the course of an ongoing survey to monitor L dwarfs for radial…

With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels…

We investigate applications of machine learning models to directly infer physical properties of brown dwarfs from their photometry and spectra using $\textit{The Cannon}$. We demonstrate that absolute magnitudes, spectral types, and…

We present the first results from applying the spectral inversion technique in the cloudy L dwarf regime. Our new framework provides a flexible approach to modelling cloud opacity which can be built incrementally as the data requires, and…

We present first results from a major program of methane filter photometry for low-mass stars and brown dwarfs. The definition of a new methane filter photometric system is described. A recipe is provided for the differential calibration of…

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely…

Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical…

This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing…

This study examines the characterization of binary star systems using Spectral Energy Distributions (SEDs), a technique increasingly essential with the rise of large-scale astronomical surveys. Binaries can emit flux at different regions of…

In this article we apply the random forest machine learning model to classify 1D topological phases when strong disorder is present. We show that using the entanglement spectrum as training features the model gives high classification…

(Abridged) Highly unequal-mass ratio binaries are rare among field brown dwarfs, with the known census described by q^(4.9+/-0.7). However, such systems can test the joint accuracy of evolutionary and atmospheric models, under the…

We present the results of a simulation to investigate the prospects of measuring mass, age, radius, metallicity and luminosity data for brown dwarfs in fully eclipsing binary systems around late K and early M dwarfs identified by…

We aim at building a method that efficiently identifies young low-mass stars and brown dwarfs from low-resolution near-infrared spectra, by studying gravity-sensitive features and their evolution with age. We built a dataset composed of all…

We present the first brown dwarf spectral binary characterized with JWST: WISE J014656.66+423410.0, the coldest blended-light brown dwarf binary straddling the T/Y transition. We obtained a moderate resolution (R$\sim$2700) G395H spectrum…

We analyze how accurately supervised machine learning techniques can predict the lowest energy levels of one-dimensional noninteracting ultracold atoms subject to the correlated disorder due to an optical speckle field. Deep neural networks…

By comparing near-infrared spectra with atmosphere models, we infer the effective temperature, surface gravity, projected rotational velocity, and radial velocity for 21 very-low-mass stars and brown dwarfs. The unique sample consists of…

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a…

Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based…