Related papers: Sequencing Silicates in the IRS Debris Disk Catalo…
Debris disks are the dust disks found around ~20% of nearby main sequence stars in far-IR surveys. They can be considered as descendants of protoplanetary disks or components of planetary systems, providing valuable information on…
Debris disks are exoplanetary systems containing planets, minor bodies (such as asteroids and comets) and debris dust. Unseen planets are presumed to perturb the minor bodies into crossing orbits, generating small dust grains that are…
Observations of debris disks allow for the study of planetary systems, even where planets have not been detected. However, debris disks are often only characterized by unresolved infrared excesses that resemble featureless blackbodies, and…
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The…
Debris disks are optically thin, almost gas-free dusty disks observed around a significant fraction of main-sequence stars older than about 10 Myr. Since the circumstellar dust is short-lived, the very existence of these disks is considered…
The study of the last stages of planet formation, also known as debris disks, is fundamental to place constrains on the formation of planetary sized bodies. Debris disks are composed of dust and occasionally small amounts of gas, both…
Debris disks are tenuous, dust-dominated disks commonly observed around stars over a wide range of ages. Those around main sequence stars are analogous to the Solar System's Kuiper Belt and Zodiacal light. The dust in debris disks is…
Mineralogical studies of silicate features emitted by dust grains in protoplanetary disks and Solar System bodies can shed light on the progress of planet formation. The significant fraction of crystalline material in comets, chondritic…
Main sequence stars, like the Sun, are often found to be orbited by circumstellar material that can be categorized into two groups, planets and debris. The latter is made up of asteroids and comets, as well as the dust and gas derived from…
Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific…
Debris discs are dusty belts of planetesimals around main-sequence stars, similar to the asteroid and Kuiper belts in our solar system. The planetesimals cannot be observed directly, yet they produce detectable dust in mutual collisions.…
Methods to constrain the surface mineralogy of asteroids have seen considerable development during the last decade with advancement in laboratory spectral calibrations and validation of our interpretive methodologies by spacecraft…
Debris disks give us the unique opportunity to probe the properties of small $\mu$m-sized particles, allowing us to peer into the constituents of their parent bodies, young analogs of comets and asteroids of our solar system. In the past,…
The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective. Clustering and classifying spectrally similar materials is often a first step in…
Main sequence stars are commonly surrounded by debris disks, composed of cold dust continuously replenished by a reservoir of undetected dust-producing planetesimals. In a planetary system with a belt of planetesimals (like the Solar…
We know little about the outermost exoplanets in planetary systems, because our detection methods are insensitive to moderate-mass planets on wide orbits. However, debris discs can probe the outer-planet population, because dynamical…
Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is…
We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar…
Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input.…
We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data…