activity
20102022
most citedEstimating the intrinsic dimension of datasets by a minimal neighborhood information

384 citations · 2.3k across the 72 of their papers we have counts for

collaborators

72 papers

cs.LG2022★ 41 cited

DADApy: Distance-based Analysis of DAta-manifolds in Python

Aldo Glielmo, Iuri Macocco, Diego Doimo +6

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability…

hep-ex2021

A novel measurement of initial-state gluon radiation in hadron collisions using Drell-Yan events

CDF Collaboration, T. Aaltonen, S. Amerio +397

A study of initial-state gluon radiation (ISR) in hadron collisions is presented using Drell-Yan (DY) events produced in proton-antiproton collisions by the Tevatron collider at a…

hep-ex2021

Measurement of the charge asymmetry of electrons from the decays of bosons produced in collisions at TeV

CDF Collaboration, T. Aaltonen, S. Amerio +398

At the Fermilab Tevatron proton-antiproton () collider, high-mass electron-neutrino () pairs are produced predominantly in the process $p \bar{p} \rightarrow W(\right…

hep-ex2019★ 1 cited

Search for Higgs-like particles produced in association with bottom quarks in proton-antiproton collisions

CDF Collaboration, T. Aaltonen, S. Amerio +396

We report on a search for a spin-zero non-standard-model particle in proton-antiproton collisions collected by the Collider Detector at Fermilab at a center-of-mass-energy of 1.96…

hep-ex2018★ 1 cited

Search for standard-model Z and Higgs bosons decaying into a bottom-antibottom quark pair in proton-antiproton collisions at 1.96 TeV

CDF Collaboration, T. Aaltonen, S. Amerio +396

The Collider Detector at Fermilab collected a unique sample of jets originating from bottom-quark fragmentation (-jets) by selecting online proton-antiproton () collis…

stat.ML2018★ 384 cited

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Elena Facco, Maria d'Errico, Alex Rodriguez +1

Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond. Several approaches work on the assumption…