sebastianmacaluso Goto Github PK

astartrellis

base16-ipython-notebook

Themes for your IPython Notebook

benchmark

clustertrellis

curve_fit

deep-learning_jet-images

Deep learning and computer vision techniques to identify jet substructure from proton-proton collisions at the Large Hadron Collider

energyflow

Python package for the EnergyFlow suite of tools.

flatmc

ginkgo

ginkgo_paper

Notes on Ginkgo

goldmine

Mining gold from various simulators for better likelihood-free inference

hcmanager

iris-hep.github.io

Institute for Research and Innovation in Software for High Energy Physics

jambot

junipr

Implementation of JUNIPR from arXiv:1804.09720

one-page-wonder-jekyll

Jekyll port of One Page Wonder

recasting-ichep-2016

We derive the latest constraints on various simplified models of natural SUSY with light higgsinos, stops and gluinos, using a detailed and comprehensive reinterpretation of the most recent 13 TeV ATLAS and CMS searches with ∼ 15 fb−1 of data. We discuss the implications of these constraints for fine-tuning of the electroweak scale. While the most “vanilla” version of SUSY (the MSSM with Rparity and flavor-degenerate sfermions) with 10% fine-tuning is ruled out by the current constraints, models with decoupled valence squarks or reduced missing energy can still be fully natural. However, in all of these models, the mediation scale must be extremely low (< 100 TeV). We conclude by considering the prospects for the high-luminosity LHC era, where we expect the current limits on particle masses to improve by up to ∼ 1 TeV, and discuss further model-building directions for natural SUSY that are motivated by this work.

recnn_tensorflow

Recursive Neural Network implementation for jet physics in TensorFlow

rl-baselines3-zoo_seb

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

s2and

Semantic Scholar's Author Disambiguation Algorithm & Evaluation Suite

sebastianmacaluso.github.io

stable-baselines3_seb

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

standardhc

stops-compressed-limit

Design and optimization of a set of cuts on variables that lead to discovery sensitivities on supersymmetric top quark production events in the compressed limit at Run II of the Large Hadron Collider. This involved simulation and analysis of signal and background events. This analysis was implemented on experimental data by members of the ATLAS collaboration at Harvard University producing results exactly as expected.

talksslides

Collection of slides from selected recent talks

toptagcomparison

treenin

Recursive Neural Network implementation for jet physics for GPU batch training with PyTorch.

trellisvi

Variational Inference with the Trellis

visualizebinarytrees