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What is your favourite open software package for theoretical astroparticle physics and cosmology (for gravitational waves, numerical relativity, CMB, neutrinos, dark matter, etc.)? We will list the most useful public codes on the EuCAPT webpage as a resource for the community

We are happy to announce the 3rd Dark Machines DarkMachines workshop Workshop on Accelerating the Search for Dark Matter with Machine Learning 27 April 2020 to 1 May 2020 at CERN, GENEVA, SWITZERLAND (please retweet)

We invite all scientists (PhD students, postdocs, and staff) active in theoretical astroparticle physics and cosmology & affiliated to a research institution in a European country, to participate in the 1st EuCAPT census by following this link: indico.cern.ch/event/875334/ Please RT

LES HOUCHES 2019: PHYSICS AT TEV COLLIDERS NEW PHYSICS WORKING GROUP is now online arxiv.org/pdf/2002.12220… Comes with 10fb-1 of simulated LHC data for phenomenology from Dark Machines + other pheno ML training data First datasets phenoMLdata.org

We have decided in a meeting of the organisers to postpone the 2020 darkmachines workshop at CERN until autumn (dates still have to be set) due to the current situation with SARS-COV-2. Please cancel your CERN hostel on an individual basis. Sorry !

Interesting Emory TMLS virtual workshop on “Learning New Physics with Machine Learning” starting today at 4pm CEST 👇

A new paper appeared! The Dark Machines sampling group has put its latest work on arXiv.org: A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications. Paper can be found at arxiv.org/abs/2101.04525. Summary thread below 👇

First in a series of 2021 Dark Machines papers: A comparison of optimisation algorithms for high-dimensional particle/astrophysics applications Trying to find optimal parameters of your physics model ? Is your chain of simulators non-differentiable? ->arxiv.org/abs/2101.04525

Secret dataset for the unsupervised search challenge available ... stay tuned...

You are looking for training data for your machine learning model. Take the simulated LHC events, full SM background + signal challenge. > 1000000000 events arxiv.org/pdf/2105.14027…

This is the link to the nice CERN-Courier article about Dark Machines and LHC Olympics anomaly detection challenges at LHC. cerncourier.com/a/whats-in-the… with e.g. Ben Nachman , Dr. Melissa van Beekveld , @BryanOstdiek , Luc Hendriks , @xmpierinix , @CatDogLund, Gregor Kasieczka

Our Dark Machines LHC unsupervised data made it to an art project !!! This is really cool ;) Thanks. with Caterina Doglioni @xmpierinix , @BryanOstdiek , Luc Hendriks , Dr. Melissa van Beekveld and many others

New Dark Machines related paper on automatic source identification. "Training" on astronomical data from telescope A (Meerlicht) and application to telescope B (Hubble) works very well... arxiv.org/abs/2202.00489