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1. Improved Constraints on Sterile Neutrino Mixing from Disappearance Searches in the MINOS, MINOS+ , Daya Bay, and Bugey-3 Experiments

   https://doi.org/10.1103/PhysRevLett.125.071801  

   Adamson, P. ; An, F. P. ; Anghel, I. ; Aurisano, A. ; Balantekin, A. B. ; Band, H. R. ; Barr, G. ; Bishai, M. ; Blake, A. ; Blyth, S. ; et al ( August 2020 , Physical Review Letters)
2. Precision Constraints for Three-Flavor Neutrino Oscillations from the Full MINOS+ and MINOS Dataset

   https://doi.org/10.1103/PhysRevLett.125.131802  

   Adamson, P. ; Anghel, I. ; Aurisano, A. ; Barr, G. ; Blake, A. ; Cao, S. V. ; Carroll, T. J. ; Castromonte, C. M. ; Chen, R. ; Childress, S. ; et al ( September 2020 , Physical Review Letters)

   null (Ed.)
3. Automated Collider Event Selection, Plotting, & Machine Learning with AEACuS, RHADAManTHUS, & MInOS

   https://doi.org/10.22323/1.409.0027  

   Walker, Joel W. ( July 2022 , Computational Tools for High Energy Physics and Cosmology (CompTools2021))

   Arbey, Alexandre ; Bélanger, G. ; Desai, Nishita ; Gonzalo, Tomas ; Harlander, Robert V. (Ed.)

   A trio of automated collider event analysis tools are described and demonstrated, in the form of a quick-start tutorial. AEACuS interfaces with the standard MadGraph/MadEvent, Pythia, and Delphes simulation chain, via the Root file output. An extensive algorithm library facilitates the computation of standard collider event variables and the transformation of object groups (including jet clustering and substructure analysis). Arbitrary user-defined variables and external function calls are also supported. An efficient mechanism is provided for sorting events into channels with distinct features. RHADAManTHUS generates publication-quality one- and two-dimensional histograms from event statistics computed by AEACuS, calling MatPlotLib on the back end. Large batches of simulation (representing either distinct final states and/or oversampling of a common phase space) are merged internally, and per-event weights are handled consistently throughout. Arbitrary bin-wise functional transformations are readily specified, e.g. for visualizing signal-to-background significance as a function of cut threshold. MInOS implements machine learning on computed event statistics with XGBoost. Ensemble training against distinct background components may be combined to generate composite classifications with enhanced discrimination. ROC curves, as well as score distribution, feature importance, and significance plots are generated on the fly. Each of these tools is controlled via instructions supplied in a reusable cardfile, employing a simple, compact, and powerful meta-language syntax. 

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4. Beyond the Classroom: Profiling Muon Flux in Relation to Overburden in Fermilab’s MINOS Tunnel

   https://doi.org/10.1119/5.0025659  

   Winkler, Eleanor Anne ; Graham, Paul ; Unterman, Nathan A. ; Grey, Benjamin ; Miller, Jacob M. ; Miller, Max J. ; Sears, Allen J. ; Bernat, Alex ; Frank, Shoshana ; Simon, Joshua ; et al ( February 2022 , The Physics Teacher)
5. Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes

   https://doi.org/10.1186/s13059-022-02714-x  

   Hunt, Martin ; Letcher, Brice ; Malone, Kerri M. ; Nguyen, Giang ; Hall, Michael B. ; Colquhoun, Rachel M. ; Lima, Leandro ; Schatz, Michael C. ; Ramakrishnan, Srividya ; CRyPTIC consortium ; et al ( July 2022 , Genome Biology)

   There are many short-read variant-calling tools, with different strengths and weaknesses. We present a tool, Minos, which combines outputs from arbitrary variant callers, increasing recall without loss of precision. We benchmark on 62 samples from three bacterial species and an outbreak of 385Mycobacterium tuberculosissamples. Minos also enables joint genotyping; we demonstrate on a large (N=13k)M. tuberculosiscohort, building a map of non-synonymous SNPs and indels in a region where all such variants are assumed to cause rifampicin resistance. We quantify the correlation with phenotypic resistance and then replicate in a second cohort (N=10k).

    

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