Statistical-mechanical lattice models for protein-DNA binding in chromatin

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibriums measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.Comment: 19 pages, 7 figures, accepted author manuscript, to appear in J. Phys.: Cond. Mat

A High Statistics Search for Electron-Neutrino --> Tau-Neutrino Oscillations

We present new limits on nu_e to nu_tau and nu_e to nu_sterile oscillations by searching for electron neutrino dissappearance in the high-energy wide-band CCFR neutrino beam. Sensitivity to nu_tau appearance comes from tau decay modes in which a large fraction of the energy deposited is electromagnetic. The beam is composed primarily of muon neutrinos but this analysis uses the 2.3% electron neutrino component of the beam. Electron neutrino energies range from 30 to 600 GeV and flight lengths vary from 0.9 to 1.4 km. This limit improves the sensitivity of existing limits and obtains a lowest 90% confidence upper limit in sin**2(2*alpha) of 9.9 x 10**(-2) at delta-m**2 of 125 eV**2.Comment: submitted to Phys. Rev. D. Rapid Com

Parton Distributions Working Group

The main focus of this working group was to investigate the different issues associated with the development of quantitative tools to estimate parton distribution functions uncertainties. In the conclusion, we introduce a "Manifesto" that describes an optimal method for reporting data.Comment: Report of the Parton Distributions Working Group of the 'QCD and Weak Boson Physics workshop in preparation for Run II at the Fermilab Tevatron'. Co-Conveners: L. de Barbaro, S.A. Keller, S. Kuhlmann, H. Schellman, and W.-K. Tun

Renaissance of the ~1 TeV Fixed-Target Program

This document describes the physics potential of a new fixed-target program based on a ~1 TeV proton source. Two proton sources are potentially available in the future: the existing Tevatron at Fermilab, which can provide 800 GeV protons for fixed-target physics, and a possible upgrade to the SPS at CERN, called SPS+, which would produce 1 TeV protons on target. In this paper we use an example Tevatron fixed-target program to illustrate the high discovery potential possible in the charm and neutrino sectors. We highlight examples which are either unique to the program or difficult to accomplish at other venues.Comment: 31 pages, 11 figure

New Measurements of Nucleon Structure Functions from CCFR/NuTeV

We report on the extraction of the structure functions F_2 and Delta xF_3 = xF_3nu-xF_3nub from CCFR neutrino-Fe and antineutrino-Fe differential cross sections. The extraction is performed in a physics model independent (PMI) way. This first measurement for Delta xF_3, which is useful in testing models of heavy charm production, is higher than current theoretical predictions. Within 5% the F_2 (PMI) values measured in neutrino and muon scattering are in agreement with the predictions of Next-to-Leading-Order PDFs (using massive charm production schemes), thus resolving the long-standing discrepancy between the two measurements.Comment: 3 pages, Presented by Arie Bodek at DPF2000 Conference, Columbus, Ohio, Aug. 200

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page