28 research outputs found
Mechanical loss of laser-welded fused silica fibers
No full textThe mechanical quality factor of a carbon dioxide laser-welded fiber was measured and compared to flame-welded fibers to determine the suitability of laser welding for attaching suspension fibers to test masses in precision experiments. The loss in the fiber was found to be limited primarily by thermoelastic damping and surface loss, rather than loss from the weld. This technique is attractive for the attachment of fused silica suspensions where low thermal noise and precision location of the weld are considered.Gregory Harry, Thomas Corbitt, Marat Freytsis, David Ottaway, and Nergis Mavalval
Training data for neural posterior estimation for pulsar timing arrays
No full text<p>Training data used in https://arxiv.org/abs/2310.12209</p>
<pre>D. Shih, M. Freytsis, S. R. Taylor, J. A. Dror and N. Smyth,
"Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows,"
[arXiv:2310.12209 [astro-ph.IM]].</pre>
Training data for neural posterior estimation for pulsar timing arrays
No full text<p>Training data used in https://arxiv.org/abs/2310.12209</p>
<pre>D. Shih, M. Freytsis, S. R. Taylor, J. A. Dror and N. Smyth,
"Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows,"
[arXiv:2310.12209 [astro-ph.IM]].</pre>
Noise Injection Node Regularization for Robust Learning
No full textWe introduce Noise Injection Node Regularization (NINR), a method of
injecting structured noise into Deep Neural Networks (DNN) during the training
stage, resulting in an emergent regularizing effect. We present theoretical and
empirical evidence for substantial improvement in robustness against various
test data perturbations for feed-forward DNNs when trained under NINR. The
novelty in our approach comes from the interplay of adaptive noise injection
and initialization conditions such that noise is the dominant driver of
dynamics at the start of training. As it simply requires the addition of
external nodes without altering the existing network structure or optimization
algorithms, this method can be easily incorporated into many standard problem
specifications. We find improved stability against a number of data
perturbations, including domain shifts, with the most dramatic improvement
obtained for unstructured noise, where our technique outperforms other existing
methods such as Dropout or regularization, in some cases. We further show
that desirable generalization properties on clean data are generally
maintained.Comment: 16 pages, 9 figure
The importance of calorimetry for highly-boosted jet substructure
No full text© 2018 IOP Publishing Ltd and Sissa Medialab. Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstrate physics contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required
A new basis for Hamiltonian SU(2) simulations
No full textDue to rapidly improving quantum computing hardware, Hamiltonian simulations
of relativistic lattice field theories have seen a resurgence of attention.
This computational tool requires turning the formally infinite-dimensional
Hilbert space of the full theory into a finite-dimensional one. For gauge
theories, a widely-used basis for the Hilbert space relies on the
representations induced by the underlying gauge group, with a truncation that
keeps only a set of the lowest dimensional representations. This works well at
large bare gauge coupling, but becomes less efficient at small coupling, which
is required for the continuum limit of the lattice theory. In this work, we
develop a new basis suitable for the simulation of an SU(2) lattice gauge
theory in the maximal tree gauge. In particular, we show how to perform a
Hamiltonian truncation so that the eigenvalues of both the magnetic and
electric gauge-fixed Hamiltonian are mostly preserved, which allows for this
basis to be used at all values of the coupling. Little prior knowledge is
assumed, so this may also be used as an introduction to the subject of
Hamiltonian formulations of lattice gauge theories.Comment: 27 pages, 11 figure
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Readout of TPC Tracking Chambers with GEMs and Pixel Chip
No full textTwo layers of GEMs and the ATLAS Pixel Chip, FEI3, have been combined and tested as a prototype for Time Projection Chamber (TPC) readout at the International Linear Collider (ILC). The double-layer GEM system amplifies charge with gain sufficient to detect all track ionization. The suitability of three gas mixtures for this application was investigated, and gain measurements are presented. A large sample of cosmic ray tracks was reconstructed in 3D by using the simultaneous timing and 2D spatial information from the pixel chip. The chip provides pixel charge measurement as well as timing. These results demonstrate that a double GEM and pixel combination, with a suitably modified pixel ASIC, could meet the stringent readout requirements of the ILC
Cataloging accreted stars within
No full textAims. The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2.
Methods. A method known as “transfer learning” is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIR
