1,313 research outputs found
Physics of An Ultrahigh-Statistics Charm Experiment
We review the physics goals of an ultrahigh-statistics charm experiment and
place them in the broader context of the community's efforts to study the
Standard Model and to search for physics beyond the Standard Model, and we
point out some of the experimental difficulties which must be overcome if these
goals are to be met.Comment: 9 pages, no figure
Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices
Using deep neural networks to identify and locate proton-proton collision
points, or primary vertices, in LHCb has been studied for several years.
Preliminary results demonstrated the ability for a hybrid deep learning
algorithm to achieve similar or better physics performances compared to
standard heuristic approaches. The previously studied architectures relied
directly on hand-calculated Kernel Density Estimators (KDEs) as input features.
Calculating these KDEs was slow, making use of the DNN inference engines in the
experiment's real-time analysis (trigger) system problematic. Here we present
recent results from a high-performance hybrid deep learning algorithm that uses
track parameters as input features rather than KDEs, opening the path to
deployment in the real-time trigger system.Comment: Proceedings for the ACAT 2022 conferenc
Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
We are studying the use of deep neural networks (DNNs) to identify and locate
primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work
focused on finding primary vertices in simulated LHCb data using a hybrid
approach that started with kernel density estimators (KDEs) derived
heuristically from the ensemble of charged track parameters and predicted
"target histogram" proxies, from which the actual PV positions are extracted.
We have recently demonstrated that using a UNet architecture performs
indistinguishably from a "flat" convolutional neural network model. We have
developed an "end-to-end" tracks-to-hist DNN that predicts target histograms
directly from track parameters using simulated LHCb data that provides better
performance (a lower false positive rate for the same high efficiency) than the
best KDE-to-hists model studied. This DNN also provides better efficiency than
the default heuristic algorithm for the same low false positive rate.
"Quantization" of this model, using FP16 rather than FP32 arithmetic, degrades
its performance minimally. Reducing the number of UNet channels degrades
performance more substantially. We have demonstrated that the KDE-to-hists
algorithm developed for LHCb data can be adapted to ATLAS and ACTS data using
two variations of the UNet architecture. Within ATLAS/ACTS, these algorithms
have been validated against the standard vertex finder algorithm. Both
variations produce PV-finding efficiencies similar to that of the standard
algorithm and vertex-vertex separation resolutions that are significantly
better
Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
The locations of proton-proton collision points in LHC experiments are called
primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm
for identifying and locating these, targeting the Run 3 incarnation of LHCb,
have been described at conferences in 2019 and 2020. In the past year we have
made significant progress in a variety of related areas. Using two newer Kernel
Density Estimators (KDEs) as input feature sets improves the fidelity of the
models, as does using full LHCb simulation rather than the "toy Monte Carlo"
originally (and still) used to develop models. We have also built a deep
learning model to calculate the KDEs from track information. Connecting a
tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a
proof-of-concept that a single deep learning model can use track information to
find PVs with high efficiency and high fidelity. We have studied a variety of
models systematically to understand how variations in their architectures
affect performance. While the studies reported here are specific to the LHCb
geometry and operating conditions, the results suggest that the same approach
could be used by the ATLAS and CMS experiments
Hnrnph1 Is A Quantitative Trait Gene for Methamphetamine Sensitivity.
Psychostimulant addiction is a heritable substance use disorder; however its genetic basis is almost entirely unknown. Quantitative trait locus (QTL) mapping in mice offers a complementary approach to human genome-wide association studies and can facilitate environment control, statistical power, novel gene discovery, and neurobiological mechanisms. We used interval-specific congenic mouse lines carrying various segments of chromosome 11 from the DBA/2J strain on an isogenic C57BL/6J background to positionally clone a 206 kb QTL (50,185,512-50,391,845 bp) that was causally associated with a reduction in the locomotor stimulant response to methamphetamine (2 mg/kg, i.p.; DBA/2J < C57BL/6J)-a non-contingent, drug-induced behavior that is associated with stimulation of the dopaminergic reward circuitry. This chromosomal region contained only two protein coding genes-heterogeneous nuclear ribonucleoprotein, H1 (Hnrnph1) and RUN and FYVE domain-containing 1 (Rufy1). Transcriptome analysis via mRNA sequencing in the striatum implicated a neurobiological mechanism involving a reduction in mesolimbic innervation and striatal neurotransmission. For instance, Nr4a2 (nuclear receptor subfamily 4, group A, member 2), a transcription factor crucial for midbrain dopaminergic neuron development, exhibited a 2.1-fold decrease in expression (DBA/2J < C57BL/6J; p 4.2 x 10-15). Transcription activator-like effector nucleases (TALENs)-mediated introduction of frameshift deletions in the first coding exon of Hnrnph1, but not Rufy1, recapitulated the reduced methamphetamine behavioral response, thus identifying Hnrnph1 as a quantitative trait gene for methamphetamine sensitivity. These results define a novel contribution of Hnrnph1 to neurobehavioral dysfunction associated with dopaminergic neurotransmission. These findings could have implications for understanding the genetic basis of methamphetamine addiction in humans and the development of novel therapeutics for prevention and treatment of substance abuse and possibly other psychiatric disorders
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