How to GAN : Novel simulation methods for the LHC

Various aspects of LHC simulations can be supplemented by generative networks. For event generation we show how a GAN can describe the full phase space structure of top-pair production including intermediate on-shell resonances and phase space bound- aries. In order to resolve these sharp peaking features, we introduce the maximum mean discrepancy. Additionally, the architecture can be extended in a straightforward manner to improve the network performance and to handle weighted events in the training data. Furthermore, we employ GANs to generate new events which are distributed according to the sum or difference of the input data. We first show with the help of a toy example how such a network can beat the statistical limitations of bin-wise subtraction methods. Afterwards we demonstrate how this network can subtract background events or describe collinear subtraction events in next-to-leading order calculations. Finally, we show how detector simulations can be inverted using GANs and INNs. They allow us to reconstruct parton level information from measured events. In detail, our results show how conditional generative networks can invert Monte Carlo simulations statistically. INNs even allow for a statistical interpretation of single-event unfolding and yield the possibility to unfold parton showering

Teachers\u27 Perceptions and Experiences in Implementing Mobile Devices Into Their Teaching

This phenomenological study addressed the lack of understanding of how teachers implement personal devices in the classroom and whether the instruction is constructivist in nature. Although mobile technology is convenient, it is not yet understood if Bring Your Own Device/Technology (BYOD/BYOT) programs encourage a teacher pedagogy shift. The purpose of this qualitative study was to explore the perceptions and lived experiences of 10 teachers in Grades 6 to 12 who had been part of a BYOD/BYOT program for more than 2 years. Data from interviews and lesson demonstrations were analyzed via a constructivist framework first identifying themes and then categories. Teachers perceived that using mobile technology provided the replacement of old tools, instructional planning changes, and the shifting of learning to the students from the traditional design of the teacher as the lecturer to the teacher as the facilitator. Teachers experienced more student engagement and collaboration although they needed to monitor students more carefully to avoid students\u27 being off task and to ensure safety usage of the mobile devices in the classroom. There are implications for social change both on the local and organizational level. Teachers can better understand how their pedagogy aligns with constructivist teaching and learning, and therefore can see where they still need to grow. On the organizational level, school districts may better understand that using technology at first will be used to replace previous pedagogy practices directly and that it will take support and time for technology implementation to impact changes in teachers\u27 philosophy of teaching

ELSA -- Enhanced latent spaces for improved collider simulations

Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W+jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement (LASER) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RAMBO-on-diet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.Comment: 17 pages, 9 figures, 2 tables, code and data at https://github.com/ramonpeter/els

Racial disparities in police use of deadly force against unarmed individuals persist after appropriately benchmarking shooting data on violent crime rates

Cesario et al. argue that benchmarking the relative counts of killings by police on relative crime rates, rather than relative population sizes, generates a measure of racial disparity in the use of lethal force that is unbiased by differential crime rates. Their publication, however, lacked any formal derivation showing that their benchmarking methodology has the statistical properties required to establish such a claim. We use the causal model of lethal force by police conditional on relative crime rates implicit in their analyses and prove that their benchmarking methodology does not, in general, remove the bias introduced by crime rate differences. Instead, it creates strong statistical biases that mask true racial disparities, especially in the killing of unarmed noncriminals by police. Reanalysis of their data using formally derived criminality-correcting benchmarks shows that there is strong and statistically reliable evidence of anti-Black racial disparities in the killing of unarmed Americans by police in 2015?2016