[{"id":285,"title":"Create standalone simulation tools to facilitate collaboration between HEP and machine learning community","sha":"ff0489d51bdb17359cef823c1d6b7029","user_id":null,"state":"published","body":"Discussions at recent workshops have made it clear that one of the key barriers to collaboration between high energy physics and the machine learning community is access to training data. Recent successes in data sharing through the [HiggsML](http://doi.org/10.7483/OPENDATA.ATLAS.ZBP2.M5T8) and [Flavours of Physics](https://www.kaggle.com/c/flavours-of-physics/data) Kaggle challenges have borne much fruit, but required significant effort to coordinate.\r\n\r\nWhile static simulated datasets are useful for challenges, in the course of investigating new machine learning techniques it is advantageous to be able to generate training data on demand (e.g. Refs. [1](https://archive.ics.uci.edu/ml/datasets/HEPMASS), [2](https://archive.ics.uci.edu/ml/datasets/SUSY), [3](https://archive.ics.uci.edu/ml/datasets/HIGGS) ). \r\nTherefore we recommend efforts be made to produce the ingredients required to facilitate such collaboration:\r\n   * Specific challenges for HEP experiments should be fully specified such that minimal domain-specific knowledge is required to attack them.\r\n   * Stand-alone simulators should be made open source. They should be developed to be easy to use without domain-specific expertise, while still being representative of real experimental challenges. Such a simulation will permit non-HEP researchers to generate realistic HEP datasets for training and testing. These simulators could range from truth-level simulation of a hard scattering to fast simulation like [Delphes](http://dx.doi.org/10.1007/JHEP02(2014)057), to full [GEANT4](http://dx.doi.org/10.1016/S0168-9002(03)01368-8) simulation of sensor arrays.\r\n   * Performance metrics (objective functions) and operational constraints should be defined to evaluate proposed solutions.\r\n","created_at":"2016-02-26T21:34:14.721Z","updated_at":"2016-04-03T17:04:01.541Z","zenodo_id":99761,"doi":"http://dx.doi.org/10.5281/zenodo.46864","subject":null,"tags":["dslhc","machinelearning","datascience","open data","simulation"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":412,"title":"Recasting through reweighting","sha":"8106c030eba22dd3a8d268940d5e42d8","user_id":null,"state":"published","body":"*Recasting* refers to reinterpreting the results of searches for new particles or standard model measurements in the context of different theoretical models\u0026nbsp;\\[[1](http://inspirehep.net/record/872781)\\]. The fundamental task is to replace the original hypothesis $p_0(x)$ with a new hypothesis $p_1(x)$, where $x$ is some observed quantity. The effect of the detector response and analysis cuts can be encoded in a *folding* operator $\\int\\,W(x|z)dz$ acting on the truth-level distribution $p(z)$. By keeping the  analysis fixed, $W(x|z)$ does not change, thus recasting amounts to:\r\n\\begin{equation}p_0(x)=\\int\\,p_0(z)W(x|z)dz\\hspace{.5cm}\\Longrightarrow\\hspace{.5cm}p_1(x)=\\int\\,p_1(z)W(x|z)dz\\end{equation}\r\n\r\nThere are two primary approaches:\r\n  * **folding**: Samples from $p_1(z)$ are run through a detector simulation and analysis chain to estimate $p_1(x)$\u0026nbsp;\\[[2](https://indico.cern.ch/event/567550/contributions/2638695/)\\]. This is common when $z$ is high-dimensional, $p_0(z)$ and $p_1(z)$ are very different, or $W(x|z)$ is sensitive to experimental details.\r\n  * **unfolding**: An alternate theory $p_1(z)$ is compared directly to an unfolded distribution $\\hat{p}(z)$ obtained from applying an approximate inverse operation to the observed data. Typically, unfolding is restricted to low-dimensional $x, z$ and Gaussian uncertainties.\r\n\r\nWe point out a third option\r\n   * **reweighting**: Reweight pre-folded events $(x_i,z_i)~\\sim\\,p_0(x,z)$ by the factor $r(z_i)=p_1(z_i)/p_0(z_i)$, as in\r\n\\begin{equation}p_1(x)=\\int p_1(z)W(x|z)dz=\\int\\,p_0(z)\\underbrace{\\frac{p_1(z)}{p_0(z)}}_{\\textrm{reweighting}}W(x|z)dz\\end{equation}\r\nThis approach does not require simulating new events or the approximations used in unfolding. Note, sample variance becomes a problem if $r(z_i)\\gg\\,1$.","created_at":"2017-10-14T14:16:18.901Z","updated_at":"2022-06-05T20:51:32.689Z","zenodo_id":1013926,"doi":"https://doi.org/10.5281/zenodo.1013926","subject":null,"tags":["physics","reinterpretation","lhc"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]