Conveners
General: Preamble and Intro to Statistics
- Jan Conrad
General: Intro to Workshop
- Hugh Lippincott (Fermilab)
General: Blind Analysis, Look Elsewhere
- Tina Pollmann (Technische Universität München)
General: Experience Outside Dark Matter, and Limits
- Jim Dobson
General: Limit Setting Methods
- Ben Loer (Pacific Northwest National Laboratory)
- Ben Loer
General: Signal and Backgrounds
- Olaf Behnke (DESY)
General: Signal and Backgrounds
- Maria Elena Monzani (SLAC National Accelerator Laboratory)
- maria monzani (Stanford University)
General: Backgrounds/Response Modeling
- Jelle Aalbers (Nikhef)
- Jelle Aalbers (Stockholm University)
General: Nuisance Parameters (Form Factors/Halo)
- Luca Grandi (The University of Chicago)
General: Machine Learning
- Hagar Landsmann
General: Summaries
- Jan Conrad
Description
Generic session block
These will review some simple statistical concepts that are relevant to this Workshop. Among other topics, it will include upper limits, p-values and likelihood ratios. It is intended for those who would like to be reminded of their Statistics, before the Workshop begins.
Direct detection experiments place some of the strongest constraints on models of dark matter and are therefore essential to include in global analyses of such models. In this talk I will discuss the specific requirements for and possible applications of likelihood functions from direct detection experiments. I will give a brief introduction into the public code DDCalc, which provides...
Direct detection experiments rely on a variety of bias mitigation strategies, most notably blinding and salting. I will review the main challenges and methods for blinding and salting, in preparation for the next generation of dark matter searches.
A variety of data blinding strategies have been adopted by different experiments searching for dark matter. I will present a brief overview of the relative strengths and weaknesses of some of the most common strategies, and describe the reasoning for and implementation of these methods by some leading dark matter experiments.
The look-elsewhere effect is a phenomenon which often arises when looking for signals whose location is not known in advance. In this setting, signal searches can be conducted by performing several tests of hypothesis at different positions over the search area considered. However, if the result of each individual test is not adequately adjusted for the fact that many tests are conducted...
The ATLAS and CMS collaborations have produced numerous results during the
first two data-taking runs of the LHC, ranging from precision measurements of SM processes to searches for exotic phenomena and the dicovery of the Higgs boson. All of these results make use of sophisticated statistical techniques, not only to provide statistical inferences from the data, but also during the...
In this talk, I will review the various methods, tools, tricks, and shortcuts used by various dark matter experiments to set upper limits on dark matter interaction cross-section. Focusing on the progression of the methods as the number of parameter increases and the background decreases I will discuss the big question of evolution vs. intelligent design (in the statistical inference realm J).
I will review some classical methods of asymptotic inference and their higher order extensions. The focus will be on modern likelihood based solutions, though Bayesian counterparts will be mentioned in by-passing. The discussion will touch upon topics such as small sample sizes, large number of nuisance parameters, nonregular settings and complex models.
We present a method for deriving signal significance p-values in the 5 sigma region for finite samples, to order O(n^{-3/2}), for a number of signal detection statistics (Wald, score, and likelihood ratio). Connection with the look elsewhere effect is discussed.
The talk is based in part on the article by I. Volobouev and A. Trindade 2018 JINST 13 P12011
When searching for new astrophysical phenomena, uncertainty arising from background mismodelling can dramatically compromise the sensitivity of the experiment under study. Specifically, overestimating the background distribution in the signal region increases the chances of missing new physics. Conversely, underestimating the background outside the signal region leads to an artificially...
I will speak about two aspects of partially specified models. The first of these arise from modelling explicitly only aspects of direct concern, retaining a degree of agnosticism over other aspects of the data generating process. I will illustrate with examples how this leads to a large number of nuisance parameters, and how these can be evaded in some situations. The second type of partially...
In order for an experiment to be able to claim discovery of a signal, it must first master its backgrounds and understand how a signal will manifest in the detector. The range of energies involved in the interactions tend to be low due to kinematics so much of an experiment's sensitivity is driven by its threshold. In this region, modeling the detector response can be difficult but I will...
We have entered the era of single electron-hole pair sensitive crystal detectors with a threshold as low as the indirect band gap. These detectors are excellent devices to search for light dark sector particles with masses well below the threshold of to date typical direct Dark Matter search detectors. But with new opportunities come new challenges. New sources of background govern the...
In many types of scintillator-based dark matter experiments, pulse shape discrimination (PSD) is used to mitigate backgrounds. The leakage probability of events mitigated through PSD into the region of interest (ROI) is an important parameter used to define the ROI and to inform the ROI background model. Determining the leakage probability requires an understanding of the distribution of...
The statistical fluctuation of the number of e-/ion pairs produced in an ionizing interaction is known to be sub-Poissonian, the dispersion being reduced by the so-called “Fano Factor”. Due to a lack of appropriate modelling tools, this phenomenon is often folded into the overall energy response of ionization detectors. While this may be adequate down to relatively low-energies, this treatment...
I will give an overview of the nuclear physics input required for the interpretation of direct detection searches for dark matter, concentrating on approaches based on chiral effective field theory.
In particular, I will discuss the sources of uncertainty in the calculation of the nuclear responses, to trigger the discussion if and how to include these uncertainties in statistical analyses.
The Gaia satellite is transforming our understanding of the distribution of dark matter in the Milky Way. I’ll discuss two structures that have recently been detected in the Milky Way, the S1 stellar stream and the Gaia Sausage, and their impact on experiments searching for the direct detection of dark matter.
Dark Machines is a research collective of about 200 physicists and data scientists, who use state-of-the-art machine learning techniques to solve dark matter related problems. These problems are typically organised as challenges: physicists provide datasets and the machine learning experts try to solve the problem in the best possible way. A few of the current focuses are particle track...
