42 citations · 49 across the 10 of their papers we have counts for
11 papers
Sequential SBI for Resonance Searches at the LHC
Henning Bahl, Ranit Das
Neural simulation-based inference has been shown to extract significantly more information from LHC data than histogram-based analyses. So far, it has mostly been applied to coupli…
NAE, Statistically
Ranit Das, Jonathan Ostertag-Henning, Tilman Plehn +1
Searches for new physics using neural anomaly scores have transformative potential, but suffer from a lack of statistical interpretability. The normalized autoencoder (NAE) provide…
Kitchen Sink Anomaly Detection
Ranit Das, Marie Hein, Gregor Kasieczka +6
An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have…
SURFing to the Fundamental Limit of Jet Tagging
Ian Pang, Darius A. Faroughy, David Shih +2
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…
Accurate and robust methods for direct background estimation in resonant anomaly detection
Ranit Das, Thorben Finke, Marie Hein +4
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality backgrou…
SIGMA: Single Interpolated Generative Model for Anomalies
Ranit Das, David Shih
A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by t…
Residual ANODE
Ranit Das, Gregor Kasieczka, David Shih
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability. The key to R-ANODE is to…
How to Understand Limitations of Generative Networks
Ranit Das, Luigi Favaro, Theo Heimel +3
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustr…
Feature Selection with Distance Correlation
Ranit Das, Gregor Kasieczka, David Shih
Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine le…
Quantum violation of macrorealism under multi-outcome two-parameter generalised measurements
Debarshi Das, Arindam Gayen, Ranit Das +1
Generalised dichotomic quantum measurements are fully characterised by two real parameters, dubbed as sharpness parameter and biasedness parameter. The trade-off between the degree…