TY  - JOUR
AU  - Khosa, C. K.
AU  - Sanz, V.
PY  - 2023
DA  - 2023//
TI  - Anomaly Awareness
T2  - SciPost Phys.
JO  - Scipost Physics
SP  - 053
EP  - 24pp
VL  - 15
IS  - 2
PB  - Scipost Foundation
AB  - We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
SN  - 2542-4653
UR  - https://arxiv.org/abs/2007.14462
UR  - https://doi.org/10.21468/SciPostPhys.15.2.053
DO  - 10.21468/SciPostPhys.15.2.053
LA  - English
N1  - WOS:001048488200002
ID  - Khosa+Sanz2023
ER  -