Michael Andrews completed his Ph.D. in Physics at Carnegie Mellon University where he was involved with the CMS collaboration at the Large Hadron Collider at CERN. He worked at CERN in Geneva, Switzerland, from 2015 to 2019 where he served as Run Coordinator for the CMS electromagnetic calorimeter group. For his distinguished service to CMS detector operations, he received the CMS Achievement Award in 2018.Michael’s physics research focuses on the application advanced deep learning techniques to problems in LHC physics. He played a leading role in the development of deep learning algorithms trained directly on low-level detector data, so-called end-to-end physics reconstruction. His work on end-to-end physics reconstruction led to the first CMS results demonstrating the breakthrough potential of this technique over traditional methods for the reconstruction of boosted decays to highly merged photons. For his contributions, summarized in his Ph.D. thesis, he was awardedthe CMS Ph.D. Thesis Award in 2021.

• Introduction.- The LHC and the CMS detector.- Theory & phenomenology.- Analysis strategy.- Data sets.- Signal selection.- a mass regression.- Analysis.- Results.- Conclusions.- Supplementary studies.

Michael Andrews completed his Ph.D. in Physics at Carnegie Mellon University where he was involved with the CMS collaboration at the Large Hadron Collider at CERN. He worked at CERN in Geneva, Switzerland, from 2015 to 2019 where he served as Run Coordinator for the CMS electromagnetic calorimeter group. For his distinguished service to CMS detector operations, he received the CMS Achievement Award in 2018.Michael’s physics research focuses on the application advanced deep learning techniques to problems in LHC physics. He played a leading role in the development of deep learning algorithms trained directly on low-level detector data, so-called end-to-end physics reconstruction. His work on end-to-end physics reconstruction led to the first CMS results demonstrating the breakthrough potential of this technique over traditional methods for the reconstruction of boosted decays to highly merged photons. For his contributions, summarized in his Ph.D. thesis, he was awardedthe CMS Ph.D. Thesis Award in 2021.

• Introduction.- The LHC and the CMS detector.- Theory & phenomenology.- Analysis strategy.- Data sets.- Signal selection.- a mass regression.- Analysis.- Results.- Conclusions.- Supplementary studies.

Michael Andrews completed his Ph.D. in Physics at Carnegie Mellon University where he was involved with the CMS collaboration at the Large Hadron Collider at CERN. He worked at CERN in Geneva, Switzerland, from 2015 to 2019 where he served as Run Coordinator for the CMS electromagnetic calorimeter group. For his distinguished service to CMS detector operations, he received the CMS Achievement Award in 2018.Michael’s physics research focuses on the application advanced deep learning techniques to problems in LHC physics. He played a leading role in the development of deep learning algorithms trained directly on low-level detector data, so-called end-to-end physics reconstruction. His work on end-to-end physics reconstruction led to the first CMS results demonstrating the breakthrough potential of this technique over traditional methods for the reconstruction of boosted decays to highly merged photons. For his contributions, summarized in his Ph.D. thesis, he was awardedthe CMS Ph.D. Thesis Award in 2021.

• Introduction.- The LHC and the CMS detector.- Theory & phenomenology.- Analysis strategy.- Data sets.- Signal selection.- a mass regression.- Analysis.- Results.- Conclusions.- Supplementary studies.