@misc{nokey,

title = {End-To-End Optimization of the Layout of a Gamma Ray Observatory},

author = {T. Dorigo, M. Aehle, J. Donini, M. Doro, N. R. Gauger, R. Izbicki, A. Lee, L. Masserano, F. Nardi, Sidharth S S, A. Shen},

url = {https://arxiv.org/abs/2310.01857},

year  = {2023},

date = {2023-10-03},

urldate = {2023-10-03},

howpublished = { arXiv:2310.01857},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming},

author = {M. Aehle, L. Arsini, R. Belén Barreiro, A. Belias, F. Bury, S. Cebrian, A. Demin, J. Dickinson, J. Donini, T. Dorigo, M. Doro, N. R. Gauger, A. Giammanco, L. Gray, B. S. González, V. Kain, J. Kieseler, L. Kusch, et al.},

url = {https://arxiv.org/abs/2310.05673},

year  = {2023},

date = {2023-09-30},

howpublished = {arXiv:2310.05673},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{sagebaum2023integrating,

title = {Integrating Enzyme-generated functions into CoDiPack},

author = {M. Sagebaum and M. Aehle and N. R. Gauger},

url = {https://arxiv.org/abs/2307.06075},

year  = {2023},

date = {2023-07-13},

urldate = {2023-07-13},

abstract = {In operator overloading algorithmic differentiation, it can be beneficial to create custom derivative functions for some parts of the code base. For manual implementations of the derivative functions, it can be quite cumbersome to derive, implement, test, and maintain these. The process can be automated with source transformation algorithmic differentiation tools like Tapenade or compiler-based algorithmic differentiation tools like Enzyme. This eliminates most of the work required from a manual implementation but usually has the same efficiency with respect to timing and memory. We present a new helper in CoDiPack that allows Enzyme-generated derivative functions to be automatically added during the recording process of CoDiPack. The validity of the approach is demonstrated on a synthetic benchmark, which shows promising results.},

howpublished = { arXiv:2307.06075},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization},

author = {J. Rottmayer, E. Özkaya, S. Satcunanathan, B. Y. Zhou, M. Aehle, N. R. Gauger, M. Meinke, W. Schröder, S. Pullin

},

url = {https://arxiv.org/abs/2301.13047},

year  = {2023},

date = {2023-01-30},

urldate = {2023-01-30},

howpublished = {arXiv:2301.13047},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Reverse-Mode Automatic Differentiation of Compiled Programs},

author = {M. Aehle, J. Blühdorn, M. Sagebaum, N.R. Gauger},

url = {https://arxiv.org/pdf/2212.13760.pdf},

year  = {2022},

date = {2022-12-28},

urldate = {2022-12-28},

howpublished = {arXiv:2212.13760},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Exploiting Differentiable Programming for the End-to-end Optimization of Detectors},

author = {M. Aehle, M. Bawaj, A. Belias, A. Boldyrev, P. de Castro Manzano, C. Delaere, D. Derkach, J. Donini, T. Dorigo, A. Edelen, P. Elmer, F. Fanzago, N. R. Gauger, et al.},

url = {https://indico.ph.tum.de/event/7050/contributions/6355/attachments/4455/5678/submission_nupecc.pdf},

year  = {2022},

date = {2022-10-25},

urldate = {2022-10-25},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Forward-Mode Automatic Differentiation of Compiled Programs},

author = {M. Aehle, J. Blühdorn, M. Sagebaum, N. R. Gauger},

url = {https://arxiv.org/pdf/2209.01895},

year  = {2022},

date = {2022-09-05},

urldate = {2022-09-05},

abstract = {Algorithmic differentiation (AD) is a set of techniques to obtain accurate derivatives of a computer-implemented function in an automatic fashion. State-of-the-art AD tools rely on the source code of the implementation or internal representations of compilers building it.

We present the new AD tool Derivgrind, which augments the machine code of compiled programs with forward AD logic. Derivgrind leverages the Valgrind instrumentation framework for a structured access to the machine code, and a shadow memory tool to store dot values. Depending on the application scenario, no access to the source code is required at all, or the access is restricted to the parts defining input and output variables.

Derivgrind's versatility comes at the price of scaling the running time by a factor between 60 and 140, measured on a benchmark based on a PDE solver. Results of our extensive test suite indicate that Derivgrind produces correct results on GCC- and Clang-compiled programs, including a Python interpreter, with a small number of exceptions. While we provide a list of scenarios that Derivgrind does not handle correctly, most of them are academic examples or originate from highly optimized math libraries. We will therefore further study the potential of our tool in more complex software projects. },

howpublished = {arXiv:2209.01895},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper},

author = {T. Dorigo, A. Giammanco, P. Vischia, M. Aehle, M. Bawaj, A. Boldyrev, P. de Castro Manzano, D. Derkach, J. Donini, A. Edelen, F. Fanzago, N. R. Gauger, et al.},

url = {https://arxiv.org/pdf/2203.13818},

doi = {https://doi.org/10.48550/arXiv.2203.13818},

year  = {2022},

date = {2022-03-22},

urldate = {2022-03-22},

abstract = {The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.

In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. },

howpublished = {arXiv:2203.13818},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{SIVERT2022,

title = {Derivatives in Proton CT},

author = {M. Aehle, J. Alme, G. Barnaföldi, J. Blühdorn, T. Bodova, V. Borshchov, A. van den Brink, M. Chaar, V. Eikeland, G. Feofilov, C. Garth, N.R. Gauger, et al.},

url = {https://arxiv.org/pdf/2202.05551v1.pdf},

year  = {2022},

date = {2022-02-14},

urldate = {2022-02-14},

abstract = {Algorithmic derivatives can be useful to quantify uncertainties and optimize parameters using computer simulations. Whether they actually are, depends on how "well-linearizable" the program is. Proton computed tomography (pCT) is a medical imaging technology with the potential to increase the spatial accuracy of the dose delivered in proton-beam radiotherapy. The Bergen pCT collaboration is developing and constructing a digital tracking calorimeter (DTC) to measure the position, direction and energy of protons after they passed through a patient, and a software pipeline to process these data into a pCT image. We revisit the software pipeline from the perspective of algorithmic differentiation (AD). In the early subprocedures, several obstacles such as discrete variables or frequent discontinuities were identified, and are probably tackled best by using surrogate models. The model-based iterative reconstruction (MBIR) subprocedure in the end seems to be AD-ready, and we propose changes in the AD workflow that can reduce the memory consumption in reverse mode.},

howpublished = {arXiv: 2202.05551v1},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}