Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Absolute calibration of GafChromic film for very high flux laser driven ion beams.

We report on the calibration of GafChromic HD-v2 radiochromic film in the extremely high dose regime up to 100 kGy together with very high dose rates up to 7 × 1011 Gy/s. The absolute calibration was done with nanosecond ion bunches at the Neutralized Drift Compression Experiment II particle accelerator at Lawrence Berkeley National Laboratory (LBNL) and covers a broad dose dynamic range over three orders of magnitude. We then applied the resulting calibration curve to calibrate a laser driven ion experiment performed on the BELLA petawatt laser facility at LBNL. Here, we reconstructed the spatial and energy resolved distributions of the laser-accelerated proton beams. The resulting proton distribution is in fair agreement with the spectrum that was measured with a Thomson spectrometer in combination with a microchannel plate detector

Helium-3 and Helium-4 acceleration by high power laser pulses for hadron therapy

The laser driven acceleration of ions is considered a promising candidate for an ion source for hadron therapy of oncological diseases. Though proton and carbon ion sources are conventionally used for therapy, other light ions can also be utilized. Whereas carbon ions require 400 MeV per nucleon to reach the same penetration depth as 250 MeV protons, helium ions require only 250 MeV per nucleon, which is the lowest energy per nucleon among the light ions. This fact along with the larger biological damage to cancer cells achieved by helium ions, than that by protons, makes this species an interesting candidate for the laser driven ion source. Two mechanisms (Magnetic Vortex Acceleration and hole-boring Radiation Pressure Acceleration) of PW-class laser driven ion acceleration from liquid and gaseous helium targets are studied with the goal of producing 250 MeV per nucleon helium ion beams that meet the hadron therapy requirements. We show that He3 ions, having almost the same penetration depth as He4 with the same energy per nucleon, require less laser power to be accelerated to the required energy for the hadron therapy.Comment: 8 pages, 3 figures, 1 tabl

Enhancing proton acceleration by using composite targets

Efficient laser ion acceleration requires high laser intensities, which can only be obtained by tightly focusing laser radiation. In the radiation pressure acceleration regime, where the tightly focused laser driver leads to the appearance of the fundamental limit for the maximum attainable ion energy, this limit corresponds to the laser pulse group velocity as well as to another limit connected with the transverse expansion of the accelerated foil and consequent onset of the foil transparency. These limits can be relaxed by using composite targets, consisting of a thin foil followed by a near critical density slab. Such targets provide guiding of a laser pulse inside a self-generated channel and background electrons, being snowplowed by the pulse, compensate for the transverse expansion. The use of composite targets results in a significant increase in maximum ion energy, compared to a single foil target case.Comment: 16 pages, 9 figure

Radiation Pressure Acceleration: the factors limiting maximum attainable ion energy

Radiation pressure acceleration (RPA) is a highly efficient mechanism of laser-driven ion acceleration, with with near complete transfer of the laser energy to the ions in the relativistic regime. However, there is a fundamental limit on the maximum attainable ion energy, which is determined by the group velocity of the laser. The tightly focused laser pulses have group velocities smaller than the vacuum light speed, and, since they offer the high intensity needed for the RPA regime, it is plausible that group velocity effects would manifest themselves in the experiments involving tightly focused pulses and thin foils. However, in this case, finite spot size effects are important, and another limiting factor, the transverse expansion of the target, may dominate over the group velocity effect. As the laser pulse diffracts after passing the focus, the target expands accordingly due to the transverse intensity profile of the laser. Due to this expansion, the areal density of the target decreases, making it transparent for radiation and effectively terminating the acceleration. The off-normal incidence of the laser on the target, due either to the experimental setup, or to the deformation of the target, will also lead to establishing a limit on maximum ion energy.Comment: 17 pages, 6 figure

A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft