Inverse thermal history modeling is an effective tool to explore plausible time-temperature (t-T) histories that can be used to describe the geologic history of a sample. Although in some inverse modeling exercises the input thermochronology data is consistent with a single set of t-T histories with similar heating and cooling trends, more commonly inverse models identify a range of paths with different and distinct heating and cooling histories but similarly good fits to the data. Each set of these “path families” typically requires a different geologic interpretation to explain the observed heating and cooling trend, so it is important to identify and consider all possible path families consistent with the regional geology that fit the modelled dataset before selecting a preferred geologic interpretation. Although the inverse model results are always consistent with measured data, a model’s ability to detect all possible path families is partly controlled by the model design – for example the choice of initial conditions, monotonicity settings, and forced time-temperature windows. In this exercise using the thermal history modeling program HeFTy, we illustrate the effects of model design on the inverse model results of a set of multi-chronometer datasets from the southern Patagonian Andes. We use model design to maximize the number of path families identified through inverse modeling. Once individual paths are classified according to path families, we use independently constrained regional geology to discriminate among the diverse plausible set of path families and evaluate different available geologic scenarios. Our exercise illustrates that models restricting exploration of all path families may not identify the true cooling history of the sample. Initially, it may appear challenging to interpret inverse model results that include multiple path families, but we argue that iterating between independent geologic data and modeling provides an effective tool to test the geologic plausibility of alternative heating and cooling histories. Although this exercise is executed using HeFTy, maximizing the identification of all possible path families should be an important component of model design in inverse modeling exercises using all inverse modeling programs.

The 17th International Conference on Thermochronology (Thermo2021) was held in Santa Fe, New Mexico, on September 12-17, 2021. This bi-annual conference series evolved via the coalescence of the International Workshops on Fission Track Thermochronology, held since 1978, and the European Workshops on Thermochronology. It has become the premier forum for thermochronology practitioners and users to discuss fundamental and methodological topics and opportunities related to their science and its future. Each conference is independently organized, and a Standing Committee consisting of past organizers and other community members helps to ensure their continuation into the future. Thermo2021 was greatly affected by the COVID-19 pandemic. Normally the meeting would have been expected to draw ~250 attendees, but travel restrictions limited in-person attendance to 86, plus 21 remote presenters. Nearly all in-person participants were from the US, and only four were international. Talks and posters were distributed among five themes: (U-Th)/He; fission track; other thermochronometers; frontiers in data handling, statistics, interpretation methods, and modeling; and integration and interpretation. Although COVID-19 presented many challenges, it also allowed the Organizing Committee to adapt creatively and transform adversity into opportunity. In particular, the smaller number of attendees permitted more talks by students and early-career scientists, both within the theme sessions and in the Charles & Nancy Naeser Early Career Session. Discussion time was prioritized: at a Tuesday evening “swap meet” for ideas, in 30-40-minute time slots within each theme session, and in Friday afternoon breakouts for the first four themes and another dedicated to early career and DEI issues. These were used to identify emergent ideas and concerns across a broad range of topics, from the theory and practice of the various thermochronometric techniques, to their interpretation through thermal history modeling and other methods, to anticipated trends in data dissemination and management, to the needs of the next generation of thermochronologists, particularly in the US. Each Friday breakout designated a scribe who recorded the discussion and distributed their notes. Each group then designated one or more writers to transform the notes into text for this White Paper. Notes or early write-up versions were provided to the international thermochronology community, and feedback solicited. In addition, cross-cutting themes that occurred across multiple breakout groups were identified and compiled. This White Paper is the outcome of these efforts. We hope that it will serve as a record for the meeting, and an overview of where the predominantly US-based component of the thermochronology community considers the current state of knowledge to be and where future efforts should be directed, for developing both the science and its human infrastructure.

Thermal history models are interpretive tools that incorporate data from chronometers and implement the published kinetics in the context of independent constraints on a sample’s known geologic history, in order to explore specific gaps in geologic knowledge. Despite their central role in the interpretation of thermochronologic datasets, our community has no standards for what characterizes a “robust” thermal history model result, how a model result’s rigor can and should be demonstrated, and how to communicate the key layers of interpretation produce a preferred thermal (and geologic) history. As a result, and through no fault of any one study or modeling program, published models are a patchwork of modeling philosophies, assumptions, and auxiliary hypotheses that are rarely sufficiently explored—to the frustration of authors, reviewers, and readers. This patchwork can give rise to conflicting conclusions and generate apparent controversies that distract from the geologic questions at hand. Therefore, our community needs to both embrace a diversity of modeling approaches and collectively discuss and set broad expectations for what constitutes thermal history modeling best practices. Here, we argue that the fundamental characteristic of any robust thermal history model result is that it is accompanied by a clear articulation of the “why”—e.g., the reason(s) that a model produces a distinctive history, be it the power of a geologic constraint, a grain’s age, a spatial relationship between samples, the choice of kinetic model, etc. We demonstrate this approach using (U-Th)/He data from basement rocks in the Front Range, CO, which when modeled require a distinctive Neoproterozoic thermal history: heating to 235-280°C after ca. 650 Ma and then cooling to <60°C during Paleozoic time. We demonstrate “why” through a suite of models that add, modify, and remove geologic constraints and data from the preferred model. We find that a heating event is required to produce the observed zircon He age-[eU] trend because (1) there is no more than ~600 My of radiation damage accumulated in the zircon crystals, (2) the geologic record places the samples at the surface prior to 650 Ma, and (3) published Ar ages require that these rocks were colder than ~250˚C for most of the last 1.5 Gy. By identifying these key factors, our sensitivity test facilitates comparisons to other studies and directs further discussion to how confident we are in the parts of our data and model set-up that produce this distinctive result. More broadly, this exercise demonstrates one of the challenges of deep-time thermochronology: the potential to accumulate multiple auxiliary assumptions that control the model result in ways that are not obvious, even to the experienced model user, without deliberate exploration of alternative solutions—further underscoring the need for more open discussion of this topic.