Apatite fission track and apatite (helium) data from the Antarctic Peninsula, 2025

The files include full analytical details and datasets from University College London Geochronology Centre used for the acquisition of apatite fission track and apatite He data. 28 samples were analysed from a range of different lithologies to examine the uplift history of the Antarctic Peninsula since the Cretaceous. This is in order to understand the processes that led to areas of high elevation that will help interpret ice sheet nucleation. The samples were selected across three separate transects (1: northern Alexander Island to northwest Palmer Land; 2: Adelaide Island to Joerg Peninsula; 3: Livingston Island to James Ross Island) from the northern and central Antarctic Peninsula with 8-10 samples chosen along east-west transects from a range of elevations. The data were collected in the interval November 2023 to March 2024 at the University College London. The analyses were conducted by Gary Twinn and Andrew Carter (University College London). Funding was provided by NERC NC-ALI funding to PICC.

Samples were taken from the British Antarctic Survey rock archive spanning expeditions between 1953 and 2007 and included sandstones, gabbros and granitoids. Apatite thermochronometry data are sensitive to long-term shifts in crustal temperatures affecting the top three kilometres of the Earth's crust and are ideal to reconstruct cooling and exhumation histories of a region (Malusa & Fitzgerald, 2019; Ehlers & Farley, 2003, Gautheron et al., 2022). Both apatite fission track (AFT) and apatite (U-Th-Sm)/He (AHe) methods were used in this study, the latter providing optimum sensitivity to rock cooling below c. 50-90 degrees C (4He partial retention zone). Thermochronometry analyses were performed at the London Geochronology Centre. AFT analyses used both neutron activation zeta based external detector (Hurford and Green, 1983) and laser-ablation inductively coupled plasma mass spectrometry (LA-ICPMS) methods (Donelick et al., 2005; Hasebe et al., 2004). The number of apatite (U-Th-Sm)/He analyses per sample varied according to apatite quality and abundances. To minimise the age dispersion within a sample, similar sized whole grains were placed in Pt tubes and heated by a 808 nm diode laser to between 900-1000 degrees C for five minutes to degas the crystal for 4He measurement on a Pfeiffer Prisma 100 quadrupole mass spectrometer. Analytical errors on gas measurements are typically between 0.01 and 0.02% ncc. Dissolved apatite U-Th-Sm spiked solutions were measured on an Agilent 7900x ICP-MS and associated errors for U are < 1% and < 1.5% for Th. To check session (U-Th)/ He reproducibility Durango standards were analysed approximately every ten unknown samples. At the time of analyses the reproducibility of the Durango age standard was 5.9%. Data were screened for discrepancies including evidence of experimental issues such as zero or low He due to empty tubes, poor extraction and/or dissolution issues (zero or low U, Th). These aspects were a particular issue for early analyses. Age outliers associated with inclusions and/or bad neighbours were also rejected by reference to grain images and sample notes. Samples with fission track mounts were also examined for abundances of U zoning. Full details for grain rejection can be found in the data repository. Table 1 details sample locations together with other tables detailing the full AFT and AHe results. Sample thermal history solutions were extracted from paired AFT and single grain AHe data using the QTQt software based on a transdimensional Bayesian inverse algorithm (Gallagher 2012). Model outputs are accepted thermal history models that can be combined to give an expected thermal history model, which is the mean of the accepted paths weighted by the posterior probability of each individual thermal history. This posterior distribution can also be used to define the 95% credible intervals that provide a measure of uncertainty. The only constraints in time-temperature space used in model runs were boxes for zircon FT cooling ages (Storey et al., 1996a), and igneous emplacement ages for samples J6.296.1 and J6.310.1. Sandstone sample DJ.185.1 incorporated a Late Cretaceous maximum depositional age. Data were predicted using the annealing and diffusion models of Ketcham et al.. (2007) and Gautheron et al.. (2009). A typical model run involved 10,000 iteration runs to optimize the Monte Carlo model search parameters before performing longer runs of 20-50,000 "burn in" and 50-100,000 post burn iterations.

Wide dispersion in single-grain dates beyond that which can be accounted for by grain size and radiation damage (Flowers et al., 2009), may be due to a range of factors that includes composition (Djimbi et al., 2015), He implantation, (Spiegel et al., 2009), fluid and mineral inclusions, (Farley, 2002, Vermeesch et al., 2007), as well as experimental issues such as incomplete grain dissolution and outgassing of He. It is hard to identify the underlying cause of overdispersion and current practice, to identify and remove anomalous single grain ages, can appear subjective.