P Tikkanen
Biographic Data
| ID | 5597671 |
|---|---|
| NAME | P Tikkanen |
| GIVEN NAMES | P |
| FAMILY NAME | Tikkanen |
| SIGNATURE | TIKKANEN P |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 10 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2017 |
| H-INDEX | 2 |
Automatic Optimization of AMS with LabView
Accelerator systems designed for a wide variety of ion beam analysis (IBA) applications usually have a multitude of beamline components and long beam lines. In the use of these systems, beam optimization is especially important for attaining high-precision accelerator mass spectrometry (AMS) results. The optimization involves multiple parameters, dependencies between parameters, and multiple optimization targets. To improve the repeatability and …
Using car4ams, the Bayesian AMS Data Analysis Code
The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the G…
Radiocarbon Dating of Iron
The iron dating project Aikarauta has been launched in Finland. This paper presents the results of the preliminary investigations. The ability for radiocarbon measurement by accelerator mass spectrometry (AMS) of iron in Finland has been demonstrated by using coal-produced iron as reference material. An elemental analyzer has been harnessed to measure the carbon content of small iron samples. In addition, we have hypothesized that a fingerprint o…
Pushing the Limits of AMS Radiocarbon Dating with Improved Bayesian Data Analysis
We present an improved version of the continuous autoregressive (CAR) model, a Bayesian data analysis model for accelerator mass spectrometry (AMS). Measurement error is taken to be Poisson-distributed, improving the analysis for samples with only a few counts. This, in turn, enables pushing the limit of radiocarbon measurements to lower concentrations. On the computational side, machine drift is described with a vector of parameters, and hence t…
An Information-Efficient Bayesian Model for AMS Data Analysis
A Bayesian model for accelerator mass spectrometry (AMS) data analysis is presented. Instrumental drift is modeled with a continuous autoregressive (CAR) process, and measurement uncertainties are taken to be Gaussian. All samples have a parameter describing their true value. The model adapts itself to different instrumental parameters based on the data, and yields the most probable true values for the unknown samples. The model is able to use th…
Bayesian Periodic Signal Detection Applied to Intcal98 Data
A Bayesian multiple-frequency model has been developed for spectral analysis of data with unknown correlated noise. A description of the model is given and the method is applied to decadal atmospheric INTCAL98 Δ 14 C data. The noise of the INTCAL98 data is found to be red, and there seems to be no support for continuous harmonic frequencies in the data
Radiocarbon Dating of Iron
The iron dating project Aikarauta has been launched in Finland. This paper presents the results of the preliminary investigations. The ability for radiocarbon measurement by accelerator mass spectrometry (AMS) of iron in Finland has been demonstrated by using coal-produced iron as reference material. An elemental analyzer has been harnessed to measure the carbon content of small iron samples. In addition, we have hypothesized that a fingerprint o…
Using car4ams, the Bayesian AMS Data Analysis Code
The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the G…
Pushing the Limits of AMS Radiocarbon Dating with Improved Bayesian Data Analysis
We present an improved version of the continuous autoregressive (CAR) model, a Bayesian data analysis model for accelerator mass spectrometry (AMS). Measurement error is taken to be Poisson-distributed, improving the analysis for samples with only a few counts. This, in turn, enables pushing the limit of radiocarbon measurements to lower concentrations. On the computational side, machine drift is described with a vector of parameters, and hence t…
An Information-Efficient Bayesian Model for AMS Data Analysis
A Bayesian model for accelerator mass spectrometry (AMS) data analysis is presented. Instrumental drift is modeled with a continuous autoregressive (CAR) process, and measurement uncertainties are taken to be Gaussian. All samples have a parameter describing their true value. The model adapts itself to different instrumental parameters based on the data, and yields the most probable true values for the unknown samples. The model is able to use th…
Bayesian Periodic Signal Detection Applied to Intcal98 Data
A Bayesian multiple-frequency model has been developed for spectral analysis of data with unknown correlated noise. A description of the model is given and the method is applied to decadal atmospheric INTCAL98 Δ 14 C data. The noise of the INTCAL98 data is found to be red, and there seems to be no support for continuous harmonic frequencies in the data
Pushing the Limits of AMS Radiocarbon Dating with Improved Bayesian Data Analysis
We present an improved version of the continuous autoregressive (CAR) model, a Bayesian data analysis model for accelerator mass spectrometry (AMS). Measurement error is taken to be Poisson-distributed, improving the analysis for samples with only a few counts. This, in turn, enables pushing the limit of radiocarbon measurements to lower concentrations. On the computational side, machine drift is described with a vector of parameters, and hence t…
An Information-Efficient Bayesian Model for AMS Data Analysis
A Bayesian model for accelerator mass spectrometry (AMS) data analysis is presented. Instrumental drift is modeled with a continuous autoregressive (CAR) process, and measurement uncertainties are taken to be Gaussian. All samples have a parameter describing their true value. The model adapts itself to different instrumental parameters based on the data, and yields the most probable true values for the unknown samples. The model is able to use th…
Radiocarbon Dating of Iron
The iron dating project Aikarauta has been launched in Finland. This paper presents the results of the preliminary investigations. The ability for radiocarbon measurement by accelerator mass spectrometry (AMS) of iron in Finland has been demonstrated by using coal-produced iron as reference material. An elemental analyzer has been harnessed to measure the carbon content of small iron samples. In addition, we have hypothesized that a fingerprint o…
Using car4ams, the Bayesian AMS Data Analysis Code
The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the G…
Automatic Optimization of AMS with LabView
Accelerator systems designed for a wide variety of ion beam analysis (IBA) applications usually have a multitude of beamline components and long beam lines. In the use of these systems, beam optimization is especially important for attaining high-precision accelerator mass spectrometry (AMS) results. The optimization involves multiple parameters, dependencies between parameters, and multiple optimization targets. To improve the repeatability and …
Computer Science (5 works) · Mathematics (5 works) · Statistics (5 works) · Accelerator mass spectrometry (4 works) · Bayesian probability (4 works) · Artificial Intelligence (3 works) · Autoregressive model (3 works) · Physics (3 works) · Radiocarbon dating (3 works) · Archaeology and ancient environmental studies (2 works)