PHYS 211 Learning Objectives

The focus for the first quarter of PHYS 211 is to learn the fundamentals of experimental physics in a step-by-step manner. The course works through a set of broad experiments that are broken down into guided tutorials and short assignments with an emphasis on scaffolded learning and repeated opportunities for practice.

The expected learning outcomes for Autumn Quarter PHYS 21101 are broken into four categories, as follows.

• (1) Students are able to keep a lab notebook which provides a full record of their work on an experiment. Specifically, students can…
  • …use notes, sketches, photos, and other records to preserve the context for their work;
  • …organize and record procedure, data, and observations clearly and completely; and
  • …provide in-notebook calculations, sanity checks, and quick plots, as needed.

• (2) Students are able to decide for themselves how to collect data, and are able to make real-time decisions about next steps based on interpretation of that data. Specifically, students can…
  • …make choices about what data to collect (and how much);
  • …make in-the-moment estimates of uncertainties, and revise or add data collection techniques based on these estimates;
  • …plot data as they go and perform other calculations and sanity checks to determine the quality and consistency of the data they collect; and
  • …use their existing data to determine next steps.

• (3) Students are able to estimate statistical uncertainties in both measured and calculated quantities. Specifically, students can…
  • …identify when it is appropriate to estimate uncertainty based on measurement resolution, repeated measurements, or from intuition/plausibility arguments;
  • …quantify statistical uncertainties using a well-defined method;
  • …make order of magnitude estimates of quantities and uncertainties, and identify when an uncertainty is dominant or negligible;
  • …apply statistics principles (Gaussian, Poissonian, etc), when appropriate; and
  • …propagate uncorrelated and correlated uncertainties in calculated quantities.

• (4) Students are able to identify systematic bias in experiments and apply techniques to correct for or account for bias (qualitatively or quantitatively). Specifically, students can…
  • …postulate plausible sources of bias or uncertainty;
  • …estimate magnitude and direction of systematic bias or uncertainty;
  • …propose (and when possible, carry out) experiments to explore (and quantify) systematic bias or uncertainty; and
  • …discuss and contextualize their findings about bias or uncertainty.

• (5) Students are comfortable working with apparatus commonly encountered in experimental physics, and can understand their inherent biases and limitations. Specifically, students are able to…
  • …operate basic benchtop equipment such as power supplies, waveform generators, and multimeters;
  • …comfortably use oscilloscopes to study time-varying voltage signalsm, which includes using skills such as the following:
    • internal and external triggering;
    • AC vs. DC coupling;
    • built-in cursor and measurement tools;
    • persistence, averaging, and reference tools;
    • FFT and other math operations; and
    • saving digital data;
  • … safely handle lab hazards such as radioactivity, vacuum tubes, and high voltage supplies; and
  • …understand and use particle detector systems (such as NaI+PMT systems) and associated acquisition hardware and software.

• (6) Students are able to process and analyze data in order to extract quantities of interest, test models, or study new behaviors. Specifically, students can…
  • …perform quick manipulations or analyses of data while in the lab in order to assess quality and to make first-pass interpretations, such as the following:
    • linearize data to look for trends or agreement with predictions,
    • calibrate to look for agreement with absolute values,
    • examine limiting cases to look for simplified or extreme behavior, and
    • perform “standard candle” checks to look for agreement with previously-known or verified results;
  • …plot raw or calculated data in linear, log-linear, and log-log forms, and include error bars or other indicators of uncertainties;
  • …use least-squares fitting to extract values from data sets or test models, and compute goodness of fit parameters (e.g. chi-square) and residuals;
  • …interpret fits (both through visual inspection and through statistical measures such as the goodness of fit and residual values), and coherently justify those interpretations when discussing results or presenting conclusions; and
  • …explain the propagation of statistical uncertainties through to final results (whether from raw calculation or through fitting), and discuss how (or if) systematic uncertainties or biases are accounted for.

• (7) Students are able to iterate on an experiment and use the results of their analysis to determine areas for improvement. Specifically, students can…
  • …perform repeated measurements to improve statistical uncertainty (when greater precision is needed);
  • …extend the range or density of data collection to explore behavior in different regimes (when testing needs to be expanded);
  • …revise data collection techniques (when better methods are possible);
  • … explore (and quantify) systematic effects (when bias is detected); and
  • …modify theoretical models (both for measurement devices and for the physics being studied, when necessary).

• (8) Students are able to appropriately present results. Specifically, students can…
  • …report values with appropriate significant figures and units;
  • …format values clearly (in-text or in a table) using correct and consistent variable names, scientific notation, SI unit conventions, etc.
  • …typeset their analyses and reports using LaTeX, and use features such as inline math and equations, separately-numbered equations, numbered and captioned figures and tables, reference lists, and in-text references (to equations, tables, figures, and citations, etc.);
  • …create professional figures (including appropriate axes, labels, text and arrow annotations, text sizing, point and error bar sizing, color usage, significant figures and units on values, white space, etc.);
  • …determine which figures to present, and how to group or consolidate data sets into fewer figures when appropriate;
  • …write appropriate, descriptive, and complete figure and table captions; and
  • …provide the reader appropriate experimental context, including choices made during data collection or processing that affect the results.

• (9) Students are able to appropriately discuss results. Specifically, students can…
  • …organize a discussion in a logical way that makes the strongest case possible, while being factually correct and clear to the reader;
  • …contextualize results vis-a-vis theory or expectations from prior to experiment and in light of observations and findings during/after experiment; and
  • …write professionally and clearly by employing techniques such as the following:
    • writing in full sentences and paragraphs, and splitting text into sections and subsections when appropriate;
    • writing precisely (by avoiding vague/hollow statements, jargon, and acronyms… and by using technical terms correctly instead of to add “fluff” or “formality”); and
    • writing in a narrative style that is easy-to-follow and which uses natural language as much as possible.

• (10) Students are able to draw conclusions that are supported by their results and the correct interpretation of their data. Specifically, students can…
  • …compare their measured or computed values (with uncertainties) to expectations or literature values, and (when appropriate) use statistical measures such as a t-test (i.e. number of sigma) comparison or confidence interval to highlight quantitative agreement;
  • …cite appropriate sources for literature values or can clearly motivate or derive expected results that are based on theory;
  • …discuss the limitations of the experiment and the impact of uncertainties and/or biases on the reliability and repeatability of the results;
  • …support their conclusions with both clear (and well motivated) statistical uncertainties and with plausible (and quantitative) discussion of systematic biases; and
  • …identify short-comings in their own work and suggest avenues for future exploration (in terms of data collection or apparatus, analysis techniques, applicable theory and models, or even the question(s) being asked).