Long time-series photometry on temperate sites and what to gain from a move to Antarctica ARENA workshop, “Time-series observations from Dome C” Catania T.Granzer & K.Strassmeier, Sep 17th, 2008

Outline: Long-term stellar photometry: Spot modelling Cycle variations Astroseismology Transit searches,… Robotic observations Needs/gains The perfect observation Thermal/Antarctic I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

(Direct) Spot modelling: Strassmeier K. G., et al., 2002 Continuous, covering at least a single rotation Complementary to Doppler-Imaging * I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Activity cycles: * Extremely long time scales, like decades. Constant data quality. * I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk. Olah, et al., 2008

Activity cycles cont‘d Stellar activity cycles like Sun. Bright targets. I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk. Data obtained with 75cm, photoelectric robotic telescope

Transit searches: Continuous observations (unknown parameter space) High precision on many targets. Can be done in white light. * * Winn, Holman & Fuentes, 2006 I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Astroseismology: 29 frequencies found in BI CMi (Breger, et al., 2002) Uninterrupted data sets to resolve entire frequency spectrum. Two colors. Short exposure times. * I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Astroseismology (cont‘d): ‘Whole Earth Telescope’ to beat day/night cycle. Highest duty times with robotic telescopes. All APT observations with a single, robotic telescope! I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Fairborn Observatory 14 robotic telescopes, 0.1-2m Washington Camp, Arizona, 1560m 14 robotic telescopes, 0.1-2m First installation world-wide Mainly Photometry

Twin-telescope STELLA Tenerife / Teide 2400m Altitude 2x 1,2m telescopes WiFSIP: 4kx4k imager SES: high-R Echelle STELLA

STELLA-I Instrumentation Fiber-fed Echelle spectrograph, fixed format, fiber entrance 50µm (2.1"), R42000

STELLA-II Instrumentation 4kx4k CCD, 22’ FoV, whole Strömgren, Sloan & Johnson filter set + H

Task: Feed light into fiber

STELLA-I Acquisition unit Beam-splitter diverts 4% on guider CCD (KAF-0402ME, uncooled). Mirror around fiber entrance. Optic wheel with flat mirror for calibration light, glass pyramid for focus.

Task: Feed light into fiber Fiber entrance At acquire, bring stellar image onto fiber position Hold it there during science exposure Image through beam splitter Image from mirror around fiber Flat field exposure, guider image

Task: Pointing Guider field-of-view ~2.5 arcmin Pointing accuracy STELLA-I currently 15.8 arcsec

Classic pointing model 7-parameter model (alt/az mount), automatically determined in STELLA at predefined intervals: I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk. AN,AE… tilt of az-axis against N,E NPAE… non-perpendicularity of alt to az axis BNP… non-perpendicularity of opt. axis to alt axis TF… tube flexure

Consequences A stable mount is required for good pointing. Temperature drifts in some parameters already on rocky grounds. Drifts of the ice will not be completely plane-parallel and thus introduce drifts in the pointing model with time. Cannot use only the science observations, they introduce bias. I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Task: Acquire Image from beam-splitter Image from mirror around fiber Read-out stripes (shutter-less system) Mirror image shows fiber Acquire on beam-splitter image At acquire, 2-5 images are required. Depending on star brightness, this translates to ~10-40 sec. Beam-splitter causes the images to be elongated in y-direction.

Acquire (cont.) Acquire frames are bias and dark corrected. Stars identified at prob.  0.443 Probability function defined by manual identification of stars on ~100 acquire frames Acquire frames are bias and dark corrected. A truncated gauss is used for star detection (similar DAOfind). Stars are discriminated from cosmics by their elongation and sharpness. Elongation criterion must be weak due to beam-splitter.

Task: Closed-loop guiding Guiding is done on beam-splitter image Magnitude difference on added guider frames allows estimate of light loss 51 Peg, 20 min, ~1200 guider frames, average Here: 32% 30 min @ LQ Hya, Gauss-filtered

Closed-loop guiding (cont.) Each guider frame gives a single offset for the two telescope axis Up to ten single offsets are averaged (target brightness depending). This average offset is fed into a PID-loop The PID output is applied to the telescope at f=1/5 Hz. Problems with high wind gusts. Dependency of optimal PID parameters on seeing and guider dead-time, from a telescope model Currently, three PID parameter set per axis are used, depending on seeing and wind speed.

Task: Focus A focus pyramid in the beam splits the image into four parts. At correct focus, the images have a certain distance. Pyramid is out-of focus, when star is in focus (different optical path). Not a perfect square, but distances highly reproducible. For STELLA-I, Δs=1px for Δf=0.03933mm 5-20sec. for focusing. Measure diagonals or Measure side length.

Task: Scheduling Scheduling currently simple, a few science targets plus RV and flux standards. Each run starts at solz > 0 with bias, followed by flat-fields and ThAr. During night, a ThAr plus an RV standard is taken every 2h.

Approach: Dispatch scheduling: Picks target according to actual conditions. Must run in real-time, but N Allows easy reaction to weather changes. Used on most robotic systems. I will take about principle motivations why we want to do robotic observation, about the principle necessities robotic astronomy faces. After a very short –and incomplete- overview about current projects, I want to focus your attention on the institute’s STELLA project. Here I want to address how we approached robotizing of the instrument. This will be mainly a talk about software, not a real science talk.

Robotic/Remote: STELLA and many other projects show that it works! Robotic: (Almost) no human interference. Low bandwidth sufficient. Unattended observations, autonomous reaction to unforeseen events (bad weather). STELLA and many other projects show that it works!

What can we gain from polar sites: A simple example: Take a 75cm telescope from Arizona to Dome-C.

The perfect observation: No read-out noise, etc. Ignore seeing (2nd order effect in photometry) Remaining error sources: Scintillation, Photon noise, Background noise. Scintillation: ²~sec(Z)³N²T3/2 (Davids et al., 1996) Photon noise: ²~N (Poisson statistics) Background with Moon. Use a perfect comparison star. Take one month around 21st Dec. Take an object that passes the zenith. Observe all night with hsun<-18°.

Model of a perfect time-series: 10 sec.exposures Scintillation noise limited

Periodogram:

Same for Dome C: Use same scintillation law (probably much better!) Zenith-passing object now z<30° Observe at hsol < -12°

51092 vs. 98692 measures:

Periodogram:

Detection probability: The geographic uniqueness alone offers profound advantages over low-latitude sites for time-series observations.