--- title: 'ARTEMIS: Multi-Domain Research Systems' url: https://www.emergentmind.com/topics/artemis type: topic --- # ARTEMIS: Multi-Domain Research Systems ARTEMIS is a recurrent research name rather than a single system. Across arXiv, it denotes several unrelated instruments, backends, security systems, datasets, accelerators, and learning frameworks. The best-known uses include the **Advanced Radio Transient Event Monitor and Identification System** for real-time radio transient searches, the **Automatic and Real-Time dEtection and MItigation System** for BGP hijacking, multiple astronomy instruments such as **ARTEMIS IV** and **ArTéMiS**, and a wide range of machine-learning and systems papers spanning affective vision-language modeling, homomorphic-encryption-aware pruning, zkML commit-and-prove SNARKs, in-DRAM transformer acceleration, autonomous-driving planning, and perception-policy learning [1111.6399] [1609.05702] [2101.07396] [2407.12638] [2409.12055]. ## 1. Names, expansions, and scope The name is used in several technically distinct ways. | Form | Designation | Domain | |---|---|---| | **ARTEMIS** | **Advanced Radio Transient Event Monitor and Identification System** | Radio transient and pulsar backends | | **ARTEMIS IV** | Improved multichannel solar radio spectrograph of the University of Athens | Solar radio astronomy | | **ArTéMiS** | Wide-field submillimeter camera | Submillimeter instrumentation | | **ARTEMIS** | **Automatic and Real-Time dEtection and MItigation System** | BGP hijack detection and mitigation | | **ArtEmis** | **Affective Language for Visual Art** | Vision-language dataset and modeling | These usages are independent. In astronomy alone, the name may refer to a LOFAR transient backend, a solar spectrograph, a submillimeter camera, or the **Artemis-enabled Stellar Imager** concept for the lunar surface; in networking it identifies a BGP defense system; in machine learning and systems it labels datasets, accelerators, pruning methods, program-analysis tools, and policy-learning frameworks [1210.4318] [1009.3628] [1407.2545] [2503.02105]. ## 2. Radio astronomy: real-time transient and pulsar processing In high-time-resolution radio astronomy, ARTEMIS denotes the **Advanced Radio Transient Event Monitor and Identification System**, a combined hardware/software system for real-time processing of high-time-resolution radio astronomy data, built for searches for millisecond radio transients and pulsars with next-generation telescopes such as LOFAR and MeerKAT. The scientific target class includes pulsar single pulses, RRATs, FRBs, and related fast bursts. The central computational task is **incoherent de-dispersion**, implemented in a modular C++ pipeline called **AMPP** inside the **PELICAN** scalable framework developed at the Oxford e-Research Centre [1111.6399]. The pipeline receives high-time-resolution telescope streams, can perform further channelization, forms Stokes parameters, applies RFI mitigation, optionally integrates samples, then searches de-dispersed time series for single pulses or bursts. For filterbank-like total-intensity data \(I(f,t)\), the cold-plasma delay is described by \[ \Delta t \approx 4.15 \times 10^3 \,\mathrm{ms}\; \mathrm{DM}\left(f_1^{-2} - f_2^{-2}\right), \] with frequencies in MHz and \(\mathrm{DM} = \int_0^d n_e(l)\,dl\). ARTEMIS uses incoherent rather than coherent de-dispersion in this work because it operates on detected intensity, is less data intensive, and maps well to GPU parallelism [1111.6399]. The 2011 system paper focuses on two brute-force de-dispersion implementations: a GPU algorithm optimized for NVIDIA Fermi and a CPU implementation optimized for Intel SSE/AVX. The GPU kernel exploits L1 cache reuse, registers for accumulators, and tiling in \((\mathrm{DM}, t)\) space; with \(N_f \approx 2000\) channels and \(N_{\mathrm{DM}} \approx 2000\) trial dispersion measures, it achieves about **40–50% of peak GPU performance**, which the authors identify as sufficient for real-time survey operation. Simulated data tests recovered a square-pulse signal at the injected \(\mathrm{DM} = 50\ \mathrm{pc\ cm^{-3}}\), and the GPU kernel was tested successfully in a real ARTEMIS environment [1111.6399]. The LOFAR deployment extended this concept into a dedicated station backend. ARTEMIS was described there as a non-disruptive add-on to LOFAR international stations, built from **four 12-core servers** connected through **10 Gigabit Ethernet**, ingesting a **3.2 Gbit/s** beamformed stream corresponding to approximately **48 MHz** sky bandwidth, **5 \(\mu\)s** sampling, and **two** polarizations. The pipeline performs additional polyphase-filter channelization, Stokes generation, RFI excision, temporal integration, and real-time brute-force de-dispersion over **typically at least 2000 trial DMs** [1210.4318]. That paper distinguishes two GPU de-dispersion kernels. The **Shared Memory algorithm** has a maximum DM limit of approximately **100** and achieves **~12 Gb/s** throughput on a **Kepler K10 GPU**. The **L1 algorithm** relaxes the DM limit and can process a **3.2 Gb/s** single-station LOFAR stream up to **DM \(\approx 500\)** in real time; with **10 NVIDIA K10 GPUs**, it could process a **127-beam tied-array** with **2000 channels per pencil beam** up to **DM \(\approx 250\)** in real time. Full systems were installed at **Chilbolton** and **Nançay**, with test systems at **Jülich** and **Onsala** [1210.4318]. ## 3. Astronomical instrumentation beyond the transient backend A separate astronomical use is **ARTEMIS IV**, the multichannel solar radio spectrograph operated by the University of Athens at the Thermopylae Satellite Telecommunication Station. In its improved form it covers **20–650 MHz** using two antennas and two receivers in parallel: a swept-frequency analyzer spanning the full band at **10 spectrums/sec** with **630 channels/spectrum**, and an acousto-optical receiver covering **270–450 MHz** at **100 spectrums/sec** with **128 channels/spectrum**. The reported sensitivity is about **3 SFU** in the **20–100 MHz** range and **30 SFU** in the **100–650 MHz** range, and daily operation is fully automated, including GPS timing, antenna pointing, calibration, acquisition, and DVD archiving [1009.3628]. Another instrument is **ArTéMiS**, a wide-field submillimeter camera developed for the **12-m APEX telescope**. It is intended for simultaneous operation at **200 \(\mu\)m**, **350 \(\mu\)m**, and **450 \(\mu\)m** using filled bolometer arrays. The preliminary on-sky paper reports only the **350 \(\mu\)m** focal plane, with an average relative pointing accuracy of **3 arcsec**, a beam estimated at **8.5 arcsec**, a median **NEFD** at **350 \(\mu\)m** of **600 mJy.s\(^{1/2}\)**, best values of **300 mJy.s\(^{1/2}\)**, and a mapping speed already **more than 5 times better** than the previous **350 \(\mu\)m** instrument at APEX [1407.2545]. The name also appears in the **Artemis-enabled Stellar Imager (AeSI)** concept, where “Artemis-enabled” refers to the NASA Artemis lunar program rather than an acronym expansion. AeSI is a proposed lunar long-baseline UV/optical imaging interferometer whose Phase I baseline design uses **15 primary mirrors** arranged in an elliptical array with a **1 km major axis**, with a growth path to **30 mirrors** and larger arrays through staged deployments. The NIAC Phase I report argues that the lunar surface, coupled with Artemis infrastructure for transport, power, communication, and operations, makes a reconfigurable dispersed-aperture interferometer feasible [2503.02105]. ## 4. Internet routing security: BGP hijack detection and mitigation In networking, ARTEMIS expands to **Automatic and Real-Time dEtection and MItigation System** and denotes a self-operated defense against BGP prefix hijacking. Its central premise is that an AS can detect hijacks against **its own prefixes** with very high accuracy because it knows the legitimate origin ASNs and expected routing relationships for those prefixes. The system monitors multiple real-time control-plane sources, including **BGPmon**, **RIPE RIS streaming**, and **Periscope**; the 2016 full paper also discusses support for **BGPstream** [1609.05702]. The basic detection rule is simple: if a monitored prefix is observed with an illegitimate origin AS, ARTEMIS declares a hijack. The later, more detailed work extends this to a taxonomy spanning exact-prefix hijacks, sub-prefix hijacks, BGP squatting, and **Type-0**, **Type-1**, and more general **Type-N** AS-path manipulations, using local knowledge of authorized origins, neighbors, and previously verified AS-links. The system is explicitly designed to be self-managed rather than third-party operated, in order to avoid delayed notification, reduce false positives, and preserve routing-policy privacy [1801.01085]. Mitigation is based primarily on **prefix de-aggregation**. If a hijacked prefix is larger than **/24**, ARTEMIS can announce more specific sub-prefixes from the legitimate AS, for example splitting a hijacked **/23** into two **/24**s. The 2017 demonstration paper, evaluated on the **PEERING** testbed, reports average detection in approximately **45 seconds**, approximately **15 seconds** to issue the de-aggregated announcements, and full mitigation within about **5 minutes**, for a total of about **6 minutes** from hijack launch. It explicitly notes that this method may not work for **/24** prefixes because more specific IPv4 announcements are often filtered [1702.05349]. The broader 2016 and 2018 studies report even faster end-to-end behavior in their own experimental settings. The 2016 paper describes detection within **a few seconds** and mitigation within **minutes** after launch, using extensive real-Internet hijacking experiments [1609.05702]. The 2018 paper argues that ARTEMIS combines comprehensiveness, accuracy, speed, privacy, and flexibility, and reports that prefix hijacking can be **neutralized within a minute** in real-world experiments [1801.01085]. These differing figures reflect different implementations and experimental setups rather than a single universal latency bound. ## 5. Machine learning, accelerators, and static analysis In machine learning and information systems, the name covers a diverse set of unrelated contributions. **ArtEmis** is a large-scale vision-language dataset and modeling framework for affective language about artworks. It contains **439K** emotion attributions and explanations for **81K** artworks from **WikiArt**, supporting tasks such as emotion classification from images or text and affective caption generation. The work treats the image, the elicited emotion, and the free-form explanation as a coupled triad rather than reducing image understanding to object description alone [2101.07396]. In summarization, **Artemis** expands to **Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization**. It is a hierarchical annotation protocol for single-document, extractive, indicative summarization, moving from paragraph-level selection to section-, document-, and short-summary selection. The paper reports analysis on **532 annotated documents**, with five judges per document and average annotation time of **4.17 minutes per judge per document** [2005.02146]. Several ARTEMIS papers focus on secure or efficient computation. In privacy-preserving ML under homomorphic encryption, **Artemis** is an HE-aware pruning method designed to reduce expensive **Rotation** operations in HE convolution. It uses group Lasso regularization aligned with positional and diagonal pruning patterns and reports **1.2–6x** improvements for **ResNet18** and **ResNet50** across three datasets [2310.01664]. In hardware acceleration, **ARTEMIS** is a mixed analog-stochastic in-DRAM accelerator for transformer models; it combines stochastic multiplication with analog accumulation through a metal-on-metal capacitor and reports at least **3.0x speedup**, **1.8x lower energy**, and **1.9x better energy efficiency** compared with CPU, GPU, TPU, and state-of-the-art PIM transformer accelerators [2407.12638]. In zkML, **Artemis** is a commit-and-prove SNARK construction compatible with any homomorphic polynomial commitment; for the **VGG** model it reduces commitment-check overhead from **11.5x** to **1.1x** [2409.12055]. The name also appears in program analysis and autonomous systems. A 2025 security paper presents **Artemis** as a static taint-analysis tool for **SSRF** detection in PHP web applications, combining candidate source/sink extraction, explicit and implicit call graphs, over-tainting controls, and path-condition compatibility analysis. Evaluated on **250** PHP applications, it reports **207 true vulnerable paths**, including **106 true SSRFs**, with **15 false positives**; **35** of the detected SSRFs were newly found and reported, and **24** were confirmed and assigned CVE IDs [2502.21026]. In autonomous driving, **ARTEMIS** is an end-to-end framework that combines **autoregressive trajectory planning** with **Mixture-of-Experts**, factorizing trajectory prediction as \[ p(Y \mid S) = \prod_{t=1}^{H} p(y_t \mid y_{